From eb883fa96721f0664d9ad412a4d2c2bf01b559de Mon Sep 17 00:00:00 2001
From: Dvermetten <d.vermetten@gmail.com>
Date: Tue, 21 Apr 2026 11:56:34 +0200
Subject: [PATCH 1/9] Update UI of the table

---
 docs/index.html       | 164 ++++++++++++++++++++++---
 docs/javascript.html  | 140 +++++++++++++++++++---
 docs/problems.html    |  25 +++-
 docs/table_styles.css | 273 +++++++++++++++++++++++++++++++++++++++---
 yaml_to_html.py       |  52 +++++++-
 5 files changed, 594 insertions(+), 60 deletions(-)

diff --git a/docs/index.html b/docs/index.html
index c544e1f..d39b48e 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -24,7 +24,20 @@ <h2>OPL &ndash; Optimisation problem library</h2>
 <p>
 Submit problems and corrections on <a target="_blank" href="https://github.com/OpenOptimizationOrg/OPL/">GitHub</a> with pull requests / issues, through the <a target="_blank" href="https://docs.google.com/forms/d/e/1FAIpQLSehQp24AuFAH2j9jizDhq8K_BYgNGMKXWTMu6s-2RwEJrK59Q/viewform">Google form</a>, or by email: <a href="mailto:koen.van.der.blom@cwi.nl" itemprop="email">koen.van.der.blom@cwi.nl</a>
 </p>
-<table class="dataframe display compact display styled-table" id="problems">
+
+<section class="table-shell">
+    <div class="table-toolbar">
+        <div class="toolbar-title">Visible columns</div>
+        <div class="toolbar-actions">
+            <button type="button" class="toolbar-btn" id="show-all-columns">Show all</button>
+            <button type="button" class="toolbar-btn" id="hide-all-columns">Hide all</button>
+        </div>
+        <div class="column-controls">
+            <label class="column-chip"><input class="col-toggle" type="checkbox" data-column="0" checked><span>name</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="1"><span>textual description</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="2" checked><span>suite/generator/single</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="3" checked><span>objectives</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="4" checked><span>dimensionality</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="5" checked><span>variable type</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="6" checked><span>constraints</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="7" checked><span>dynamic</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="8" checked><span>noise</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="9" checked><span>multi-fidelity</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="10" checked><span>source (real-world/artificial)</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="11"><span>reference</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="12"><span>implementation</span></label>
+        </div>
+    </div>
+    <div class="table-wrap">
+        <table class="dataframe display compact display styled-table" id="problems">
   <thead>
     <tr style="text-align: right;">
       <th>name</th>
@@ -1215,46 +1228,161 @@ <h2>OPL &ndash; Optimisation problem library</h2>
     </tr>
   </tbody>
 <tfoot><tr><th>name</th> <th>textual description</th> <th>suite/generator/single</th> <th>objectives</th> <th>dimensionality</th> <th>variable type</th> <th>constraints</th> <th>dynamic</th> <th>noise</th> <th>multi-fidelity</th> <th>source (real-world/artificial)</th> <th>reference</th> <th>implementation</th></tr> </tfoot></table>
+    </div>
+</section>
+
+<section class="details-shell" id="problem-details" aria-live="polite">
+    <h3 class="details-title">Problem details</h3>
+    <p class="details-hint" id="problem-details-hint">Click a table row to inspect full details.</p>
+    <dl class="details-grid" id="problem-details-content"></dl>
+</section>
+
 
 <script>
-new DataTable('#problems', {
+const table = new DataTable('#problems', {
     layout: {
         topStart: 'search',
         topEnd: null,
         bottomStart: 'info',
         bottom: 'paging',
         bottomEnd: 'pageLength'
-        },
-    lengthMenu: [15, 25, 50, -1],
+    },
+    lengthMenu: [10, 15, 25, 50, -1],
     initComplete: function () {
         this.api()
             .columns()
             .every(function () {
-                let column = this;
-                let title = column.footer().textContent;
- 
-                // Create input element
-                let input = document.createElement('input');
-                input.placeholder = title;
+                const column = this;
+                const title = column.footer().textContent;
+                const input = document.createElement('input');
+                input.placeholder = `Filter ${title}`;
                 column.footer().replaceChildren(input);
- 
-                // Event listener for user input
+
                 input.addEventListener('keyup', () => {
-                    if (column.search() !== this.value) {
+                    if (column.search() !== input.value) {
                         column.search(input.value).draw();
                     }
                 });
 
-                // Stop search boxes from applying sorting
-                input.addEventListener('click', (e) => {
-                    e.stopPropagation();
+                // Prevent sorting when interacting with filter inputs.
+                input.addEventListener('click', (event) => {
+                    event.stopPropagation();
                 });
             });
     }
 });
 
-// Move row with search boxes to second row
-$('#problems tfoot tr').appendTo('#problems thead');
+const footerRow = document.querySelector('#problems tfoot tr');
+const thead = document.querySelector('#problems thead');
+if (footerRow && thead) {
+    thead.appendChild(footerRow);
+}
+
+const detailsHint = document.getElementById('problem-details-hint');
+const detailsContent = document.getElementById('problem-details-content');
+
+const stripHtml = (value) => {
+    const wrapper = document.createElement('div');
+    wrapper.innerHTML = value || '';
+    return wrapper.textContent || wrapper.innerText || '';
+};
+
+const escapeHtml = (value) => {
+    const wrapper = document.createElement('div');
+    wrapper.textContent = value || '';
+    return wrapper.innerHTML;
+};
+
+const renderRowDetails = (rowApi) => {
+    const rowData = rowApi.data();
+    if (!rowData || !detailsContent) {
+        return;
+    }
+
+    const headers = table
+        .columns()
+        .header()
+        .toArray()
+        .map((header) => header.textContent.trim());
+
+    const detailsHtml = headers
+        .map((label, index) => {
+            const value = rowData[index] || '<span class="details-empty">n/a</span>';
+            return `<div class="detail-item"><dt>${escapeHtml(label)}</dt><dd>${value}</dd></div>`;
+        })
+        .join('');
+
+    const name = stripHtml(rowData[0]);
+    if (detailsHint) {
+        detailsHint.textContent = name ? `Selected: ${name}` : 'Selected row';
+    }
+
+    detailsContent.innerHTML = detailsHtml;
+};
+
+const applyColumnVisibility = (redraw = true) => {
+    document.querySelectorAll('.col-toggle').forEach((toggle) => {
+        const index = Number(toggle.getAttribute('data-column'));
+        const chip = toggle.closest('.column-chip');
+
+        if (chip) {
+            chip.classList.toggle('is-off', !toggle.checked);
+        }
+
+        table.column(index).visible(toggle.checked, false);
+    });
+
+    table.columns.adjust().draw(redraw);
+};
+
+document.querySelectorAll('.col-toggle').forEach((toggle) => {
+    toggle.addEventListener('change', () => {
+        applyColumnVisibility(false);
+    });
+});
+
+const setAllColumns = (visible) => {
+    document.querySelectorAll('.col-toggle').forEach((toggle) => {
+        toggle.checked = visible;
+    });
+
+    applyColumnVisibility(false);
+};
+
+const showAllButton = document.getElementById('show-all-columns');
+if (showAllButton) {
+    showAllButton.addEventListener('click', () => setAllColumns(true));
+}
+
+const hideAllButton = document.getElementById('hide-all-columns');
+if (hideAllButton) {
+    hideAllButton.addEventListener('click', () => setAllColumns(false));
+}
+
+const tableBody = document.querySelector('#problems tbody');
+if (tableBody) {
+    tableBody.addEventListener('click', (event) => {
+        const targetRow = event.target.closest('tr');
+        if (!targetRow) {
+            return;
+        }
+
+        const rowApi = table.row(targetRow);
+        if (!rowApi || !rowApi.any()) {
+            return;
+        }
+
+        document.querySelectorAll('#problems tbody tr.is-active').forEach((row) => {
+            row.classList.remove('is-active');
+        });
+
+        targetRow.classList.add('is-active');
+        renderRowDetails(rowApi);
+    });
+}
+
+// Honor default checkbox states (some columns start hidden).
+applyColumnVisibility(false);
 
 </script>
 
diff --git a/docs/javascript.html b/docs/javascript.html
index a194ca7..73f9e58 100644
--- a/docs/javascript.html
+++ b/docs/javascript.html
@@ -1,44 +1,150 @@
 
 
 <script>
-new DataTable('#problems', {
+const table = new DataTable('#problems', {
     layout: {
         topStart: 'search',
         topEnd: null,
         bottomStart: 'info',
         bottom: 'paging',
         bottomEnd: 'pageLength'
-        },
-    lengthMenu: [15, 25, 50, -1],
+    },
+    lengthMenu: [10, 15, 25, 50, -1],
     initComplete: function () {
         this.api()
             .columns()
             .every(function () {
-                let column = this;
-                let title = column.footer().textContent;
- 
-                // Create input element
-                let input = document.createElement('input');
-                input.placeholder = title;
+                const column = this;
+                const title = column.footer().textContent;
+                const input = document.createElement('input');
+                input.placeholder = `Filter ${title}`;
                 column.footer().replaceChildren(input);
- 
-                // Event listener for user input
+
                 input.addEventListener('keyup', () => {
-                    if (column.search() !== this.value) {
+                    if (column.search() !== input.value) {
                         column.search(input.value).draw();
                     }
                 });
 
-                // Stop search boxes from applying sorting
-                input.addEventListener('click', (e) => {
-                    e.stopPropagation();
+                // Prevent sorting when interacting with filter inputs.
+                input.addEventListener('click', (event) => {
+                    event.stopPropagation();
                 });
             });
     }
 });
 
-// Move row with search boxes to second row
-$('#problems tfoot tr').appendTo('#problems thead');
+const footerRow = document.querySelector('#problems tfoot tr');
+const thead = document.querySelector('#problems thead');
+if (footerRow && thead) {
+    thead.appendChild(footerRow);
+}
+
+const detailsHint = document.getElementById('problem-details-hint');
+const detailsContent = document.getElementById('problem-details-content');
+
+const stripHtml = (value) => {
+    const wrapper = document.createElement('div');
+    wrapper.innerHTML = value || '';
+    return wrapper.textContent || wrapper.innerText || '';
+};
+
+const escapeHtml = (value) => {
+    const wrapper = document.createElement('div');
+    wrapper.textContent = value || '';
+    return wrapper.innerHTML;
+};
+
+const renderRowDetails = (rowApi) => {
+    const rowData = rowApi.data();
+    if (!rowData || !detailsContent) {
+        return;
+    }
+
+    const headers = table
+        .columns()
+        .header()
+        .toArray()
+        .map((header) => header.textContent.trim());
+
+    const detailsHtml = headers
+        .map((label, index) => {
+            const value = rowData[index] || '<span class="details-empty">n/a</span>';
+            return `<div class="detail-item"><dt>${escapeHtml(label)}</dt><dd>${value}</dd></div>`;
+        })
+        .join('');
+
+    const name = stripHtml(rowData[0]);
+    if (detailsHint) {
+        detailsHint.textContent = name ? `Selected: ${name}` : 'Selected row';
+    }
+
+    detailsContent.innerHTML = detailsHtml;
+};
+
+const applyColumnVisibility = (redraw = true) => {
+    document.querySelectorAll('.col-toggle').forEach((toggle) => {
+        const index = Number(toggle.getAttribute('data-column'));
+        const chip = toggle.closest('.column-chip');
+
+        if (chip) {
+            chip.classList.toggle('is-off', !toggle.checked);
+        }
+
+        table.column(index).visible(toggle.checked, false);
+    });
+
+    table.columns.adjust().draw(redraw);
+};
+
+document.querySelectorAll('.col-toggle').forEach((toggle) => {
+    toggle.addEventListener('change', () => {
+        applyColumnVisibility(false);
+    });
+});
+
+const setAllColumns = (visible) => {
+    document.querySelectorAll('.col-toggle').forEach((toggle) => {
+        toggle.checked = visible;
+    });
+
+    applyColumnVisibility(false);
+};
+
+const showAllButton = document.getElementById('show-all-columns');
+if (showAllButton) {
+    showAllButton.addEventListener('click', () => setAllColumns(true));
+}
+
+const hideAllButton = document.getElementById('hide-all-columns');
+if (hideAllButton) {
+    hideAllButton.addEventListener('click', () => setAllColumns(false));
+}
+
+const tableBody = document.querySelector('#problems tbody');
+if (tableBody) {
+    tableBody.addEventListener('click', (event) => {
+        const targetRow = event.target.closest('tr');
+        if (!targetRow) {
+            return;
+        }
+
+        const rowApi = table.row(targetRow);
+        if (!rowApi || !rowApi.any()) {
+            return;
+        }
+
+        document.querySelectorAll('#problems tbody tr.is-active').forEach((row) => {
+            row.classList.remove('is-active');
+        });
+
+        targetRow.classList.add('is-active');
+        renderRowDetails(rowApi);
+    });
+}
+
+// Honor default checkbox states (some columns start hidden).
+applyColumnVisibility(false);
 
 </script>
 
diff --git a/docs/problems.html b/docs/problems.html
index 4c2be02..768796d 100644
--- a/docs/problems.html
+++ b/docs/problems.html
@@ -1,4 +1,17 @@
-<table class="dataframe display compact display styled-table" id="problems">
+
+<section class="table-shell">
+    <div class="table-toolbar">
+        <div class="toolbar-title">Visible columns</div>
+        <div class="toolbar-actions">
+            <button type="button" class="toolbar-btn" id="show-all-columns">Show all</button>
+            <button type="button" class="toolbar-btn" id="hide-all-columns">Hide all</button>
+        </div>
+        <div class="column-controls">
+            <label class="column-chip"><input class="col-toggle" type="checkbox" data-column="0" checked><span>name</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="1"><span>textual description</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="2" checked><span>suite/generator/single</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="3" checked><span>objectives</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="4" checked><span>dimensionality</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="5" checked><span>variable type</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="6" checked><span>constraints</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="7" checked><span>dynamic</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="8" checked><span>noise</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="9" checked><span>multi-fidelity</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="10" checked><span>source (real-world/artificial)</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="11"><span>reference</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="12"><span>implementation</span></label>
+        </div>
+    </div>
+    <div class="table-wrap">
+        <table class="dataframe display compact display styled-table" id="problems">
   <thead>
     <tr style="text-align: right;">
       <th>name</th>
@@ -1188,4 +1201,12 @@
       <td><a href="https://gitlab.com/verel/pubo-importance-benchmark">https://gitlab.com/verel/pubo-importance-benchmark</a></td>
     </tr>
   </tbody>
-<tfoot><tr><th>name</th> <th>textual description</th> <th>suite/generator/single</th> <th>objectives</th> <th>dimensionality</th> <th>variable type</th> <th>constraints</th> <th>dynamic</th> <th>noise</th> <th>multi-fidelity</th> <th>source (real-world/artificial)</th> <th>reference</th> <th>implementation</th></tr> </tfoot></table>
\ No newline at end of file
+<tfoot><tr><th>name</th> <th>textual description</th> <th>suite/generator/single</th> <th>objectives</th> <th>dimensionality</th> <th>variable type</th> <th>constraints</th> <th>dynamic</th> <th>noise</th> <th>multi-fidelity</th> <th>source (real-world/artificial)</th> <th>reference</th> <th>implementation</th></tr> </tfoot></table>
+    </div>
+</section>
+
+<section class="details-shell" id="problem-details" aria-live="polite">
+    <h3 class="details-title">Problem details</h3>
+    <p class="details-hint" id="problem-details-hint">Click a table row to inspect full details.</p>
+    <dl class="details-grid" id="problem-details-content"></dl>
+</section>
diff --git a/docs/table_styles.css b/docs/table_styles.css
index 05418c8..439f38c 100644
--- a/docs/table_styles.css
+++ b/docs/table_styles.css
@@ -1,35 +1,274 @@
-/* Apply to everything */
+:root {
+    --opl-bg: #f5f7fb;
+    --opl-surface: #ffffff;
+    --opl-text: #12263a;
+    --opl-muted: #607389;
+    --opl-border: #d7e1ec;
+    --opl-accent: #0f6c8b;
+    --opl-accent-soft: #eaf4fa;
+    --opl-heading: "Segoe UI", "Trebuchet MS", sans-serif;
+    --opl-body: "Tahoma", "Verdana", sans-serif;
+}
+
 * {
-    font-family: sans-serif;
+    box-sizing: border-box;
 }
 
 body {
     margin: 1vw;
-    background-color: WhiteSmoke;
+    font-family: var(--opl-body);
+    color: var(--opl-text);
+    background:
+        radial-gradient(circle at 4% 2%, #e6edf7 0, #e6edf700 34%),
+        radial-gradient(circle at 92% 4%, #ecf3df 0, #ecf3df00 38%),
+        var(--opl-bg);
+}
+
+h2 {
+    font-family: var(--opl-heading);
+    letter-spacing: 0.01em;
+}
+
+.table-shell {
+    margin-top: 1rem;
+    border: 1px solid var(--opl-border);
+    border-radius: 16px;
+    background: var(--opl-surface);
+    box-shadow: 0 14px 30px rgba(17, 37, 61, 0.08);
+    overflow: hidden;
+}
+
+.table-toolbar {
+    display: grid;
+    gap: 0.65rem;
+    padding: 0.9rem 1rem;
+    border-bottom: 1px solid var(--opl-border);
+    background: linear-gradient(90deg, #f7fafc, #edf6fb);
+}
+
+.toolbar-title {
+    font-size: 0.78rem;
+    text-transform: uppercase;
+    letter-spacing: 0.08em;
+    font-weight: 700;
+    color: var(--opl-muted);
+}
+
+.toolbar-actions {
+    display: flex;
+    flex-wrap: wrap;
+    gap: 0.45rem;
+}
+
+.toolbar-btn {
+    border: 1px solid #b8c8df;
+    background: #fff;
+    color: #1b3249;
+    border-radius: 8px;
+    padding: 0.36rem 0.62rem;
+    font-size: 0.8rem;
+    font-weight: 600;
+    cursor: pointer;
+    transition: all 120ms ease;
+}
+
+.toolbar-btn:hover {
+    background: #eff6fb;
+    border-color: #94adc9;
+}
+
+.toolbar-btn:active {
+    transform: translateY(1px);
+}
+
+.column-controls {
+    display: flex;
+    flex-wrap: wrap;
+    gap: 0.45rem;
+}
+
+.column-chip {
+    display: inline-flex;
+    align-items: center;
+    gap: 0.4rem;
+    padding: 0.35rem 0.65rem;
+    border-radius: 999px;
+    border: 1px solid var(--opl-border);
+    background: #fff;
+    font-size: 0.82rem;
+    cursor: pointer;
+    user-select: none;
+    transition: all 120ms ease;
+}
+
+.column-chip:hover {
+    border-color: #b8c8df;
+    background: #f7fbfd;
+}
+
+.column-chip.is-off {
+    color: var(--opl-muted);
+    background: #f1f5f9;
+}
+
+.column-chip input {
+    accent-color: var(--opl-accent);
+}
+
+.table-wrap {
+    overflow-x: auto;
+    padding: 0.45rem;
+}
+
+#problems {
+    width: 100%;
+    min-width: 980px;
+    border-collapse: separate;
+    border-spacing: 0;
+}
+
+#problems thead th {
+    background: #f9fcff;
+    color: #1d324a;
+    border-bottom: 1px solid var(--opl-border);
+    font-weight: 700;
+    white-space: nowrap;
+}
+
+#problems td {
+    vertical-align: top;
+    line-height: 1.4;
+}
+
+#problems th:first-child {
+    position: sticky;
+    left: 0;
+    z-index: 5;
+    background: #f9fcff;
+    box-shadow: 1px 0 0 0 var(--opl-border);
+}
+
+#problems td:first-child {
+    position: sticky;
+    left: 0;
+    z-index: 2;
+    background: #fcfeff;
+    box-shadow: 1px 0 0 0 var(--opl-border);
+}
+
+#problems tbody tr:nth-of-type(odd) td {
+    background: #fcfeff;
+}
+
+#problems tbody tr:nth-of-type(even) td {
+    background: #f8fbfe;
+}
+
+#problems tbody tr:hover td {
+    background: var(--opl-accent-soft);
+}
+
+#problems tbody tr:hover td:first-child {
+    background: #deedf6;
+}
+
+#problems tbody tr.is-active td {
+    background: #dceefa;
 }
 
-input[type=text] {
-  background-color: #3CBC8D;
-  color: white;
+#problems tbody tr.is-active td:first-child {
+    background: #cce5f5;
 }
 
-.styled-table {
-    font-size: 0.9em;
-    box-shadow: 0 0 20px rgba(0, 0, 0, 0.15);
+#problems a {
+    color: #085f75;
 }
 
-.styled-table thead tr {
-    background-color: AntiqueWhite;
+#problems thead input {
+    width: 100%;
+    padding: 0.35rem 0.45rem;
+    border-radius: 6px;
+    border: 1px solid var(--opl-border);
+    background: #fff;
+    color: var(--opl-text);
 }
 
-.styled-table tbody tr:nth-of-type(odd) {
-    background-color: SeaShell;
+.details-shell {
+    margin-top: 0.9rem;
+    padding: 0.9rem 1rem;
+    border: 1px solid var(--opl-border);
+    border-radius: 14px;
+    background: var(--opl-surface);
+    box-shadow: 0 10px 24px rgba(17, 37, 61, 0.06);
 }
 
-.styled-table tbody tr:nth-of-type(even) {
-    background-color: Snow;
+.details-title {
+    margin: 0 0 0.3rem;
+    font-family: var(--opl-heading);
+    font-size: 1.02rem;
 }
 
-.styled-table tbody tr:hover {
-    filter: brightness(.975);
+.details-hint {
+    margin: 0 0 0.75rem;
+    color: var(--opl-muted);
+    font-size: 0.88rem;
+}
+
+.details-grid {
+    margin: 0;
+    display: grid;
+    grid-template-columns: repeat(auto-fit, minmax(220px, 1fr));
+    gap: 0.6rem;
+}
+
+.detail-item {
+    margin: 0;
+    border: 1px solid var(--opl-border);
+    border-radius: 10px;
+    padding: 0.55rem 0.65rem;
+    background: #fbfdff;
+}
+
+.detail-item dt {
+    margin: 0;
+    color: #23415e;
+    font-size: 0.74rem;
+    text-transform: uppercase;
+    letter-spacing: 0.04em;
+    font-weight: 700;
+}
+
+.detail-item dd {
+    margin: 0.35rem 0 0;
+    color: var(--opl-text);
+    font-size: 0.86rem;
+    line-height: 1.4;
+    overflow-wrap: anywhere;
+}
+
+.details-empty {
+    color: var(--opl-muted);
+    font-style: italic;
+}
+
+@media (max-width: 900px) {
+    body {
+        margin: 0.6rem;
+    }
+
+    .table-toolbar {
+        padding: 0.8rem;
+    }
+
+    .column-chip {
+        font-size: 0.76rem;
+    }
+
+    .details-shell {
+        padding: 0.8rem;
+    }
+
+    .details-grid {
+        grid-template-columns: 1fr;
+    }
 }
diff --git a/yaml_to_html.py b/yaml_to_html.py
index a9a219f..1bef2dd 100755
--- a/yaml_to_html.py
+++ b/yaml_to_html.py
@@ -3,6 +3,7 @@
 import pandas as pd
 import yaml
 import shutil
+from html import escape
 
 import re
 
@@ -30,8 +31,8 @@ def repl(m):
 html_index = f"{html_dir}index.html"
 
 # Load data
-with open(yaml_file) as in_file:
-    data = pd.json_normalize(yaml.safe_load(in_file))
+with open(yaml_file) as yaml_input:
+    data = pd.json_normalize(yaml.safe_load(yaml_input))
 
 # Choose desired columns
 all_columns = False
@@ -68,12 +69,51 @@ def repl(m):
 idx = table.index('</table>')
 final_table = table[:idx] + "<tfoot><tr>" + " ".join(["<th>"+ i +"</th>" for i in data.columns])+"</tr> </tfoot>" + table[idx:]
 
+default_hidden_columns = {"textual description", "reference", "implementation"}
+
+column_toggles = "".join(
+    [
+        (
+            f'<label class="column-chip">'
+            f'<input class="col-toggle" type="checkbox" data-column="{i}"'
+            f'{" checked" if col not in default_hidden_columns else ""}>'
+            f'<span>{escape(col)}</span>'
+            f'</label>'
+        )
+        for i, col in enumerate(data.columns)
+    ]
+)
+
+modern_table_block = f"""
+<section class="table-shell">
+    <div class="table-toolbar">
+        <div class="toolbar-title">Visible columns</div>
+        <div class="toolbar-actions">
+            <button type="button" class="toolbar-btn" id="show-all-columns">Show all</button>
+            <button type="button" class="toolbar-btn" id="hide-all-columns">Hide all</button>
+        </div>
+        <div class="column-controls">
+            {column_toggles}
+        </div>
+    </div>
+    <div class="table-wrap">
+        {final_table}
+    </div>
+</section>
+
+<section class="details-shell" id="problem-details" aria-live="polite">
+    <h3 class="details-title">Problem details</h3>
+    <p class="details-hint" id="problem-details-hint">Click a table row to inspect full details.</p>
+    <dl class="details-grid" id="problem-details-content"></dl>
+</section>
+"""
+
 # Write table to file
 with open(html_table, "w") as table_file:
-    table_file.write(final_table)
+    table_file.write(modern_table_block)
 
 # Merge table and scripts into HTML page
 with open(html_index, "wb") as output_file:
-    for in_file in [html_header, html_table, html_scripts, html_footer]:
-        with open(in_file, "rb") as in_file:
-            shutil.copyfileobj(in_file, output_file)
+    for part_path in [html_header, html_table, html_scripts, html_footer]:
+        with open(part_path, "rb") as part_file:
+            shutil.copyfileobj(part_file, output_file)

From 5d1c6b9910a103c86c98c191c89a51de62313de9 Mon Sep 17 00:00:00 2001
From: Dvermetten <d.vermetten@gmail.com>
Date: Tue, 21 Apr 2026 12:30:15 +0200
Subject: [PATCH 2/9] Remove gradient because Koen doesn't like it

---
 docs/table_styles.css | 2 --
 1 file changed, 2 deletions(-)

diff --git a/docs/table_styles.css b/docs/table_styles.css
index 439f38c..e352e97 100644
--- a/docs/table_styles.css
+++ b/docs/table_styles.css
@@ -19,8 +19,6 @@ body {
     font-family: var(--opl-body);
     color: var(--opl-text);
     background:
-        radial-gradient(circle at 4% 2%, #e6edf7 0, #e6edf700 34%),
-        radial-gradient(circle at 92% 4%, #ecf3df 0, #ecf3df00 38%),
         var(--opl-bg);
 }
 

From ec7576dcec93d21a3b04fc7f4082532479e306c2 Mon Sep 17 00:00:00 2001
From: Dvermetten <d.vermetten@gmail.com>
Date: Tue, 21 Apr 2026 14:01:54 +0200
Subject: [PATCH 3/9] Further separation of html from conversion script

---
 docs/index.html          |  1 -
 docs/problems.html       |  1 -
 docs/table_styles.css    |  8 ++++++++
 docs/table_template.html | 21 +++++++++++++++++++++
 yaml_to_html.py          | 36 +++++++++++-------------------------
 5 files changed, 40 insertions(+), 27 deletions(-)
 create mode 100644 docs/table_template.html

diff --git a/docs/index.html b/docs/index.html
index d39b48e..26a801d 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -24,7 +24,6 @@ <h2>OPL &ndash; Optimisation problem library</h2>
 <p>
 Submit problems and corrections on <a target="_blank" href="https://github.com/OpenOptimizationOrg/OPL/">GitHub</a> with pull requests / issues, through the <a target="_blank" href="https://docs.google.com/forms/d/e/1FAIpQLSehQp24AuFAH2j9jizDhq8K_BYgNGMKXWTMu6s-2RwEJrK59Q/viewform">Google form</a>, or by email: <a href="mailto:koen.van.der.blom@cwi.nl" itemprop="email">koen.van.der.blom@cwi.nl</a>
 </p>
-
 <section class="table-shell">
     <div class="table-toolbar">
         <div class="toolbar-title">Visible columns</div>
diff --git a/docs/problems.html b/docs/problems.html
index 768796d..d3040ea 100644
--- a/docs/problems.html
+++ b/docs/problems.html
@@ -1,4 +1,3 @@
-
 <section class="table-shell">
     <div class="table-toolbar">
         <div class="toolbar-title">Visible columns</div>
diff --git a/docs/table_styles.css b/docs/table_styles.css
index e352e97..1c53f6d 100644
--- a/docs/table_styles.css
+++ b/docs/table_styles.css
@@ -8,6 +8,7 @@
     --opl-accent-soft: #eaf4fa;
     --opl-heading: "Segoe UI", "Trebuchet MS", sans-serif;
     --opl-body: "Tahoma", "Verdana", sans-serif;
+    --opl-table-font-size: 0.86rem;
 }
 
 * {
@@ -123,6 +124,7 @@ h2 {
     min-width: 980px;
     border-collapse: separate;
     border-spacing: 0;
+    font-size: var(--opl-table-font-size);
 }
 
 #problems thead th {
@@ -136,6 +138,11 @@ h2 {
 #problems td {
     vertical-align: top;
     line-height: 1.4;
+    font-size: inherit;
+}
+
+#problems td * {
+    font-size: inherit;
 }
 
 #problems th:first-child {
@@ -189,6 +196,7 @@ h2 {
     border: 1px solid var(--opl-border);
     background: #fff;
     color: var(--opl-text);
+    font-size: inherit;
 }
 
 .details-shell {
diff --git a/docs/table_template.html b/docs/table_template.html
new file mode 100644
index 0000000..270d61f
--- /dev/null
+++ b/docs/table_template.html
@@ -0,0 +1,21 @@
+<section class="table-shell">
+    <div class="table-toolbar">
+        <div class="toolbar-title">Visible columns</div>
+        <div class="toolbar-actions">
+            <button type="button" class="toolbar-btn" id="show-all-columns">Show all</button>
+            <button type="button" class="toolbar-btn" id="hide-all-columns">Hide all</button>
+        </div>
+        <div class="column-controls">
+            __COLUMN_TOGGLES__
+        </div>
+    </div>
+    <div class="table-wrap">
+        __TABLE__
+    </div>
+</section>
+
+<section class="details-shell" id="problem-details" aria-live="polite">
+    <h3 class="details-title">Problem details</h3>
+    <p class="details-hint" id="problem-details-hint">Click a table row to inspect full details.</p>
+    <dl class="details-grid" id="problem-details-content"></dl>
+</section>
diff --git a/yaml_to_html.py b/yaml_to_html.py
index 1bef2dd..87e8cce 100755
--- a/yaml_to_html.py
+++ b/yaml_to_html.py
@@ -29,6 +29,7 @@ def repl(m):
 html_scripts = f"{html_dir}javascript.html"
 html_footer = f"{html_dir}footer.html"
 html_index = f"{html_dir}index.html"
+html_table_template = f"{html_dir}table_template.html"
 
 # Load data
 with open(yaml_file) as yaml_input:
@@ -84,33 +85,18 @@ def repl(m):
     ]
 )
 
-modern_table_block = f"""
-<section class="table-shell">
-    <div class="table-toolbar">
-        <div class="toolbar-title">Visible columns</div>
-        <div class="toolbar-actions">
-            <button type="button" class="toolbar-btn" id="show-all-columns">Show all</button>
-            <button type="button" class="toolbar-btn" id="hide-all-columns">Hide all</button>
-        </div>
-        <div class="column-controls">
-            {column_toggles}
-        </div>
-    </div>
-    <div class="table-wrap">
-        {final_table}
-    </div>
-</section>
-
-<section class="details-shell" id="problem-details" aria-live="polite">
-    <h3 class="details-title">Problem details</h3>
-    <p class="details-hint" id="problem-details-hint">Click a table row to inspect full details.</p>
-    <dl class="details-grid" id="problem-details-content"></dl>
-</section>
-"""
+with open(html_table_template, encoding="utf-8") as template_file:
+    table_template = template_file.read()
+
+table_markup = (
+    table_template
+    .replace("__COLUMN_TOGGLES__", column_toggles)
+    .replace("__TABLE__", final_table)
+)
 
 # Write table to file
-with open(html_table, "w") as table_file:
-    table_file.write(modern_table_block)
+with open(html_table, "w", encoding="utf-8") as table_file:
+    table_file.write(table_markup)
 
 # Merge table and scripts into HTML page
 with open(html_index, "wb") as output_file:

From c9d37c80560f5d2a39ec74c41553f377f74387b2 Mon Sep 17 00:00:00 2001
From: Vanessa <3634850+CIGbalance@users.noreply.github.com>
Date: Tue, 21 Apr 2026 17:06:54 +0200
Subject: [PATCH 4/9] adding merge script (#138)

* adding merge script (untested)

* making tests not fail

* Apply suggestions from code review

Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>

* Change example problem format to YAML

Updated example problem format from JSON to YAML.

* requested changes

* follow new workflow

* tested script now

* remove from PR

* remove unnecessary changes here

* undoing changes

* removing additional changes

* undoing changes

* remove file again

* finishing merge

* Apply suggestions from code review

Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com>

* new_data None

* imports

* removing no changes

* add typing

* change to manual mode

* updated for new workflow

* Apply suggestions from code review

Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com>

* some additional review updates

* check format fixes

* do not write if failing

---------

Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
---
 utils/README.md        | 26 ++++++++----
 utils/merge_yaml.py    | 96 ++++++++++++++++++++++++++++++++++++++++++
 utils/validate_yaml.py | 41 ++++++++++++------
 3 files changed, 142 insertions(+), 21 deletions(-)
 create mode 100644 utils/merge_yaml.py

diff --git a/utils/README.md b/utils/README.md
index 2ad9e5e..263edda 100644
--- a/utils/README.md
+++ b/utils/README.md
@@ -1,19 +1,18 @@
 # OPL YAML utils
 
-This folder contains utility scripts for working with the YAML format to describe problems in context of OPL. They are mainly intended to be run automatically via GitHub Actions to make collaboration easier.
+This folder contains utility scripts for working with the YAML format to describe problems in context of OPL. Some of them are mainly intended to be run automatically via GitHub Actions to make collaboration easier, others are utility functions for maintainers.
 
 The intended way of adding a new problem to the repository is thus as follows:
 
-* Create a file in 'utils/new_problem.yaml' based on the template (see below).
+* Create a new yaml file based on the template (see below).
+* Run the [merge script](merge_yaml.py) locally to update the [problems.yaml](../problems.yaml) file and check that the formatting is correct.
 * Create a PR with the changes (for example with a fork).
 
 What happens in the background then is:
 
-* On PR creation and commits to the PR, the [validate_yaml.py](validate_yaml.py) script is run to check that the YAML file is valid and consistent. It is expecting the changes to be in the [new_problem.yaml](new_problem.yaml) file.
+* On PR creation and commits to the PR, the [validate_yaml.py](validate_yaml.py) script is run to check that the [problems.yaml](../problems.yaml) file is still valid and consistent.
 * Then the PR should be reviewed manually.
-* When the PR is merged into the main branch, a second script runs (which doesn't exist yet), that adds the content of [new_problem.yaml](new_problem.yaml) to the [problems.yaml](../problems.yaml) file, and reverts the changes to the new_problem.yaml.
-
-:warning: Note that the GitHubActions do not exist yet either, this is a WIP.
+* When the PR is merged into the main branch with changes to problems.yaml, the checks are run again.
 
 ## validate_yaml.py
 
@@ -21,13 +20,24 @@ This script checks the new content for the following:
 
 * The YAML syntax is valid and is in expected format
 * The required fields are present.
-* Specific fields are unique across the new set of problems (e.g. name)
+* Specific fields are unique across the set of problems (e.g. name)
+
+:warning: Execute from root of the repository. Tested with python 3.12
+
+```bash
+pip install -r utils/requirements.txt
+python utils/validate_yaml.py problems.yaml
+```
+
+## merge_yaml.py
+
+This script merges a new problem description in a separate yaml file into the main [problems.yaml](../problems.yaml) file. It runs the validation checks from the above script before merging and deletes the separate yaml file after merging.
 
 :warning: Execute from root of the repository. Tested with python 3.12
 
 ```bash
 pip install -r utils/requirements.txt
-python utils/validate_yaml.py utils/new_problem.yaml
+python utils/merge_yaml.py new_problem.yaml problems.yaml
 ```
 
 ## new problem example
diff --git a/utils/merge_yaml.py b/utils/merge_yaml.py
new file mode 100644
index 0000000..6705197
--- /dev/null
+++ b/utils/merge_yaml.py
@@ -0,0 +1,96 @@
+import yaml
+import sys
+from pathlib import Path
+from typing import List, Dict
+
+# Add parent directory to sys.path
+parent = Path(__file__).resolve().parent.parent
+sys.path.insert(0, str(parent))
+
+from utils.validate_yaml import read_data, validate_data, validate_yaml
+
+
+def write_data(filepath: str, data: List[Dict]) -> bool:
+    try:
+        with open(filepath, "w") as f:
+            yaml.safe_dump(data, f, sort_keys=False)
+        print(f"::notice::Wrote data to {filepath}.")
+    except FileNotFoundError:
+        print(f"::error::File not found: {filepath}")
+        return False
+    except OSError as e:
+        print(f"::error::Error writing file {filepath}: {e}")
+        return False
+    except yaml.YAMLError as e:
+        print(f"::error::YAML syntax error: {e}")
+        return False
+    return True
+
+
+def update_existing_data(
+    existing_data: List[Dict], new_data: List[Dict], out_file: str
+) -> bool:
+    existing_data.extend(new_data)
+    # validate merged data before writing
+    valid = validate_data(existing_data)
+    if not valid:
+        print(f"::error::Merged data is not valid, cannot write to {out_file}.")
+        return False
+    write_success = write_data(out_file, existing_data)
+    return write_success
+
+
+def merge_new_problems(new_problems_yaml_path: str, big_yaml_path: str) -> bool:
+    # Read and validate new data
+    new_data_status, new_data = read_data(new_problems_yaml_path)
+    if new_data_status != 0 or new_data is None:
+        print(
+            f"::error::New problems data could not be read from {new_problems_yaml_path}."
+        )
+        return False
+    valid = validate_data(new_data)
+    if not valid:
+        print(f"::error::New problems data in {new_problems_yaml_path} is not valid.")
+        return False
+
+    # Read existing data
+    existing_data_status, existing_data = read_data(big_yaml_path)
+    if existing_data_status != 0 or existing_data is None:
+        print(
+            f"::error::Existing problems data could not be read from {big_yaml_path}."
+        )
+        return False
+
+    # All valid, we can now just merge the dicts
+    assert existing_data is not None
+    assert new_data is not None
+    updated = update_existing_data(existing_data, new_data, big_yaml_path)
+    if not updated:
+        print(f"::error::Failed to update existing problems data in {big_yaml_path}.")
+        return False
+
+    # Validate resulting data
+    final_status, final_data = validate_yaml(big_yaml_path)
+    if final_status != 0 or final_data is None:
+        print(
+            f"::error::Merged data in {big_yaml_path} is not valid after merging new problems."
+        )
+        return False
+
+    print(
+        f"::notice::Merged {len(new_data)} new problems into {big_yaml_path}. {new_problems_yaml_path} can now be deleted."
+    )
+    return True
+
+
+if __name__ == "__main__":
+    if len(sys.argv) != 3:
+        print("Usage: python merge_yaml.py <new_problems.yaml> <big_yaml_path>")
+        sys.exit(1)
+    new_problems_yaml_path = sys.argv[1]
+    big_yaml_path = sys.argv[2]
+    status = merge_new_problems(new_problems_yaml_path, big_yaml_path)
+    if not status:
+        sys.exit(1)
+    else:
+        sys.exit(0)
diff --git a/utils/validate_yaml.py b/utils/validate_yaml.py
index 34f1899..79f4238 100644
--- a/utils/validate_yaml.py
+++ b/utils/validate_yaml.py
@@ -2,6 +2,7 @@
 
 import sys
 from pathlib import Path
+from typing import List, Dict, Tuple
 
 # Add parent directory to sys.path
 parent = Path(__file__).resolve().parent.parent
@@ -16,7 +17,7 @@
 UNIQUE_WARNING_FIELDS = ["reference", "implementation"]
 
 
-def read_data(filepath):
+def read_data(filepath: str) -> Tuple[int, List[Dict] | None]:
     try:
         with open(filepath, "r") as f:
             data = yaml.safe_load(f)
@@ -29,8 +30,11 @@ def read_data(filepath):
         return 1, None
 
 
-def check_format(data):
+def check_format(data: List[Dict]) -> bool:
     num_problems = len(data)
+    if not isinstance(data, list):
+        print("::error::YAML file should contain a list of entries.")
+        return False
     if len(data) < 1:
         print("::error::YAML file should contain at least one top level entry.")
         return False
@@ -42,14 +46,16 @@ def check_format(data):
             return False
         unique_fields.append({k: v for k, v in entry.items() if k in UNIQUE_FIELDS})
     for k in UNIQUE_FIELDS:
-        values = [entry[k] for entry in unique_fields]
+        values = [
+            entry[k] for entry in unique_fields if k in entry and entry[k] is not None
+        ]
         if len(values) != len(set(values)):
             print(f"::error::Field '{k}' must be unique across all entries.")
             return False
     return True
 
 
-def check_fields(data):
+def check_fields(data: Dict) -> bool:
     missing = [field for field in REQUIRED_FIELDS if field not in data]
     if missing:
         print(f"::error::Missing required fields: {', '.join(missing)}")
@@ -79,7 +85,7 @@ def check_fields(data):
     return True
 
 
-def check_novelty(data, checked_data):
+def check_novelty(data: Dict, checked_data: List[Dict]) -> bool:
     for field in UNIQUE_FIELDS + UNIQUE_WARNING_FIELDS:
         # skip empty fields
         if not data.get(field):
@@ -101,13 +107,10 @@ def check_novelty(data, checked_data):
     return True
 
 
-def validate_yaml(filepath):
-    status, data = read_data(filepath)
-    if status != 0:
-        sys.exit(1)
-    if not check_format(data):
-        sys.exit(1)
+def validate_data(data: List[Dict]) -> bool:
     assert data is not None
+    if not check_format(data):
+        return False
 
     checked_data = []
 
@@ -115,12 +118,24 @@ def validate_yaml(filepath):
         # Check required and unique fields
         if not check_fields(new_data) or not check_novelty(new_data, checked_data):
             print(f"::error::Validation failed for entry {i+1}.")
-            sys.exit(1)
+            return False
         checked_data.append(new_data)  # Add to checked data for novelty checks
 
     # YAML is valid if we reach this point
     print("YAML syntax is valid.")
-    sys.exit(0)
+
+    return True
+
+
+def validate_yaml(filepath: str) -> None:
+    status, data = read_data(filepath)
+    if status != 0 or data is None:
+        sys.exit(1)
+    valid = validate_data(data)
+    if not valid:
+        sys.exit(1)
+    else:
+        sys.exit(0)
 
 
 if __name__ == "__main__":

From f6ec9efeae4c0fc5580f9748b12cf068730ef510 Mon Sep 17 00:00:00 2001
From: Dvermetten <d.vermetten@gmail.com>
Date: Tue, 21 Apr 2026 18:00:14 +0200
Subject: [PATCH 5/9] Display code snippets

---
 docs/index.html          | 370 ++++++++++++++++++++++++++++++---------
 docs/javascript.html     | 205 ++++++++++++++++++++++
 docs/problems.html       | 165 ++++++++---------
 docs/table_styles.css    |  97 ++++++++++
 docs/table_template.html |   9 +
 yaml_to_html.py          |  38 +++-
 6 files changed, 727 insertions(+), 157 deletions(-)

diff --git a/docs/index.html b/docs/index.html
index 9b4cad3..78f6a70 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -55,7 +55,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
     </tr>
   </thead>
   <tbody>
-    <tr>
+    <tr data-problem-id="bbob">
       <td>BBOB</td>
       <td></td>
       <td>suite</td>
@@ -70,7 +70,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://doi.org/10.1080/10556788.2020.1808977">https://doi.org/10.1080/10556788.2020.1808977</a></td>
       <td><a href="https://github.com/numbbo/coco">https://github.com/numbbo/coco</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="bbob_biobj">
       <td>BBOB-biobj</td>
       <td></td>
       <td>suite</td>
@@ -85,7 +85,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://doi.org/10.48550/arXiv.1604.00359">https://doi.org/10.48550/arXiv.1604.00359</a></td>
       <td><a href="https://github.com/numbbo/coco">https://github.com/numbbo/coco</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="bbob_noisy">
       <td>BBOB-noisy</td>
       <td></td>
       <td>suite</td>
@@ -100,7 +100,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://hal.inria.fr/inria-00369466">https://hal.inria.fr/inria-00369466</a></td>
       <td><a href="https://web.archive.org/web/20210416065610/https://coco.gforge.inria.fr/doku.php?id=downloads">https://web.archive.org/web/20210416065610/https://coco.gforge.inria.fr/doku.php?id=downloads</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="bbob_largescale">
       <td>BBOB-largescale</td>
       <td></td>
       <td>suite</td>
@@ -115,7 +115,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://doi.org/10.48550/arXiv.1903.06396">https://doi.org/10.48550/arXiv.1903.06396</a></td>
       <td><a href="https://github.com/numbbo/coco">https://github.com/numbbo/coco</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="bbob_mixint">
       <td>BBOB-mixint</td>
       <td></td>
       <td>suite</td>
@@ -130,7 +130,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://doi.org/10.1145/3321707.3321868">https://doi.org/10.1145/3321707.3321868</a></td>
       <td><a href="https://github.com/numbbo/coco">https://github.com/numbbo/coco</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="bbob_biobj_mixint">
       <td>BBOB-biobj-mixint</td>
       <td></td>
       <td>suite</td>
@@ -145,7 +145,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://doi.org/10.1145/3321707.3321868">https://doi.org/10.1145/3321707.3321868</a></td>
       <td><a href="https://github.com/numbbo/coco">https://github.com/numbbo/coco</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="bbob_constrained">
       <td>BBOB-constrained</td>
       <td></td>
       <td>suite</td>
@@ -160,7 +160,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="http://numbbo.github.io/coco-doc/bbob-constrained/">http://numbbo.github.io/coco-doc/bbob-constrained/</a></td>
       <td><a href="https://github.com/numbbo/coco">https://github.com/numbbo/coco</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="morepo">
       <td>MOrepo</td>
       <td></td>
       <td>suite</td>
@@ -175,7 +175,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td></td>
       <td><a href="https://github.com/MCDMSociety/MOrepo">https://github.com/MCDMSociety/MOrepo</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="zdt">
       <td>ZDT</td>
       <td></td>
       <td>suite</td>
@@ -190,7 +190,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://doi.org/10.1162/106365600568202">https://doi.org/10.1162/106365600568202</a></td>
       <td><a href="https://github.com/anyoptimization/pymoo">https://github.com/anyoptimization/pymoo</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="dtlz">
       <td>DTLZ</td>
       <td></td>
       <td>suite</td>
@@ -205,7 +205,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://doi.org/10.1109/CEC.2002.1007032">https://doi.org/10.1109/CEC.2002.1007032</a></td>
       <td><a href="https://pymoo.org/problems/many/dtlz.html">https://pymoo.org/problems/many/dtlz.html</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="wfg">
       <td>WFG</td>
       <td></td>
       <td>suite</td>
@@ -220,7 +220,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://doi.org/10.1109/TEVC.2005.861417">https://doi.org/10.1109/TEVC.2005.861417</a></td>
       <td><a href="https://pymoo.org/problems/many/wfg.html">https://pymoo.org/problems/many/wfg.html</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="cdmp">
       <td>CDMP</td>
       <td></td>
       <td>suite</td>
@@ -235,7 +235,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://doi.org/10.1145/3321707.3321878">https://doi.org/10.1145/3321707.3321878</a></td>
       <td>?</td>
     </tr>
-    <tr>
+    <tr data-problem-id="sdp">
       <td>SDP</td>
       <td></td>
       <td>suite</td>
@@ -250,7 +250,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://doi.org/10.1109/TCYB.2019.2896021">https://doi.org/10.1109/TCYB.2019.2896021</a></td>
       <td>?</td>
     </tr>
-    <tr>
+    <tr data-problem-id="maop">
       <td>MaOP</td>
       <td></td>
       <td>suite</td>
@@ -265,7 +265,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://doi.org/10.1016/j.swevo.2019.02.003">https://doi.org/10.1016/j.swevo.2019.02.003</a></td>
       <td>?</td>
     </tr>
-    <tr>
+    <tr data-problem-id="bp">
       <td>BP</td>
       <td></td>
       <td>suite</td>
@@ -280,7 +280,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://doi.org/10.1109/CEC.2019.8790277">https://doi.org/10.1109/CEC.2019.8790277</a></td>
       <td>?</td>
     </tr>
-    <tr>
+    <tr data-problem-id="gpd">
       <td>GPD</td>
       <td></td>
       <td>generator</td>
@@ -295,7 +295,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://doi.org/10.1016/j.asoc.2020.106139">https://doi.org/10.1016/j.asoc.2020.106139</a></td>
       <td>?</td>
     </tr>
-    <tr>
+    <tr data-problem-id="etmof">
       <td>ETMOF</td>
       <td></td>
       <td>suite</td>
@@ -310,7 +310,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://doi.org/10.48550/arXiv.2110.08033">https://doi.org/10.48550/arXiv.2110.08033</a></td>
       <td><a href="https://github.com/songbai-liu/etmo">https://github.com/songbai-liu/etmo</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="mmopp">
       <td>MMOPP</td>
       <td></td>
       <td>suite</td>
@@ -325,7 +325,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="http://www5.zzu.edu.cn/system/_content/download.jsp?urltype=news.DownloadAttachUrl&owner=1327567121&wbfileid=4764412">http://www5.zzu.edu.cn/system/_content/download.jsp?urltype=news.DownloadAttachUrl&owner=1327567121&wbfileid=4764412</a></td>
       <td><a href="http://www5.zzu.edu.cn/ecilab/info/1036/1251.htm">http://www5.zzu.edu.cn/ecilab/info/1036/1251.htm</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="cfd">
       <td>CFD</td>
       <td>expensive evaluations 30s-15m</td>
       <td>suite</td>
@@ -340,7 +340,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://doi.org/10.1007/978-3-319-99259-4_24">https://doi.org/10.1007/978-3-319-99259-4_24</a></td>
       <td><a href="https://bitbucket.org/arahat/cfd-test-problem-suite">https://bitbucket.org/arahat/cfd-test-problem-suite</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="gbea">
       <td>GBEA</td>
       <td>expensive evaluations 5s-35s, RW-GAN-Mario and TopTrumps are part of GBEA</td>
       <td>suite</td>
@@ -355,7 +355,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://doi.org/10.1145/3321707.3321805">https://doi.org/10.1145/3321707.3321805</a></td>
       <td><a href="https://github.com/ttusar/coco-gbea">https://github.com/ttusar/coco-gbea</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="car_structure">
       <td>Car structure</td>
       <td>54 constraints</td>
       <td>suite</td>
@@ -370,7 +370,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://doi.org/10.1145/3205651.3205702">https://doi.org/10.1145/3205651.3205702</a></td>
       <td><a href="http://ladse.eng.isas.jaxa.jp/benchmark/">http://ladse.eng.isas.jaxa.jp/benchmark/</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="emo2017">
       <td>EMO2017</td>
       <td></td>
       <td>suite</td>
@@ -385,7 +385,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://www.ini.rub.de/PEOPLE/glasmtbl/projects/bbcomp/">https://www.ini.rub.de/PEOPLE/glasmtbl/projects/bbcomp/</a></td>
       <td><a href="https://www.ini.rub.de/PEOPLE/glasmtbl/projects/bbcomp/downloads/realworld-problems-bbcomp-EMO-2017.zip">https://www.ini.rub.de/PEOPLE/glasmtbl/projects/bbcomp/downloads/realworld-problems-bbcomp-EMO-2017.zip</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="jsec2019">
       <td>JSEC2019</td>
       <td>expensive evaluations 3s; 22 constraints</td>
       <td>single</td>
@@ -400,7 +400,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="http://www.jpnsec.org/files/competition2019/EC-Symposium-2019-Competition-English.html">http://www.jpnsec.org/files/competition2019/EC-Symposium-2019-Competition-English.html</a></td>
       <td><a href="http://www.jpnsec.org/files/competition2019/EC-Symposium-2019-Competition-English.html">http://www.jpnsec.org/files/competition2019/EC-Symposium-2019-Competition-English.html</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="re">
       <td>RE</td>
       <td></td>
       <td>suite</td>
@@ -415,7 +415,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://doi.org/10.1016/j.asoc.2020.106078">https://doi.org/10.1016/j.asoc.2020.106078</a></td>
       <td><a href="https://github.com/ryojitanabe/reproblems">https://github.com/ryojitanabe/reproblems</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="cre">
       <td>CRE</td>
       <td></td>
       <td>suite</td>
@@ -430,7 +430,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://doi.org/10.1016/j.asoc.2020.106078">https://doi.org/10.1016/j.asoc.2020.106078</a></td>
       <td><a href="https://github.com/ryojitanabe/reproblems">https://github.com/ryojitanabe/reproblems</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="radar_waveform">
       <td>Radar waveform</td>
       <td></td>
       <td>single</td>
@@ -445,7 +445,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://doi.org/10.1007/978-3-540-70928-2_53">https://doi.org/10.1007/978-3-540-70928-2_53</a></td>
       <td><a href="http://code.evanhughes.org/">http://code.evanhughes.org/</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="mf2">
       <td>MF2</td>
       <td></td>
       <td>suite</td>
@@ -460,7 +460,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://doi.org/10.21105/joss.02049">https://doi.org/10.21105/joss.02049</a></td>
       <td><a href="https://github.com/sjvrijn/mf2">https://github.com/sjvrijn/mf2</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="amvop">
       <td>AMVOP</td>
       <td></td>
       <td>suite</td>
@@ -475,7 +475,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://doi.org/10.1109/TEVC.2013.2281531">https://doi.org/10.1109/TEVC.2013.2281531</a></td>
       <td>?</td>
     </tr>
-    <tr>
+    <tr data-problem-id="rwmvop">
       <td>RWMVOP</td>
       <td></td>
       <td>suite</td>
@@ -490,7 +490,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://doi.org/10.1109/TEVC.2013.2281531">https://doi.org/10.1109/TEVC.2013.2281531</a></td>
       <td>?</td>
     </tr>
-    <tr>
+    <tr data-problem-id="sbox_cost">
       <td>SBOX-COST</td>
       <td>problems from BBOB but allows instances with the optimum close to the boundary</td>
       <td>suite</td>
@@ -505,7 +505,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://doi.org/10.48550/arXiv.2305.12221">https://doi.org/10.48550/arXiv.2305.12221</a></td>
       <td><a href="https://github.com/IOHprofiler/IOHexperimenter/">https://github.com/IOHprofiler/IOHexperimenter/</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="mnk_landscapes">
       <td>ρMNK-Landscapes</td>
       <td>tunable variable and objective dimensions; tunable multimodality and correlation between objectives</td>
       <td>generator</td>
@@ -520,7 +520,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://doi.org/10.1016/j.ejor.2012.12.019">https://doi.org/10.1016/j.ejor.2012.12.019</a></td>
       <td><a href="https://gitlab.com/aliefooghe/mocobench/">https://gitlab.com/aliefooghe/mocobench/</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="mubqp">
       <td>mUBQP</td>
       <td>tunable variable and objective dimensions; tunable density and correlation between objectives</td>
       <td>generator</td>
@@ -535,7 +535,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://doi.org/10.1016/j.asoc.2013.11.008">https://doi.org/10.1016/j.asoc.2013.11.008</a></td>
       <td><a href="https://gitlab.com/aliefooghe/mocobench/">https://gitlab.com/aliefooghe/mocobench/</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="mtsp">
       <td>ρmTSP</td>
       <td>tunable variable and objective dimensions; tunable instance type (euclidian/random); tunable correlation between objectives</td>
       <td>generator</td>
@@ -550,7 +550,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://doi.org/10.1007/978-3-319-45823-6_40">https://doi.org/10.1007/978-3-319-45823-6_40</a></td>
       <td><a href="https://gitlab.com/aliefooghe/mocobench/">https://gitlab.com/aliefooghe/mocobench/</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="cec2015_dmoo">
       <td>CEC2015-DMOO</td>
       <td></td>
       <td>suite</td>
@@ -565,7 +565,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td>Benchmark Functions for CEC 2015 Special Session and Competition on Dynamic Multi-objective Optimization</td>
       <td></td>
     </tr>
-    <tr>
+    <tr data-problem-id="ealain">
       <td>Ealain</td>
       <td>Real-world-like, easily extensible to increase complexity</td>
       <td>generator</td>
@@ -580,7 +580,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://doi.org/10.1145/3638530.3654299">https://doi.org/10.1145/3638530.3654299</a></td>
       <td><a href="https://github.com/qrenau/Ealain">https://github.com/qrenau/Ealain</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="ma_bbob">
       <td>MA-BBOB</td>
       <td>Generator that creates affine combinations of BBOB functions</td>
       <td>generator</td>
@@ -595,7 +595,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://doi.org/10.1145/3673908">https://doi.org/10.1145/3673908</a></td>
       <td><a href="https://github.com/IOHprofiler/IOHexperimenter/blob/master/example/Competitions/MA-BBOB/Example_MABBOB.ipynb">https://github.com/IOHprofiler/IOHexperimenter/blob/master/example/Competitions/MA-BBOB/Example_MABBOB.ipynb</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="mpm2">
       <td>MPM2</td>
       <td>nonlinear nonseparable nonsymmetric; scalable in terms of time to evaluate the objective function</td>
       <td>generator</td>
@@ -610,7 +610,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://ls11-www.cs.tu-dortmund.de/_media/techreports/tr15-01.pdf">https://ls11-www.cs.tu-dortmund.de/_media/techreports/tr15-01.pdf</a></td>
       <td><a href="https://github.com/jakobbossek/smoof/blob/master/inst/mpm2.py">https://github.com/jakobbossek/smoof/blob/master/inst/mpm2.py</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="convex_dtlz2">
       <td>Convex DTLZ2</td>
       <td>Variant of DTLZ2 with a convex Pareto front (instead of concave)</td>
       <td>single</td>
@@ -625,7 +625,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://doi.org/10.1109/TEVC.2013.2281535">https://doi.org/10.1109/TEVC.2013.2281535</a></td>
       <td>?</td>
     </tr>
-    <tr>
+    <tr data-problem-id="inverted_dtlz1">
       <td>Inverted DTLZ1</td>
       <td>Variant of DTLZ1 with an inverted Pareto front</td>
       <td>single</td>
@@ -640,7 +640,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://doi.org/10.1109/TEVC.2013.2281534">https://doi.org/10.1109/TEVC.2013.2281534</a></td>
       <td>?</td>
     </tr>
-    <tr>
+    <tr data-problem-id="minus_dtlz">
       <td>Minus DTLZ</td>
       <td>Variant of DTLZ that minimises the inverse of the base DTLZ functions</td>
       <td>suite</td>
@@ -655,7 +655,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://doi.org/10.1109/TEVC.2016.2587749">https://doi.org/10.1109/TEVC.2016.2587749</a></td>
       <td>?</td>
     </tr>
-    <tr>
+    <tr data-problem-id="minus_wfg">
       <td>Minus WFG</td>
       <td>Variant of WFG that minimises the inverse of the base WFG functions</td>
       <td>suite</td>
@@ -670,7 +670,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://doi.org/10.1109/TEVC.2016.2587749">https://doi.org/10.1109/TEVC.2016.2587749</a></td>
       <td>?</td>
     </tr>
-    <tr>
+    <tr data-problem-id="l1_zdt">
       <td>L1-ZDT</td>
       <td>Variant of ZDT with linkages between variables within one of two groups but not between variables in a different group; Linear recombination operators can potentially take advantage of the problem structure</td>
       <td>suite</td>
@@ -685,7 +685,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://doi.org/10.1145/1143997.1144179">https://doi.org/10.1145/1143997.1144179</a></td>
       <td>?</td>
     </tr>
-    <tr>
+    <tr data-problem-id="l2_zdt">
       <td>L2-ZDT</td>
       <td>Variant of ZDT with linkages between all variables; Linear recombination operators can potentially take advantage of the problem structure</td>
       <td>suite</td>
@@ -700,7 +700,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://doi.org/10.1145/1143997.1144179">https://doi.org/10.1145/1143997.1144179</a></td>
       <td>?</td>
     </tr>
-    <tr>
+    <tr data-problem-id="l3_zdt">
       <td>L3-ZDT</td>
       <td>Variant of L2-ZDT using a mapping to prevent linear recombination operators from potentially taking advantage of the problem structure</td>
       <td>suite</td>
@@ -715,7 +715,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://doi.org/10.1145/1143997.1144179">https://doi.org/10.1145/1143997.1144179</a></td>
       <td>?</td>
     </tr>
-    <tr>
+    <tr data-problem-id="l2_dtlz">
       <td>L2-DTLZ</td>
       <td>Variant of DTLZ2 and DTLZ3 with linkages between all variables; Linear recombination operators can potentially take advantage of the problem structure</td>
       <td>suite</td>
@@ -730,7 +730,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://doi.org/10.1145/1143997.1144179">https://doi.org/10.1145/1143997.1144179</a></td>
       <td>?</td>
     </tr>
-    <tr>
+    <tr data-problem-id="l3_dtlz">
       <td>L3-DTLZ</td>
       <td>Variant of L2-DTLZ using a mapping to prevent linear recombination operators from potentially taking advantage of the problem structure</td>
       <td>suite</td>
@@ -745,7 +745,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://doi.org/10.1145/1143997.1144179">https://doi.org/10.1145/1143997.1144179</a></td>
       <td>?</td>
     </tr>
-    <tr>
+    <tr data-problem-id="cec2018_dt_cec2018_competition_on_dynamic_multiobjective_optimisation">
       <td>CEC2018 DT - CEC2018 Competition on Dynamic Multiobjective Optimisation</td>
       <td>14 problems. Time-dependent: Pareto front/Pareto set geometry; irregular Pareto front shapes; variable-linkage; number of disconnected Pareto front segments; etc.</td>
       <td>suite</td>
@@ -760,7 +760,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://www.academia.edu/download/94499025/TR-CEC2018-DMOP-Competition.pdf">https://www.academia.edu/download/94499025/TR-CEC2018-DMOP-Competition.pdf</a></td>
       <td><a href="https://pymoo.org/problems/dynamic/df.html">https://pymoo.org/problems/dynamic/df.html</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="modact_multiobjective_design_of_actuators">
       <td>MODAct - multiobjective design of actuators</td>
       <td>Realistic Constrained Multi-Objective Optimization Benchmark Problems from Design. Need the <a href="https://github.com/epfl-lamd/modact">https://github.com/epfl-lamd/modact</a> package installed; evaluation times around 20ms</td>
       <td>suite</td>
@@ -775,7 +775,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://doi.org/10.1109/TEVC.2020.3020046">https://doi.org/10.1109/TEVC.2020.3020046</a></td>
       <td><a href="https://pymoo.org/problems/constrained/modact.html">https://pymoo.org/problems/constrained/modact.html</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="iohclustering">
       <td>IOHClustering</td>
       <td>Set of benchmark problems from clustering: optimization task is selecting cluster centers for a given set of data, with the number of clusters defining problem dimensionality. Includes both a suite and a generator. Based on ML clustering datasets</td>
       <td>suite; generator</td>
@@ -790,7 +790,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://arxiv.org/pdf/2505.09233">https://arxiv.org/pdf/2505.09233</a></td>
       <td><a href="https://github.com/IOHprofiler/IOHClustering">https://github.com/IOHprofiler/IOHClustering</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="gnbg_ii">
       <td>GNBG-II</td>
       <td>Generalized Numerical Benchmark Generator (version 2). Also in IOH <a href="https://github.com/IOHprofiler/IOHGNBG">https://github.com/IOHprofiler/IOHGNBG</a></td>
       <td>suite; generator</td>
@@ -805,7 +805,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://dl.acm.org/doi/pdf/10.1145/3712255.3734271">https://dl.acm.org/doi/pdf/10.1145/3712255.3734271</a></td>
       <td><a href="https://github.com/rohitsalgotra/GNBG-II">https://github.com/rohitsalgotra/GNBG-II</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="gnbg">
       <td>GNBG</td>
       <td>Generalized Numerical Benchmark Generator</td>
       <td>suite; generator</td>
@@ -820,7 +820,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://arxiv.org/abs/2312.07083">https://arxiv.org/abs/2312.07083</a></td>
       <td><a href="https://github.com/Danial-Yazdani/GNBG-Generator">https://github.com/Danial-Yazdani/GNBG-Generator</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="dynamicbinval">
       <td>DynamicBinVal</td>
       <td>Four versions of the dynamic binary value problem</td>
       <td>suite</td>
@@ -835,7 +835,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://arxiv.org/pdf/2404.15837">https://arxiv.org/pdf/2404.15837</a></td>
       <td><a href="https://github.com/IOHprofiler/IOHexperimenter">https://github.com/IOHprofiler/IOHexperimenter</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="pbo">
       <td>PBO</td>
       <td>Suite of 25 binary optimization problems</td>
       <td>suite</td>
@@ -850,7 +850,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://dl.acm.org/doi/pdf/10.1145/3319619.3326810">https://dl.acm.org/doi/pdf/10.1145/3319619.3326810</a></td>
       <td><a href="https://github.com/IOHprofiler/IOHexperimenter">https://github.com/IOHprofiler/IOHexperimenter</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="w_model">
       <td>W-model</td>
       <td>Tunable generator for binary optimization based on several difficulty features</td>
       <td>generator</td>
@@ -865,7 +865,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://dl.acm.org/doi/abs/10.1145/3205651.3208240?casa_token=S4U_Pi9f6MwAAAAA:U9ztNTPwmupT8K3GamWZfBL7-8fqjxPtr_kprv51vdwA-REsp0EyOFGa99BtbANb0XbqyrVg795hIw">https://dl.acm.org/doi/abs/10.1145/3205651.3208240?casa_token=S4U_Pi9f6MwAAAAA:U9ztNTPwmupT8K3GamWZfBL7-8fqjxPtr_kprv51vdwA-REsp0EyOFGa99BtbANb0XbqyrVg795hIw</a></td>
       <td><a href="https://github.com/thomasWeise/BBDOB_W_Model">https://github.com/thomasWeise/BBDOB_W_Model</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="submodular_optimitzation">
       <td>Submodular Optimitzation</td>
       <td>set of graph-based submodular optimization problems from 4 problem types</td>
       <td>suite</td>
@@ -880,7 +880,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10254181">https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10254181</a></td>
       <td><a href="https://github.com/IOHprofiler/IOHexperimenter">https://github.com/IOHprofiler/IOHexperimenter</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="cec2013">
       <td>CEC2013</td>
       <td>suite used for cec2013 competition. Also in IOH <a href="https://github.com/IOHprofiler/IOHexperimenter">https://github.com/IOHprofiler/IOHexperimenter</a></td>
       <td>suite</td>
@@ -895,7 +895,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://peerj.com/articles/cs-2671/CEC2013.pdf">https://peerj.com/articles/cs-2671/CEC2013.pdf</a></td>
       <td><a href="https://github.com/P-N-Suganthan/CEC2013">https://github.com/P-N-Suganthan/CEC2013</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="cec2022">
       <td>CEC2022</td>
       <td>suite used for cec2022 competition. Also in IOH <a href="https://github.com/IOHprofiler/IOHexperimenter">https://github.com/IOHprofiler/IOHexperimenter</a></td>
       <td>suite</td>
@@ -910,7 +910,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://github.com/P-N-Suganthan/2022-SO-BO/blob/main/CEC2022%20TR.pdf">https://github.com/P-N-Suganthan/2022-SO-BO/blob/main/CEC2022%20TR.pdf</a></td>
       <td><a href="https://github.com/P-N-Suganthan/2022-SO-BO">https://github.com/P-N-Suganthan/2022-SO-BO</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="onemaxsphere_zeromaxsphere">
       <td>Onemax+Sphere / Zeromax+Sphere</td>
       <td></td>
       <td>single</td>
@@ -925,7 +925,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://doi.org/10.1145/3449726.3459521">https://doi.org/10.1145/3449726.3459521</a></td>
       <td>None</td>
     </tr>
-    <tr>
+    <tr data-problem-id="onemaxsphere_deceptivetraprotatedellipsoid">
       <td>Onemax+Sphere / DeceptiveTrap+RotatedEllipsoid</td>
       <td></td>
       <td>single</td>
@@ -940,7 +940,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://doi.org/10.1145/3449726.3459521">https://doi.org/10.1145/3449726.3459521</a></td>
       <td>None</td>
     </tr>
-    <tr>
+    <tr data-problem-id="inversedeceptivetraprotatedellipsoid_deceptivetraprotatedellipsoid">
       <td>InverseDeceptiveTrap+RotatedEllipsoid / DeceptiveTrap+RotatedEllipsoid</td>
       <td></td>
       <td>single</td>
@@ -955,7 +955,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://doi.org/10.1145/3449726.3459521">https://doi.org/10.1145/3449726.3459521</a></td>
       <td>None</td>
     </tr>
-    <tr>
+    <tr data-problem-id="porkchopplotinterplanetarytrajectory">
       <td>PorkchopPlotInterplanetaryTrajectory</td>
       <td></td>
       <td>suite</td>
@@ -970,7 +970,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://doi.org/10.1109/CEC65147.2025.11042973">https://doi.org/10.1109/CEC65147.2025.11042973</a></td>
       <td><a href="https://github.com/ShuaiqunPan/Transfer_Random_forests_BBOB_Real_world">https://github.com/ShuaiqunPan/Transfer_Random_forests_BBOB_Real_world</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="kinematicsrobotarm">
       <td>KinematicsRobotArm</td>
       <td></td>
       <td>suite</td>
@@ -985,7 +985,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://doi.org/10.1023/A:1013258808932">https://doi.org/10.1023/A:1013258808932</a></td>
       <td><a href="https://github.com/ShuaiqunPan/Transfer_Random_forests_BBOB_Real_world">https://github.com/ShuaiqunPan/Transfer_Random_forests_BBOB_Real_world</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="vehicledynamics">
       <td>VehicleDynamics</td>
       <td></td>
       <td>suite</td>
@@ -1000,7 +1000,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://www.scitepress.org/Papers/2023/121580/121580.pdf">https://www.scitepress.org/Papers/2023/121580/121580.pdf</a></td>
       <td><a href="https://zenodo.org/records/8307853">https://zenodo.org/records/8307853</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="mechbench">
       <td>MECHBench</td>
       <td>This is a set of problems with inspiration from Structural Mechanics Design Optimization. The suite comprises three physical models, from which the user may define different kind of problems which impact the final design output.</td>
       <td>Problem Suite</td>
@@ -1015,7 +1015,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://arxiv.org/abs/2511.10821">https://arxiv.org/abs/2511.10821</a></td>
       <td><a href="https://github.com/BayesOptApp/MECHBench">https://github.com/BayesOptApp/MECHBench</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="expobench">
       <td>EXPObench</td>
       <td>Wind farm layout optimization, gas filter design, pipe shape optimization, hyperparameter tuning, and hospital simulation</td>
       <td>Problem Suite</td>
@@ -1030,7 +1030,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://doi.org/10.1016/j.asoc.2023.110744">https://doi.org/10.1016/j.asoc.2023.110744</a></td>
       <td><a href="https://github.com/AlgTUDelft/ExpensiveOptimBenchmark">https://github.com/AlgTUDelft/ExpensiveOptimBenchmark</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="gasoline_direct_injection_engine_design">
       <td>Gasoline direct injection engine design</td>
       <td>A multi-objective optimization problem seeking to minimize fuel consumption and NOx emissions over a two-minute dynamic duty cycle, subject to five constraints (turbine inlet temperature, number of knock occurrences, peak cylinder pressure, peak cylinder pressure rise, total work). Seven decision variables are defined: four define the hardware choices of cylinder compression ratio, turbo machinery and EGR cooler sizing; three relate to control variables that parameterise the engine control logic.</td>
       <td>Single Problem</td>
@@ -1045,7 +1045,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td></td>
       <td><a href="https://doi.org/10.1016/j.ejor.2022.08.032">https://doi.org/10.1016/j.ejor.2022.08.032</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="beacon">
       <td>BEACON</td>
       <td>Generator for bi-objective benchmark problems with explicitly controlled correlations in continuous spaces.</td>
       <td>Generator</td>
@@ -1060,7 +1060,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://dl.acm.org/doi/10.1145/3712255.3734303">https://dl.acm.org/doi/10.1145/3712255.3734303</a></td>
       <td><a href="https://github.com/Stebbet/BEACON/">https://github.com/Stebbet/BEACON/</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="tulipaenergy">
       <td>TulipaEnergy</td>
       <td>Determine the optimal investment and operation decisions  for different types of assets in the energy system (production, consumption, conversion, storage, and transport), while minimizing loss of load.</td>
       <td>Problem Suite</td>
@@ -1075,7 +1075,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td>See <a href="https://tulipaenergy.github.io/TulipaEnergyModel.jl/stable/40-scientific-foundation/45-scientific-references">https://tulipaenergy.github.io/TulipaEnergyModel.jl/stable/40-scientific-foundation/45-scientific-references</a></td>
       <td><a href="https://tulipaenergy.github.io/TulipaEnergyModel.jl/stable/">https://tulipaenergy.github.io/TulipaEnergyModel.jl/stable/</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="ato">
       <td>ATO</td>
       <td>Parameters of the Modules of the Automatic Train Operation should be optimized. The parameters are continuous with different ranges. There are two objectives (minimizing energy consumption, minimizing driving duration.</td>
       <td>Single Problem</td>
@@ -1090,7 +1090,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td></td>
       <td>-</td>
     </tr>
-    <tr>
+    <tr data-problem-id="brachytherapy_treatment_planning">
       <td>Brachytherapy treatment planning</td>
       <td>Treatment planning for internal radiation therapy</td>
       <td>Problem Suite</td>
@@ -1105,7 +1105,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://www.sciencedirect.com/science/article/pii/S1538472123016781">https://www.sciencedirect.com/science/article/pii/S1538472123016781</a></td>
       <td></td>
     </tr>
-    <tr>
+    <tr data-problem-id="fleetopt">
       <td>FleetOpt</td>
       <td>Healthcare organisation in the UK provided data about their current fleet of vehicles to conduct non-emergency heathcare trips in the Argyll and Bute region of Scotland, UK. They also provided historical data about the trips the vehicles took and about the bases which the vehicles return to. The aim is to reduce the existing fleet of vehicles while still ensuring all trips can be covered. Moving a vehicle from one base to another to help cover trips is OK as long as the original base can still cover its trips. Link to paper with more details: <a href="https://dl.acm.org/doi/abs/10.1145/3638530.3664137">https://dl.acm.org/doi/abs/10.1145/3638530.3664137</a></td>
       <td>Single Problem</td>
@@ -1120,7 +1120,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://dl.acm.org/doi/abs/10.1145/3638530.3664137">https://dl.acm.org/doi/abs/10.1145/3638530.3664137</a></td>
       <td>Not public: was done for real client with their private data</td>
     </tr>
-    <tr>
+    <tr data-problem-id="building_spatial_design">
       <td>Building spatial design</td>
       <td>Optimise the spatial layout of a building to: minimise energy consumption for climate control, and minimise the strain on the structure</td>
       <td>Single Problem</td>
@@ -1135,7 +1135,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://hdl.handle.net/1887/81789">https://hdl.handle.net/1887/81789</a></td>
       <td><a href="https://github.com/TUe-excellent-buildings/BSO-toolbox">https://github.com/TUe-excellent-buildings/BSO-toolbox</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="electric_motor_design_optimization">
       <td>Electric Motor Design Optimization</td>
       <td>The goal is to find a design of a synchronous electric motor for power steering systems that minimizes costs and satisfies all constraints.</td>
       <td>Single Problem</td>
@@ -1150,7 +1150,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://dis.ijs.si/tea/Publications/Tusar23Multistep.pdf">https://dis.ijs.si/tea/Publications/Tusar23Multistep.pdf</a> (paper in Slovene)</td>
       <td>Implementation not freely available</td>
     </tr>
-    <tr>
+    <tr data-problem-id="bono_bench">
       <td>BONO-Bench</td>
       <td>Bi-objective problem generator and suite with scalable continuous decision space. Features complex problem properties (different types of multimodality and challenges in decision and objective space) as well as Pareto front approximations with error guarantees for the hypervolume and exact R2 indicators.</td>
       <td>Generator</td>
@@ -1165,7 +1165,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td></td>
       <td><a href="https://github.com/schaepermeier/bonobench">https://github.com/schaepermeier/bonobench</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="randoptgen">
       <td>RandOptGen</td>
       <td>RandOptGen: A Unified Random Problem Generator for Single-and Multi-Objective Optimization Problems with Mixed-Variable Input Spaces</td>
       <td>Generator</td>
@@ -1180,7 +1180,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td></td>
       <td><a href="https://github.com/MALEO-research-group/RandOptGen">https://github.com/MALEO-research-group/RandOptGen</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="cuter">
       <td>CUTEr</td>
       <td>A constrained and unconstrained testing environment</td>
       <td>Problem Suite</td>
@@ -1195,7 +1195,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://dl.acm.org/doi/10.1145/962437.962439">https://dl.acm.org/doi/10.1145/962437.962439</a></td>
       <td>Not Found</td>
     </tr>
-    <tr>
+    <tr data-problem-id="cutest">
       <td>CUTEst</td>
       <td>The Constrained and Unconstrained Testing Environment with safe threads (CUTEst) for optimization software</td>
       <td>Problem Suite</td>
@@ -1210,7 +1210,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://link.springer.com/article/10.1007/s10589-014-9687-3">https://link.springer.com/article/10.1007/s10589-014-9687-3</a></td>
       <td><a href="https://github.com/jfowkes/pycutest">https://github.com/jfowkes/pycutest</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="puboi">
       <td>PUBOi</td>
       <td>A benchmark in which variable importance is tunable, based on the Walsh function</td>
       <td>Generator</td>
@@ -1234,10 +1234,24 @@ <h2>OPL &ndash; Optimisation problem library</h2>
     <h3 class="details-title">Problem details</h3>
     <p class="details-hint" id="problem-details-hint">Click a table row to inspect full details.</p>
     <dl class="details-grid" id="problem-details-content"></dl>
+    <div class="snippet-shell" id="problem-snippet-shell">
+        <h4 class="snippet-title">README.md</h4>
+        <p class="snippet-hint" id="problem-readme-hint">README content for this snippet folder will appear here.</p>
+        <div class="snippet-readme markdown-body" id="problem-readme-content"></div>
+        <h4 class="snippet-title">Snippet: <code id="problem-snippet-path">call_{problem_id}.py</code></h4>
+        <p class="snippet-meta"><a id="problem-snippet-folder-link" href="#" target="_blank" rel="noopener noreferrer">Open snippet folder</a></p>
+        <p class="snippet-hint" id="problem-snippet-hint">Select a problem to check for an available code snippet.</p>
+        <pre class="snippet-code"><code id="problem-snippet-content"></code></pre>
+    </div>
 </section>
 
 
 <script>
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+
 const table = new DataTable('#problems', {
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@@ -1279,6 +1293,12 @@ <h3 class="details-title">Problem details</h3>
 
 const detailsHint = document.getElementById('problem-details-hint');
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@@ -1292,6 +1312,196 @@ <h3 class="details-title">Problem details</h3>
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+    }
+
+    if (markdownParserPromise) {
+        return markdownParserPromise;
+    }
+
+    markdownParserPromise = new Promise((resolve, reject) => {
+        const script = document.createElement('script');
+        script.src = 'https://cdn.jsdelivr.net/npm/marked/marked.min.js';
+        script.async = true;
+        script.onload = () => {
+            if (window.marked?.parse) {
+                resolve(window.marked.parse);
+            } else {
+                reject(new Error('Marked loaded without parse function'));
+            }
+        };
+        script.onerror = () => reject(new Error('Failed to load marked library'));
+        document.head.appendChild(script);
+    });
+
+    return markdownParserPromise;
+};
+
+const renderMarkdownToHtml = async (markdownText) => {
+    if (!markdownText) {
+        return '';
+    }
+
+    try {
+        const parse = await ensureMarkdownParser();
+        return parse(markdownText);
+    } catch (error) {
+        return `<pre><code>${escapeHtml(markdownText)}</code></pre>`;
+    }
+};
+
+const toProblemId = (value) => {
+    if (!value) {
+        return '';
+    }
+
+    return value
+        .toLowerCase()
+        .trim()
+        .replace(/[-\s]+/g, '_')
+        .replace(/[^a-z0-9_]/g, '')
+        .replace(/_+/g, '_')
+        .replace(/^_+|_+$/g, '');
+};
+
+const setSnippetState = ({
+    path = '',
+    hint = '',
+    code = '',
+    folderHref = '',
+    readmeHintText = '',
+    readmeHtml = ''
+}) => {
+    if (snippetPath) {
+        snippetPath.textContent = path || 'call_{problem_id}.py';
+    }
+
+    if (snippetHint) {
+        snippetHint.textContent = hint;
+    }
+
+    if (snippetContent) {
+        snippetContent.textContent = code;
+    }
+
+    if (snippetFolderLink) {
+        if (folderHref) {
+            snippetFolderLink.href = folderHref;
+            snippetFolderLink.textContent = 'Open snippet folder';
+            snippetFolderLink.removeAttribute('aria-disabled');
+        } else {
+            snippetFolderLink.href = '#';
+            snippetFolderLink.textContent = 'Snippet folder unavailable';
+            snippetFolderLink.setAttribute('aria-disabled', 'true');
+        }
+    }
+
+    if (readmeHint) {
+        readmeHint.textContent = readmeHintText;
+    }
+
+    if (readmeContent) {
+        readmeContent.innerHTML = readmeHtml;
+    }
+};
+
+const loadSnippet = async (problemId) => {
+    if (!problemId) {
+        setSnippetState({
+            hint: 'No problem id available for this row.',
+            code: '',
+            readmeHintText: 'README cannot be loaded without a problem id.',
+            readmeHtml: ''
+        });
+        return;
+    }
+
+    const fileName = `call_${problemId}.py`;
+    const rawUrl = `${SNIPPET_BASE_URL}/${encodeURIComponent(problemId)}/${encodeURIComponent(fileName)}`;
+    const readmeRawUrl = `${SNIPPET_BASE_URL}/${encodeURIComponent(problemId)}/README.md`;
+    const folderUrl = `${SNIPPET_TREE_BASE_URL}/${encodeURIComponent(problemId)}`;
+
+    setSnippetState({
+        path: `${problemId}/${fileName}`,
+        hint: 'Loading snippet from repository...',
+        code: '',
+        folderHref: folderUrl,
+        readmeHintText: 'Loading README.md from repository...',
+        readmeHtml: ''
+    });
+
+    if (snippetCache.has(problemId)) {
+        const cached = snippetCache.get(problemId);
+        setSnippetState({
+            path: `${problemId}/${fileName}`,
+            hint: cached.codeHint,
+            code: cached.code,
+            folderHref: folderUrl,
+            readmeHintText: cached.readmeHint,
+            readmeHtml: cached.readmeHtml
+        });
+        return;
+    }
+
+    try {
+        const [snippetResponse, readmeResponse] = await Promise.all([
+            fetch(rawUrl),
+            fetch(readmeRawUrl)
+        ]);
+
+        let code = '';
+        let codeHint = '';
+        if (snippetResponse.status === 404) {
+            codeHint = 'No snippet file found for this problem id in the code_snippets branch.';
+        } else if (!snippetResponse.ok) {
+            codeHint = `Unable to fetch snippet (HTTP ${snippetResponse.status}).`;
+        } else {
+            code = await snippetResponse.text();
+            codeHint = 'Snippet found in code_snippets branch.';
+        }
+
+        let readme = '';
+        let readmeHtml = '';
+        let readmeHintText = '';
+        if (readmeResponse.status === 404) {
+            readmeHintText = 'No README.md found in this snippet folder.';
+        } else if (!readmeResponse.ok) {
+            readmeHintText = `Unable to fetch README.md (HTTP ${readmeResponse.status}).`;
+        } else {
+            readme = await readmeResponse.text();
+            readmeHtml = await renderMarkdownToHtml(readme);
+            readmeHintText = 'README.md loaded from snippet folder.';
+        }
+
+        snippetCache.set(problemId, {
+            code,
+            codeHint,
+            readmeHint: readmeHintText,
+            readmeHtml
+        });
+
+        setSnippetState({
+            path: `${problemId}/${fileName}`,
+            hint: codeHint,
+            code,
+            folderHref: folderUrl,
+            readmeHintText,
+            readmeHtml
+        });
+    } catch (error) {
+        setSnippetState({
+            path: `${problemId}/${fileName}`,
+            hint: 'Network error while fetching snippet.',
+            code: '',
+            folderHref: folderUrl,
+            readmeHintText: 'Network error while fetching README.md.',
+            readmeHtml: ''
+        });
+    }
+};
+
 const renderRowDetails = (rowApi) => {
     const rowData = rowApi.data();
     if (!rowData || !detailsContent) {
@@ -1377,6 +1587,10 @@ <h3 class="details-title">Problem details</h3>
 
         targetRow.classList.add('is-active');
         renderRowDetails(rowApi);
+
+        const name = stripHtml(rowApi.data()[0]);
+        const problemId = targetRow.getAttribute('data-problem-id') || toProblemId(name);
+        loadSnippet(problemId);
     });
 }
 
diff --git a/docs/javascript.html b/docs/javascript.html
index 73f9e58..80dde81 100644
--- a/docs/javascript.html
+++ b/docs/javascript.html
@@ -1,6 +1,11 @@
 
 
 <script>
+const SNIPPET_BASE_URL = 'https://raw.githubusercontent.com/OpenOptimizationOrg/OPL/code_snippets/code_snippets';
+const SNIPPET_TREE_BASE_URL = 'https://github.com/OpenOptimizationOrg/OPL/tree/code_snippets/code_snippets';
+const snippetCache = new Map();
+let markdownParserPromise = null;
+
 const table = new DataTable('#problems', {
     layout: {
         topStart: 'search',
@@ -42,6 +47,12 @@
 
 const detailsHint = document.getElementById('problem-details-hint');
 const detailsContent = document.getElementById('problem-details-content');
+const snippetPath = document.getElementById('problem-snippet-path');
+const snippetHint = document.getElementById('problem-snippet-hint');
+const snippetContent = document.getElementById('problem-snippet-content');
+const snippetFolderLink = document.getElementById('problem-snippet-folder-link');
+const readmeHint = document.getElementById('problem-readme-hint');
+const readmeContent = document.getElementById('problem-readme-content');
 
 const stripHtml = (value) => {
     const wrapper = document.createElement('div');
@@ -55,6 +66,196 @@
     return wrapper.innerHTML;
 };
 
+const ensureMarkdownParser = () => {
+    if (window.marked?.parse) {
+        return Promise.resolve(window.marked.parse);
+    }
+
+    if (markdownParserPromise) {
+        return markdownParserPromise;
+    }
+
+    markdownParserPromise = new Promise((resolve, reject) => {
+        const script = document.createElement('script');
+        script.src = 'https://cdn.jsdelivr.net/npm/marked/marked.min.js';
+        script.async = true;
+        script.onload = () => {
+            if (window.marked?.parse) {
+                resolve(window.marked.parse);
+            } else {
+                reject(new Error('Marked loaded without parse function'));
+            }
+        };
+        script.onerror = () => reject(new Error('Failed to load marked library'));
+        document.head.appendChild(script);
+    });
+
+    return markdownParserPromise;
+};
+
+const renderMarkdownToHtml = async (markdownText) => {
+    if (!markdownText) {
+        return '';
+    }
+
+    try {
+        const parse = await ensureMarkdownParser();
+        return parse(markdownText);
+    } catch (error) {
+        return `<pre><code>${escapeHtml(markdownText)}</code></pre>`;
+    }
+};
+
+const toProblemId = (value) => {
+    if (!value) {
+        return '';
+    }
+
+    return value
+        .toLowerCase()
+        .trim()
+        .replace(/[-\s]+/g, '_')
+        .replace(/[^a-z0-9_]/g, '')
+        .replace(/_+/g, '_')
+        .replace(/^_+|_+$/g, '');
+};
+
+const setSnippetState = ({
+    path = '',
+    hint = '',
+    code = '',
+    folderHref = '',
+    readmeHintText = '',
+    readmeHtml = ''
+}) => {
+    if (snippetPath) {
+        snippetPath.textContent = path || 'call_{problem_id}.py';
+    }
+
+    if (snippetHint) {
+        snippetHint.textContent = hint;
+    }
+
+    if (snippetContent) {
+        snippetContent.textContent = code;
+    }
+
+    if (snippetFolderLink) {
+        if (folderHref) {
+            snippetFolderLink.href = folderHref;
+            snippetFolderLink.textContent = 'Open snippet folder';
+            snippetFolderLink.removeAttribute('aria-disabled');
+        } else {
+            snippetFolderLink.href = '#';
+            snippetFolderLink.textContent = 'Snippet folder unavailable';
+            snippetFolderLink.setAttribute('aria-disabled', 'true');
+        }
+    }
+
+    if (readmeHint) {
+        readmeHint.textContent = readmeHintText;
+    }
+
+    if (readmeContent) {
+        readmeContent.innerHTML = readmeHtml;
+    }
+};
+
+const loadSnippet = async (problemId) => {
+    if (!problemId) {
+        setSnippetState({
+            hint: 'No problem id available for this row.',
+            code: '',
+            readmeHintText: 'README cannot be loaded without a problem id.',
+            readmeHtml: ''
+        });
+        return;
+    }
+
+    const fileName = `call_${problemId}.py`;
+    const rawUrl = `${SNIPPET_BASE_URL}/${encodeURIComponent(problemId)}/${encodeURIComponent(fileName)}`;
+    const readmeRawUrl = `${SNIPPET_BASE_URL}/${encodeURIComponent(problemId)}/README.md`;
+    const folderUrl = `${SNIPPET_TREE_BASE_URL}/${encodeURIComponent(problemId)}`;
+
+    setSnippetState({
+        path: `${problemId}/${fileName}`,
+        hint: 'Loading snippet from repository...',
+        code: '',
+        folderHref: folderUrl,
+        readmeHintText: 'Loading README.md from repository...',
+        readmeHtml: ''
+    });
+
+    if (snippetCache.has(problemId)) {
+        const cached = snippetCache.get(problemId);
+        setSnippetState({
+            path: `${problemId}/${fileName}`,
+            hint: cached.codeHint,
+            code: cached.code,
+            folderHref: folderUrl,
+            readmeHintText: cached.readmeHint,
+            readmeHtml: cached.readmeHtml
+        });
+        return;
+    }
+
+    try {
+        const [snippetResponse, readmeResponse] = await Promise.all([
+            fetch(rawUrl),
+            fetch(readmeRawUrl)
+        ]);
+
+        let code = '';
+        let codeHint = '';
+        if (snippetResponse.status === 404) {
+            codeHint = 'No snippet file found for this problem id in the code_snippets branch.';
+        } else if (!snippetResponse.ok) {
+            codeHint = `Unable to fetch snippet (HTTP ${snippetResponse.status}).`;
+        } else {
+            code = await snippetResponse.text();
+            codeHint = 'Snippet found in code_snippets branch.';
+        }
+
+        let readme = '';
+        let readmeHtml = '';
+        let readmeHintText = '';
+        if (readmeResponse.status === 404) {
+            readmeHintText = 'No README.md found in this snippet folder.';
+        } else if (!readmeResponse.ok) {
+            readmeHintText = `Unable to fetch README.md (HTTP ${readmeResponse.status}).`;
+        } else {
+            readme = await readmeResponse.text();
+            readmeHtml = await renderMarkdownToHtml(readme);
+            readmeHintText = 'README.md loaded from snippet folder.';
+        }
+
+        snippetCache.set(problemId, {
+            code,
+            codeHint,
+            readmeHint: readmeHintText,
+            readmeHtml
+        });
+
+        setSnippetState({
+            path: `${problemId}/${fileName}`,
+            hint: codeHint,
+            code,
+            folderHref: folderUrl,
+            readmeHintText,
+            readmeHtml
+        });
+    } catch (error) {
+        setSnippetState({
+            path: `${problemId}/${fileName}`,
+            hint: 'Network error while fetching snippet.',
+            code: '',
+            folderHref: folderUrl,
+            readmeHintText: 'Network error while fetching README.md.',
+            readmeHtml: ''
+        });
+    }
+};
+
 const renderRowDetails = (rowApi) => {
     const rowData = rowApi.data();
     if (!rowData || !detailsContent) {
@@ -140,6 +341,10 @@
 
         targetRow.classList.add('is-active');
         renderRowDetails(rowApi);
+
+        const name = stripHtml(rowApi.data()[0]);
+        const problemId = targetRow.getAttribute('data-problem-id') || toProblemId(name);
+        loadSnippet(problemId);
     });
 }
 
diff --git a/docs/problems.html b/docs/problems.html
index da33017..9869979 100644
--- a/docs/problems.html
+++ b/docs/problems.html
@@ -29,7 +29,7 @@
     </tr>
   </thead>
   <tbody>
-    <tr>
+    <tr data-problem-id="bbob">
       <td>BBOB</td>
       <td></td>
       <td>suite</td>
@@ -44,7 +44,7 @@
       <td><a href="https://doi.org/10.1080/10556788.2020.1808977">https://doi.org/10.1080/10556788.2020.1808977</a></td>
       <td><a href="https://github.com/numbbo/coco">https://github.com/numbbo/coco</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="bbob_biobj">
       <td>BBOB-biobj</td>
       <td></td>
       <td>suite</td>
@@ -59,7 +59,7 @@
       <td><a href="https://doi.org/10.48550/arXiv.1604.00359">https://doi.org/10.48550/arXiv.1604.00359</a></td>
       <td><a href="https://github.com/numbbo/coco">https://github.com/numbbo/coco</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="bbob_noisy">
       <td>BBOB-noisy</td>
       <td></td>
       <td>suite</td>
@@ -74,7 +74,7 @@
       <td><a href="https://hal.inria.fr/inria-00369466">https://hal.inria.fr/inria-00369466</a></td>
       <td><a href="https://web.archive.org/web/20210416065610/https://coco.gforge.inria.fr/doku.php?id=downloads">https://web.archive.org/web/20210416065610/https://coco.gforge.inria.fr/doku.php?id=downloads</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="bbob_largescale">
       <td>BBOB-largescale</td>
       <td></td>
       <td>suite</td>
@@ -89,7 +89,7 @@
       <td><a href="https://doi.org/10.48550/arXiv.1903.06396">https://doi.org/10.48550/arXiv.1903.06396</a></td>
       <td><a href="https://github.com/numbbo/coco">https://github.com/numbbo/coco</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="bbob_mixint">
       <td>BBOB-mixint</td>
       <td></td>
       <td>suite</td>
@@ -104,7 +104,7 @@
       <td><a href="https://doi.org/10.1145/3321707.3321868">https://doi.org/10.1145/3321707.3321868</a></td>
       <td><a href="https://github.com/numbbo/coco">https://github.com/numbbo/coco</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="bbob_biobj_mixint">
       <td>BBOB-biobj-mixint</td>
       <td></td>
       <td>suite</td>
@@ -119,7 +119,7 @@
       <td><a href="https://doi.org/10.1145/3321707.3321868">https://doi.org/10.1145/3321707.3321868</a></td>
       <td><a href="https://github.com/numbbo/coco">https://github.com/numbbo/coco</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="bbob_constrained">
       <td>BBOB-constrained</td>
       <td></td>
       <td>suite</td>
@@ -134,7 +134,7 @@
       <td><a href="http://numbbo.github.io/coco-doc/bbob-constrained/">http://numbbo.github.io/coco-doc/bbob-constrained/</a></td>
       <td><a href="https://github.com/numbbo/coco">https://github.com/numbbo/coco</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="morepo">
       <td>MOrepo</td>
       <td></td>
       <td>suite</td>
@@ -149,7 +149,7 @@
       <td></td>
       <td><a href="https://github.com/MCDMSociety/MOrepo">https://github.com/MCDMSociety/MOrepo</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="zdt">
       <td>ZDT</td>
       <td></td>
       <td>suite</td>
@@ -164,7 +164,7 @@
       <td><a href="https://doi.org/10.1162/106365600568202">https://doi.org/10.1162/106365600568202</a></td>
       <td><a href="https://github.com/anyoptimization/pymoo">https://github.com/anyoptimization/pymoo</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="dtlz">
       <td>DTLZ</td>
       <td></td>
       <td>suite</td>
@@ -179,7 +179,7 @@
       <td><a href="https://doi.org/10.1109/CEC.2002.1007032">https://doi.org/10.1109/CEC.2002.1007032</a></td>
       <td><a href="https://pymoo.org/problems/many/dtlz.html">https://pymoo.org/problems/many/dtlz.html</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="wfg">
       <td>WFG</td>
       <td></td>
       <td>suite</td>
@@ -194,7 +194,7 @@
       <td><a href="https://doi.org/10.1109/TEVC.2005.861417">https://doi.org/10.1109/TEVC.2005.861417</a></td>
       <td><a href="https://pymoo.org/problems/many/wfg.html">https://pymoo.org/problems/many/wfg.html</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="cdmp">
       <td>CDMP</td>
       <td></td>
       <td>suite</td>
@@ -209,7 +209,7 @@
       <td><a href="https://doi.org/10.1145/3321707.3321878">https://doi.org/10.1145/3321707.3321878</a></td>
       <td>?</td>
     </tr>
-    <tr>
+    <tr data-problem-id="sdp">
       <td>SDP</td>
       <td></td>
       <td>suite</td>
@@ -224,7 +224,7 @@
       <td><a href="https://doi.org/10.1109/TCYB.2019.2896021">https://doi.org/10.1109/TCYB.2019.2896021</a></td>
       <td>?</td>
     </tr>
-    <tr>
+    <tr data-problem-id="maop">
       <td>MaOP</td>
       <td></td>
       <td>suite</td>
@@ -239,7 +239,7 @@
       <td><a href="https://doi.org/10.1016/j.swevo.2019.02.003">https://doi.org/10.1016/j.swevo.2019.02.003</a></td>
       <td>?</td>
     </tr>
-    <tr>
+    <tr data-problem-id="bp">
       <td>BP</td>
       <td></td>
       <td>suite</td>
@@ -254,7 +254,7 @@
       <td><a href="https://doi.org/10.1109/CEC.2019.8790277">https://doi.org/10.1109/CEC.2019.8790277</a></td>
       <td>?</td>
     </tr>
-    <tr>
+    <tr data-problem-id="gpd">
       <td>GPD</td>
       <td></td>
       <td>generator</td>
@@ -269,7 +269,7 @@
       <td><a href="https://doi.org/10.1016/j.asoc.2020.106139">https://doi.org/10.1016/j.asoc.2020.106139</a></td>
       <td>?</td>
     </tr>
-    <tr>
+    <tr data-problem-id="etmof">
       <td>ETMOF</td>
       <td></td>
       <td>suite</td>
@@ -284,7 +284,7 @@
       <td><a href="https://doi.org/10.48550/arXiv.2110.08033">https://doi.org/10.48550/arXiv.2110.08033</a></td>
       <td><a href="https://github.com/songbai-liu/etmo">https://github.com/songbai-liu/etmo</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="mmopp">
       <td>MMOPP</td>
       <td></td>
       <td>suite</td>
@@ -299,7 +299,7 @@
       <td><a href="http://www5.zzu.edu.cn/system/_content/download.jsp?urltype=news.DownloadAttachUrl&owner=1327567121&wbfileid=4764412">http://www5.zzu.edu.cn/system/_content/download.jsp?urltype=news.DownloadAttachUrl&owner=1327567121&wbfileid=4764412</a></td>
       <td><a href="http://www5.zzu.edu.cn/ecilab/info/1036/1251.htm">http://www5.zzu.edu.cn/ecilab/info/1036/1251.htm</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="cfd">
       <td>CFD</td>
       <td>expensive evaluations 30s-15m</td>
       <td>suite</td>
@@ -314,7 +314,7 @@
       <td><a href="https://doi.org/10.1007/978-3-319-99259-4_24">https://doi.org/10.1007/978-3-319-99259-4_24</a></td>
       <td><a href="https://bitbucket.org/arahat/cfd-test-problem-suite">https://bitbucket.org/arahat/cfd-test-problem-suite</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="gbea">
       <td>GBEA</td>
       <td>expensive evaluations 5s-35s, RW-GAN-Mario and TopTrumps are part of GBEA</td>
       <td>suite</td>
@@ -329,7 +329,7 @@
       <td><a href="https://doi.org/10.1145/3321707.3321805">https://doi.org/10.1145/3321707.3321805</a></td>
       <td><a href="https://github.com/ttusar/coco-gbea">https://github.com/ttusar/coco-gbea</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="car_structure">
       <td>Car structure</td>
       <td>54 constraints</td>
       <td>suite</td>
@@ -344,7 +344,7 @@
       <td><a href="https://doi.org/10.1145/3205651.3205702">https://doi.org/10.1145/3205651.3205702</a></td>
       <td><a href="http://ladse.eng.isas.jaxa.jp/benchmark/">http://ladse.eng.isas.jaxa.jp/benchmark/</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="emo2017">
       <td>EMO2017</td>
       <td></td>
       <td>suite</td>
@@ -359,7 +359,7 @@
       <td><a href="https://www.ini.rub.de/PEOPLE/glasmtbl/projects/bbcomp/">https://www.ini.rub.de/PEOPLE/glasmtbl/projects/bbcomp/</a></td>
       <td><a href="https://www.ini.rub.de/PEOPLE/glasmtbl/projects/bbcomp/downloads/realworld-problems-bbcomp-EMO-2017.zip">https://www.ini.rub.de/PEOPLE/glasmtbl/projects/bbcomp/downloads/realworld-problems-bbcomp-EMO-2017.zip</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="jsec2019">
       <td>JSEC2019</td>
       <td>expensive evaluations 3s; 22 constraints</td>
       <td>single</td>
@@ -374,7 +374,7 @@
       <td><a href="http://www.jpnsec.org/files/competition2019/EC-Symposium-2019-Competition-English.html">http://www.jpnsec.org/files/competition2019/EC-Symposium-2019-Competition-English.html</a></td>
       <td><a href="http://www.jpnsec.org/files/competition2019/EC-Symposium-2019-Competition-English.html">http://www.jpnsec.org/files/competition2019/EC-Symposium-2019-Competition-English.html</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="re">
       <td>RE</td>
       <td></td>
       <td>suite</td>
@@ -389,7 +389,7 @@
       <td><a href="https://doi.org/10.1016/j.asoc.2020.106078">https://doi.org/10.1016/j.asoc.2020.106078</a></td>
       <td><a href="https://github.com/ryojitanabe/reproblems">https://github.com/ryojitanabe/reproblems</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="cre">
       <td>CRE</td>
       <td></td>
       <td>suite</td>
@@ -404,7 +404,7 @@
       <td><a href="https://doi.org/10.1016/j.asoc.2020.106078">https://doi.org/10.1016/j.asoc.2020.106078</a></td>
       <td><a href="https://github.com/ryojitanabe/reproblems">https://github.com/ryojitanabe/reproblems</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="radar_waveform">
       <td>Radar waveform</td>
       <td></td>
       <td>single</td>
@@ -419,7 +419,7 @@
       <td><a href="https://doi.org/10.1007/978-3-540-70928-2_53">https://doi.org/10.1007/978-3-540-70928-2_53</a></td>
       <td><a href="http://code.evanhughes.org/">http://code.evanhughes.org/</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="mf2">
       <td>MF2</td>
       <td></td>
       <td>suite</td>
@@ -434,7 +434,7 @@
       <td><a href="https://doi.org/10.21105/joss.02049">https://doi.org/10.21105/joss.02049</a></td>
       <td><a href="https://github.com/sjvrijn/mf2">https://github.com/sjvrijn/mf2</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="amvop">
       <td>AMVOP</td>
       <td></td>
       <td>suite</td>
@@ -449,7 +449,7 @@
       <td><a href="https://doi.org/10.1109/TEVC.2013.2281531">https://doi.org/10.1109/TEVC.2013.2281531</a></td>
       <td>?</td>
     </tr>
-    <tr>
+    <tr data-problem-id="rwmvop">
       <td>RWMVOP</td>
       <td></td>
       <td>suite</td>
@@ -464,7 +464,7 @@
       <td><a href="https://doi.org/10.1109/TEVC.2013.2281531">https://doi.org/10.1109/TEVC.2013.2281531</a></td>
       <td>?</td>
     </tr>
-    <tr>
+    <tr data-problem-id="sbox_cost">
       <td>SBOX-COST</td>
       <td>problems from BBOB but allows instances with the optimum close to the boundary</td>
       <td>suite</td>
@@ -479,7 +479,7 @@
       <td><a href="https://doi.org/10.48550/arXiv.2305.12221">https://doi.org/10.48550/arXiv.2305.12221</a></td>
       <td><a href="https://github.com/IOHprofiler/IOHexperimenter/">https://github.com/IOHprofiler/IOHexperimenter/</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="mnk_landscapes">
       <td>ρMNK-Landscapes</td>
       <td>tunable variable and objective dimensions; tunable multimodality and correlation between objectives</td>
       <td>generator</td>
@@ -494,7 +494,7 @@
       <td><a href="https://doi.org/10.1016/j.ejor.2012.12.019">https://doi.org/10.1016/j.ejor.2012.12.019</a></td>
       <td><a href="https://gitlab.com/aliefooghe/mocobench/">https://gitlab.com/aliefooghe/mocobench/</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="mubqp">
       <td>mUBQP</td>
       <td>tunable variable and objective dimensions; tunable density and correlation between objectives</td>
       <td>generator</td>
@@ -509,7 +509,7 @@
       <td><a href="https://doi.org/10.1016/j.asoc.2013.11.008">https://doi.org/10.1016/j.asoc.2013.11.008</a></td>
       <td><a href="https://gitlab.com/aliefooghe/mocobench/">https://gitlab.com/aliefooghe/mocobench/</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="mtsp">
       <td>ρmTSP</td>
       <td>tunable variable and objective dimensions; tunable instance type (euclidian/random); tunable correlation between objectives</td>
       <td>generator</td>
@@ -524,7 +524,7 @@
       <td><a href="https://doi.org/10.1007/978-3-319-45823-6_40">https://doi.org/10.1007/978-3-319-45823-6_40</a></td>
       <td><a href="https://gitlab.com/aliefooghe/mocobench/">https://gitlab.com/aliefooghe/mocobench/</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="cec2015_dmoo">
       <td>CEC2015-DMOO</td>
       <td></td>
       <td>suite</td>
@@ -539,7 +539,7 @@
       <td>Benchmark Functions for CEC 2015 Special Session and Competition on Dynamic Multi-objective Optimization</td>
       <td></td>
     </tr>
-    <tr>
+    <tr data-problem-id="ealain">
       <td>Ealain</td>
       <td>Real-world-like, easily extensible to increase complexity</td>
       <td>generator</td>
@@ -554,7 +554,7 @@
       <td><a href="https://doi.org/10.1145/3638530.3654299">https://doi.org/10.1145/3638530.3654299</a></td>
       <td><a href="https://github.com/qrenau/Ealain">https://github.com/qrenau/Ealain</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="ma_bbob">
       <td>MA-BBOB</td>
       <td>Generator that creates affine combinations of BBOB functions</td>
       <td>generator</td>
@@ -569,7 +569,7 @@
       <td><a href="https://doi.org/10.1145/3673908">https://doi.org/10.1145/3673908</a></td>
       <td><a href="https://github.com/IOHprofiler/IOHexperimenter/blob/master/example/Competitions/MA-BBOB/Example_MABBOB.ipynb">https://github.com/IOHprofiler/IOHexperimenter/blob/master/example/Competitions/MA-BBOB/Example_MABBOB.ipynb</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="mpm2">
       <td>MPM2</td>
       <td>nonlinear nonseparable nonsymmetric; scalable in terms of time to evaluate the objective function</td>
       <td>generator</td>
@@ -584,7 +584,7 @@
       <td><a href="https://ls11-www.cs.tu-dortmund.de/_media/techreports/tr15-01.pdf">https://ls11-www.cs.tu-dortmund.de/_media/techreports/tr15-01.pdf</a></td>
       <td><a href="https://github.com/jakobbossek/smoof/blob/master/inst/mpm2.py">https://github.com/jakobbossek/smoof/blob/master/inst/mpm2.py</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="convex_dtlz2">
       <td>Convex DTLZ2</td>
       <td>Variant of DTLZ2 with a convex Pareto front (instead of concave)</td>
       <td>single</td>
@@ -599,7 +599,7 @@
       <td><a href="https://doi.org/10.1109/TEVC.2013.2281535">https://doi.org/10.1109/TEVC.2013.2281535</a></td>
       <td>?</td>
     </tr>
-    <tr>
+    <tr data-problem-id="inverted_dtlz1">
       <td>Inverted DTLZ1</td>
       <td>Variant of DTLZ1 with an inverted Pareto front</td>
       <td>single</td>
@@ -614,7 +614,7 @@
       <td><a href="https://doi.org/10.1109/TEVC.2013.2281534">https://doi.org/10.1109/TEVC.2013.2281534</a></td>
       <td>?</td>
     </tr>
-    <tr>
+    <tr data-problem-id="minus_dtlz">
       <td>Minus DTLZ</td>
       <td>Variant of DTLZ that minimises the inverse of the base DTLZ functions</td>
       <td>suite</td>
@@ -629,7 +629,7 @@
       <td><a href="https://doi.org/10.1109/TEVC.2016.2587749">https://doi.org/10.1109/TEVC.2016.2587749</a></td>
       <td>?</td>
     </tr>
-    <tr>
+    <tr data-problem-id="minus_wfg">
       <td>Minus WFG</td>
       <td>Variant of WFG that minimises the inverse of the base WFG functions</td>
       <td>suite</td>
@@ -644,7 +644,7 @@
       <td><a href="https://doi.org/10.1109/TEVC.2016.2587749">https://doi.org/10.1109/TEVC.2016.2587749</a></td>
       <td>?</td>
     </tr>
-    <tr>
+    <tr data-problem-id="l1_zdt">
       <td>L1-ZDT</td>
       <td>Variant of ZDT with linkages between variables within one of two groups but not between variables in a different group; Linear recombination operators can potentially take advantage of the problem structure</td>
       <td>suite</td>
@@ -659,7 +659,7 @@
       <td><a href="https://doi.org/10.1145/1143997.1144179">https://doi.org/10.1145/1143997.1144179</a></td>
       <td>?</td>
     </tr>
-    <tr>
+    <tr data-problem-id="l2_zdt">
       <td>L2-ZDT</td>
       <td>Variant of ZDT with linkages between all variables; Linear recombination operators can potentially take advantage of the problem structure</td>
       <td>suite</td>
@@ -674,7 +674,7 @@
       <td><a href="https://doi.org/10.1145/1143997.1144179">https://doi.org/10.1145/1143997.1144179</a></td>
       <td>?</td>
     </tr>
-    <tr>
+    <tr data-problem-id="l3_zdt">
       <td>L3-ZDT</td>
       <td>Variant of L2-ZDT using a mapping to prevent linear recombination operators from potentially taking advantage of the problem structure</td>
       <td>suite</td>
@@ -689,7 +689,7 @@
       <td><a href="https://doi.org/10.1145/1143997.1144179">https://doi.org/10.1145/1143997.1144179</a></td>
       <td>?</td>
     </tr>
-    <tr>
+    <tr data-problem-id="l2_dtlz">
       <td>L2-DTLZ</td>
       <td>Variant of DTLZ2 and DTLZ3 with linkages between all variables; Linear recombination operators can potentially take advantage of the problem structure</td>
       <td>suite</td>
@@ -704,7 +704,7 @@
       <td><a href="https://doi.org/10.1145/1143997.1144179">https://doi.org/10.1145/1143997.1144179</a></td>
       <td>?</td>
     </tr>
-    <tr>
+    <tr data-problem-id="l3_dtlz">
       <td>L3-DTLZ</td>
       <td>Variant of L2-DTLZ using a mapping to prevent linear recombination operators from potentially taking advantage of the problem structure</td>
       <td>suite</td>
@@ -719,7 +719,7 @@
       <td><a href="https://doi.org/10.1145/1143997.1144179">https://doi.org/10.1145/1143997.1144179</a></td>
       <td>?</td>
     </tr>
-    <tr>
+    <tr data-problem-id="cec2018_dt_cec2018_competition_on_dynamic_multiobjective_optimisation">
       <td>CEC2018 DT - CEC2018 Competition on Dynamic Multiobjective Optimisation</td>
       <td>14 problems. Time-dependent: Pareto front/Pareto set geometry; irregular Pareto front shapes; variable-linkage; number of disconnected Pareto front segments; etc.</td>
       <td>suite</td>
@@ -734,7 +734,7 @@
       <td><a href="https://www.academia.edu/download/94499025/TR-CEC2018-DMOP-Competition.pdf">https://www.academia.edu/download/94499025/TR-CEC2018-DMOP-Competition.pdf</a></td>
       <td><a href="https://pymoo.org/problems/dynamic/df.html">https://pymoo.org/problems/dynamic/df.html</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="modact_multiobjective_design_of_actuators">
       <td>MODAct - multiobjective design of actuators</td>
       <td>Realistic Constrained Multi-Objective Optimization Benchmark Problems from Design. Need the <a href="https://github.com/epfl-lamd/modact">https://github.com/epfl-lamd/modact</a> package installed; evaluation times around 20ms</td>
       <td>suite</td>
@@ -749,7 +749,7 @@
       <td><a href="https://doi.org/10.1109/TEVC.2020.3020046">https://doi.org/10.1109/TEVC.2020.3020046</a></td>
       <td><a href="https://pymoo.org/problems/constrained/modact.html">https://pymoo.org/problems/constrained/modact.html</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="iohclustering">
       <td>IOHClustering</td>
       <td>Set of benchmark problems from clustering: optimization task is selecting cluster centers for a given set of data, with the number of clusters defining problem dimensionality. Includes both a suite and a generator. Based on ML clustering datasets</td>
       <td>suite; generator</td>
@@ -764,7 +764,7 @@
       <td><a href="https://arxiv.org/pdf/2505.09233">https://arxiv.org/pdf/2505.09233</a></td>
       <td><a href="https://github.com/IOHprofiler/IOHClustering">https://github.com/IOHprofiler/IOHClustering</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="gnbg_ii">
       <td>GNBG-II</td>
       <td>Generalized Numerical Benchmark Generator (version 2). Also in IOH <a href="https://github.com/IOHprofiler/IOHGNBG">https://github.com/IOHprofiler/IOHGNBG</a></td>
       <td>suite; generator</td>
@@ -779,7 +779,7 @@
       <td><a href="https://dl.acm.org/doi/pdf/10.1145/3712255.3734271">https://dl.acm.org/doi/pdf/10.1145/3712255.3734271</a></td>
       <td><a href="https://github.com/rohitsalgotra/GNBG-II">https://github.com/rohitsalgotra/GNBG-II</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="gnbg">
       <td>GNBG</td>
       <td>Generalized Numerical Benchmark Generator</td>
       <td>suite; generator</td>
@@ -794,7 +794,7 @@
       <td><a href="https://arxiv.org/abs/2312.07083">https://arxiv.org/abs/2312.07083</a></td>
       <td><a href="https://github.com/Danial-Yazdani/GNBG-Generator">https://github.com/Danial-Yazdani/GNBG-Generator</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="dynamicbinval">
       <td>DynamicBinVal</td>
       <td>Four versions of the dynamic binary value problem</td>
       <td>suite</td>
@@ -809,7 +809,7 @@
       <td><a href="https://arxiv.org/pdf/2404.15837">https://arxiv.org/pdf/2404.15837</a></td>
       <td><a href="https://github.com/IOHprofiler/IOHexperimenter">https://github.com/IOHprofiler/IOHexperimenter</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="pbo">
       <td>PBO</td>
       <td>Suite of 25 binary optimization problems</td>
       <td>suite</td>
@@ -824,7 +824,7 @@
       <td><a href="https://dl.acm.org/doi/pdf/10.1145/3319619.3326810">https://dl.acm.org/doi/pdf/10.1145/3319619.3326810</a></td>
       <td><a href="https://github.com/IOHprofiler/IOHexperimenter">https://github.com/IOHprofiler/IOHexperimenter</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="w_model">
       <td>W-model</td>
       <td>Tunable generator for binary optimization based on several difficulty features</td>
       <td>generator</td>
@@ -839,7 +839,7 @@
       <td><a href="https://dl.acm.org/doi/abs/10.1145/3205651.3208240?casa_token=S4U_Pi9f6MwAAAAA:U9ztNTPwmupT8K3GamWZfBL7-8fqjxPtr_kprv51vdwA-REsp0EyOFGa99BtbANb0XbqyrVg795hIw">https://dl.acm.org/doi/abs/10.1145/3205651.3208240?casa_token=S4U_Pi9f6MwAAAAA:U9ztNTPwmupT8K3GamWZfBL7-8fqjxPtr_kprv51vdwA-REsp0EyOFGa99BtbANb0XbqyrVg795hIw</a></td>
       <td><a href="https://github.com/thomasWeise/BBDOB_W_Model">https://github.com/thomasWeise/BBDOB_W_Model</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="submodular_optimitzation">
       <td>Submodular Optimitzation</td>
       <td>set of graph-based submodular optimization problems from 4 problem types</td>
       <td>suite</td>
@@ -854,7 +854,7 @@
       <td><a href="https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10254181">https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10254181</a></td>
       <td><a href="https://github.com/IOHprofiler/IOHexperimenter">https://github.com/IOHprofiler/IOHexperimenter</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="cec2013">
       <td>CEC2013</td>
       <td>suite used for cec2013 competition. Also in IOH <a href="https://github.com/IOHprofiler/IOHexperimenter">https://github.com/IOHprofiler/IOHexperimenter</a></td>
       <td>suite</td>
@@ -869,7 +869,7 @@
       <td><a href="https://peerj.com/articles/cs-2671/CEC2013.pdf">https://peerj.com/articles/cs-2671/CEC2013.pdf</a></td>
       <td><a href="https://github.com/P-N-Suganthan/CEC2013">https://github.com/P-N-Suganthan/CEC2013</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="cec2022">
       <td>CEC2022</td>
       <td>suite used for cec2022 competition. Also in IOH <a href="https://github.com/IOHprofiler/IOHexperimenter">https://github.com/IOHprofiler/IOHexperimenter</a></td>
       <td>suite</td>
@@ -884,7 +884,7 @@
       <td><a href="https://github.com/P-N-Suganthan/2022-SO-BO/blob/main/CEC2022%20TR.pdf">https://github.com/P-N-Suganthan/2022-SO-BO/blob/main/CEC2022%20TR.pdf</a></td>
       <td><a href="https://github.com/P-N-Suganthan/2022-SO-BO">https://github.com/P-N-Suganthan/2022-SO-BO</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="onemaxsphere_zeromaxsphere">
       <td>Onemax+Sphere / Zeromax+Sphere</td>
       <td></td>
       <td>single</td>
@@ -899,7 +899,7 @@
       <td><a href="https://doi.org/10.1145/3449726.3459521">https://doi.org/10.1145/3449726.3459521</a></td>
       <td>None</td>
     </tr>
-    <tr>
+    <tr data-problem-id="onemaxsphere_deceptivetraprotatedellipsoid">
       <td>Onemax+Sphere / DeceptiveTrap+RotatedEllipsoid</td>
       <td></td>
       <td>single</td>
@@ -914,7 +914,7 @@
       <td><a href="https://doi.org/10.1145/3449726.3459521">https://doi.org/10.1145/3449726.3459521</a></td>
       <td>None</td>
     </tr>
-    <tr>
+    <tr data-problem-id="inversedeceptivetraprotatedellipsoid_deceptivetraprotatedellipsoid">
       <td>InverseDeceptiveTrap+RotatedEllipsoid / DeceptiveTrap+RotatedEllipsoid</td>
       <td></td>
       <td>single</td>
@@ -929,7 +929,7 @@
       <td><a href="https://doi.org/10.1145/3449726.3459521">https://doi.org/10.1145/3449726.3459521</a></td>
       <td>None</td>
     </tr>
-    <tr>
+    <tr data-problem-id="porkchopplotinterplanetarytrajectory">
       <td>PorkchopPlotInterplanetaryTrajectory</td>
       <td></td>
       <td>suite</td>
@@ -944,7 +944,7 @@
       <td><a href="https://doi.org/10.1109/CEC65147.2025.11042973">https://doi.org/10.1109/CEC65147.2025.11042973</a></td>
       <td><a href="https://github.com/ShuaiqunPan/Transfer_Random_forests_BBOB_Real_world">https://github.com/ShuaiqunPan/Transfer_Random_forests_BBOB_Real_world</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="kinematicsrobotarm">
       <td>KinematicsRobotArm</td>
       <td></td>
       <td>suite</td>
@@ -959,7 +959,7 @@
       <td><a href="https://doi.org/10.1023/A:1013258808932">https://doi.org/10.1023/A:1013258808932</a></td>
       <td><a href="https://github.com/ShuaiqunPan/Transfer_Random_forests_BBOB_Real_world">https://github.com/ShuaiqunPan/Transfer_Random_forests_BBOB_Real_world</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="vehicledynamics">
       <td>VehicleDynamics</td>
       <td></td>
       <td>suite</td>
@@ -974,7 +974,7 @@
       <td><a href="https://www.scitepress.org/Papers/2023/121580/121580.pdf">https://www.scitepress.org/Papers/2023/121580/121580.pdf</a></td>
       <td><a href="https://zenodo.org/records/8307853">https://zenodo.org/records/8307853</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="mechbench">
       <td>MECHBench</td>
       <td>This is a set of problems with inspiration from Structural Mechanics Design Optimization. The suite comprises three physical models, from which the user may define different kind of problems which impact the final design output.</td>
       <td>Problem Suite</td>
@@ -989,7 +989,7 @@
       <td><a href="https://arxiv.org/abs/2511.10821">https://arxiv.org/abs/2511.10821</a></td>
       <td><a href="https://github.com/BayesOptApp/MECHBench">https://github.com/BayesOptApp/MECHBench</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="expobench">
       <td>EXPObench</td>
       <td>Wind farm layout optimization, gas filter design, pipe shape optimization, hyperparameter tuning, and hospital simulation</td>
       <td>Problem Suite</td>
@@ -1004,7 +1004,7 @@
       <td><a href="https://doi.org/10.1016/j.asoc.2023.110744">https://doi.org/10.1016/j.asoc.2023.110744</a></td>
       <td><a href="https://github.com/AlgTUDelft/ExpensiveOptimBenchmark">https://github.com/AlgTUDelft/ExpensiveOptimBenchmark</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="gasoline_direct_injection_engine_design">
       <td>Gasoline direct injection engine design</td>
       <td>A multi-objective optimization problem seeking to minimize fuel consumption and NOx emissions over a two-minute dynamic duty cycle, subject to five constraints (turbine inlet temperature, number of knock occurrences, peak cylinder pressure, peak cylinder pressure rise, total work). Seven decision variables are defined: four define the hardware choices of cylinder compression ratio, turbo machinery and EGR cooler sizing; three relate to control variables that parameterise the engine control logic.</td>
       <td>Single Problem</td>
@@ -1019,7 +1019,7 @@
       <td></td>
       <td><a href="https://doi.org/10.1016/j.ejor.2022.08.032">https://doi.org/10.1016/j.ejor.2022.08.032</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="beacon">
       <td>BEACON</td>
       <td>Generator for bi-objective benchmark problems with explicitly controlled correlations in continuous spaces.</td>
       <td>Generator</td>
@@ -1034,7 +1034,7 @@
       <td><a href="https://dl.acm.org/doi/10.1145/3712255.3734303">https://dl.acm.org/doi/10.1145/3712255.3734303</a></td>
       <td><a href="https://github.com/Stebbet/BEACON/">https://github.com/Stebbet/BEACON/</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="tulipaenergy">
       <td>TulipaEnergy</td>
       <td>Determine the optimal investment and operation decisions  for different types of assets in the energy system (production, consumption, conversion, storage, and transport), while minimizing loss of load.</td>
       <td>Problem Suite</td>
@@ -1049,7 +1049,7 @@
       <td>See <a href="https://tulipaenergy.github.io/TulipaEnergyModel.jl/stable/40-scientific-foundation/45-scientific-references">https://tulipaenergy.github.io/TulipaEnergyModel.jl/stable/40-scientific-foundation/45-scientific-references</a></td>
       <td><a href="https://tulipaenergy.github.io/TulipaEnergyModel.jl/stable/">https://tulipaenergy.github.io/TulipaEnergyModel.jl/stable/</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="ato">
       <td>ATO</td>
       <td>Parameters of the Modules of the Automatic Train Operation should be optimized. The parameters are continuous with different ranges. There are two objectives (minimizing energy consumption, minimizing driving duration.</td>
       <td>Single Problem</td>
@@ -1064,7 +1064,7 @@
       <td></td>
       <td>-</td>
     </tr>
-    <tr>
+    <tr data-problem-id="brachytherapy_treatment_planning">
       <td>Brachytherapy treatment planning</td>
       <td>Treatment planning for internal radiation therapy</td>
       <td>Problem Suite</td>
@@ -1079,7 +1079,7 @@
       <td><a href="https://www.sciencedirect.com/science/article/pii/S1538472123016781">https://www.sciencedirect.com/science/article/pii/S1538472123016781</a></td>
       <td></td>
     </tr>
-    <tr>
+    <tr data-problem-id="fleetopt">
       <td>FleetOpt</td>
       <td>Healthcare organisation in the UK provided data about their current fleet of vehicles to conduct non-emergency heathcare trips in the Argyll and Bute region of Scotland, UK. They also provided historical data about the trips the vehicles took and about the bases which the vehicles return to. The aim is to reduce the existing fleet of vehicles while still ensuring all trips can be covered. Moving a vehicle from one base to another to help cover trips is OK as long as the original base can still cover its trips. Link to paper with more details: <a href="https://dl.acm.org/doi/abs/10.1145/3638530.3664137">https://dl.acm.org/doi/abs/10.1145/3638530.3664137</a></td>
       <td>Single Problem</td>
@@ -1094,7 +1094,7 @@
       <td><a href="https://dl.acm.org/doi/abs/10.1145/3638530.3664137">https://dl.acm.org/doi/abs/10.1145/3638530.3664137</a></td>
       <td>Not public: was done for real client with their private data</td>
     </tr>
-    <tr>
+    <tr data-problem-id="building_spatial_design">
       <td>Building spatial design</td>
       <td>Optimise the spatial layout of a building to: minimise energy consumption for climate control, and minimise the strain on the structure</td>
       <td>Single Problem</td>
@@ -1109,7 +1109,7 @@
       <td><a href="https://hdl.handle.net/1887/81789">https://hdl.handle.net/1887/81789</a></td>
       <td><a href="https://github.com/TUe-excellent-buildings/BSO-toolbox">https://github.com/TUe-excellent-buildings/BSO-toolbox</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="electric_motor_design_optimization">
       <td>Electric Motor Design Optimization</td>
       <td>The goal is to find a design of a synchronous electric motor for power steering systems that minimizes costs and satisfies all constraints.</td>
       <td>Single Problem</td>
@@ -1124,7 +1124,7 @@
       <td><a href="https://dis.ijs.si/tea/Publications/Tusar23Multistep.pdf">https://dis.ijs.si/tea/Publications/Tusar23Multistep.pdf</a> (paper in Slovene)</td>
       <td>Implementation not freely available</td>
     </tr>
-    <tr>
+    <tr data-problem-id="bono_bench">
       <td>BONO-Bench</td>
       <td>Bi-objective problem generator and suite with scalable continuous decision space. Features complex problem properties (different types of multimodality and challenges in decision and objective space) as well as Pareto front approximations with error guarantees for the hypervolume and exact R2 indicators.</td>
       <td>Generator</td>
@@ -1139,7 +1139,7 @@
       <td></td>
       <td><a href="https://github.com/schaepermeier/bonobench">https://github.com/schaepermeier/bonobench</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="randoptgen">
       <td>RandOptGen</td>
       <td>RandOptGen: A Unified Random Problem Generator for Single-and Multi-Objective Optimization Problems with Mixed-Variable Input Spaces</td>
       <td>Generator</td>
@@ -1154,7 +1154,7 @@
       <td></td>
       <td><a href="https://github.com/MALEO-research-group/RandOptGen">https://github.com/MALEO-research-group/RandOptGen</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="cuter">
       <td>CUTEr</td>
       <td>A constrained and unconstrained testing environment</td>
       <td>Problem Suite</td>
@@ -1169,7 +1169,7 @@
       <td><a href="https://dl.acm.org/doi/10.1145/962437.962439">https://dl.acm.org/doi/10.1145/962437.962439</a></td>
       <td>Not Found</td>
     </tr>
-    <tr>
+    <tr data-problem-id="cutest">
       <td>CUTEst</td>
       <td>The Constrained and Unconstrained Testing Environment with safe threads (CUTEst) for optimization software</td>
       <td>Problem Suite</td>
@@ -1184,7 +1184,7 @@
       <td><a href="https://link.springer.com/article/10.1007/s10589-014-9687-3">https://link.springer.com/article/10.1007/s10589-014-9687-3</a></td>
       <td><a href="https://github.com/jfowkes/pycutest">https://github.com/jfowkes/pycutest</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="puboi">
       <td>PUBOi</td>
       <td>A benchmark in which variable importance is tunable, based on the Walsh function</td>
       <td>Generator</td>
@@ -1208,4 +1208,13 @@
     <h3 class="details-title">Problem details</h3>
     <p class="details-hint" id="problem-details-hint">Click a table row to inspect full details.</p>
     <dl class="details-grid" id="problem-details-content"></dl>
+    <div class="snippet-shell" id="problem-snippet-shell">
+        <h4 class="snippet-title">README.md</h4>
+        <p class="snippet-hint" id="problem-readme-hint">README content for this snippet folder will appear here.</p>
+        <div class="snippet-readme markdown-body" id="problem-readme-content"></div>
+        <h4 class="snippet-title">Snippet: <code id="problem-snippet-path">call_{problem_id}.py</code></h4>
+        <p class="snippet-meta"><a id="problem-snippet-folder-link" href="#" target="_blank" rel="noopener noreferrer">Open snippet folder</a></p>
+        <p class="snippet-hint" id="problem-snippet-hint">Select a problem to check for an available code snippet.</p>
+        <pre class="snippet-code"><code id="problem-snippet-content"></code></pre>
+    </div>
 </section>
diff --git a/docs/table_styles.css b/docs/table_styles.css
index 1c53f6d..6d3a9fd 100644
--- a/docs/table_styles.css
+++ b/docs/table_styles.css
@@ -257,6 +257,103 @@ h2 {
     font-style: italic;
 }
 
+.snippet-shell {
+    margin-top: 0.85rem;
+    border: 1px solid var(--opl-border);
+    border-radius: 10px;
+    background: #f8fbfe;
+    padding: 0.7rem;
+}
+
+.snippet-title {
+    margin: 0;
+    font-size: 0.84rem;
+    color: #23415e;
+}
+
+.snippet-hint {
+    margin: 0.4rem 0 0.55rem;
+    font-size: 0.82rem;
+    color: var(--opl-muted);
+}
+
+.snippet-meta {
+    margin: 0.35rem 0 0.45rem;
+    font-size: 0.82rem;
+}
+
+.snippet-meta a {
+    color: #0a5d74;
+    text-decoration: underline;
+}
+
+.snippet-meta a[aria-disabled="true"] {
+    color: var(--opl-muted);
+    pointer-events: none;
+    text-decoration: none;
+}
+
+.snippet-code {
+    margin: 0;
+    background: #11263b;
+    color: #e9f3ff;
+    border-radius: 8px;
+    padding: 0.7rem;
+    max-height: 320px;
+    overflow: auto;
+}
+
+.snippet-code code {
+    font-family: Consolas, "Courier New", monospace;
+    font-size: 0.8rem;
+    line-height: 1.45;
+    white-space: pre;
+}
+
+.snippet-readme {
+    margin-top: 0.35rem;
+    background: #ffffff;
+    border: 1px solid var(--opl-border);
+    border-radius: 8px;
+    padding: 0.75rem;
+    max-height: 380px;
+    overflow: auto;
+    font-size: 0.83rem;
+    line-height: 1.45;
+}
+
+.snippet-readme h1,
+.snippet-readme h2,
+.snippet-readme h3,
+.snippet-readme h4 {
+    margin: 0.7rem 0 0.45rem;
+    color: #18334e;
+}
+
+.snippet-readme p,
+.snippet-readme ul,
+.snippet-readme ol {
+    margin: 0.45rem 0;
+}
+
+.snippet-readme pre {
+    margin: 0.5rem 0;
+    background: #11263b;
+    color: #e9f3ff;
+    border-radius: 8px;
+    padding: 0.65rem;
+    overflow: auto;
+}
+
+.snippet-readme code {
+    font-family: Consolas, "Courier New", monospace;
+    font-size: 0.8rem;
+}
+
+.snippet-readme a {
+    color: #0a5d74;
+}
+
 @media (max-width: 900px) {
     body {
         margin: 0.6rem;
diff --git a/docs/table_template.html b/docs/table_template.html
index 270d61f..db77aac 100644
--- a/docs/table_template.html
+++ b/docs/table_template.html
@@ -18,4 +18,13 @@
     <h3 class="details-title">Problem details</h3>
     <p class="details-hint" id="problem-details-hint">Click a table row to inspect full details.</p>
     <dl class="details-grid" id="problem-details-content"></dl>
+    <div class="snippet-shell" id="problem-snippet-shell">
+        <h4 class="snippet-title">README.md</h4>
+        <p class="snippet-hint" id="problem-readme-hint">README content for this snippet folder will appear here.</p>
+        <div class="snippet-readme markdown-body" id="problem-readme-content"></div>
+        <h4 class="snippet-title">Snippet: <code id="problem-snippet-path">call_{problem_id}.py</code></h4>
+        <p class="snippet-meta"><a id="problem-snippet-folder-link" href="#" target="_blank" rel="noopener noreferrer">Open snippet folder</a></p>
+        <p class="snippet-hint" id="problem-snippet-hint">Select a problem to check for an available code snippet.</p>
+        <pre class="snippet-code"><code id="problem-snippet-content"></code></pre>
+    </div>
 </section>
diff --git a/yaml_to_html.py b/yaml_to_html.py
index 87e8cce..f106263 100755
--- a/yaml_to_html.py
+++ b/yaml_to_html.py
@@ -21,6 +21,32 @@ def repl(m):
 
     return URL_RE.sub(repl, value)
 
+
+def to_problem_id(value):
+    if not isinstance(value, str):
+        return ""
+
+    normalized = value.strip().lower().replace("-", "_").replace(" ", "_")
+    normalized = re.sub(r"[^a-z0-9_]", "", normalized)
+    normalized = re.sub(r"_+", "_", normalized).strip("_")
+    return normalized
+
+
+def add_problem_id_row_attributes(table_html, problem_ids):
+    marker = "<tbody>"
+    if marker not in table_html:
+        return table_html
+
+    body_start = table_html.index(marker) + len(marker)
+    prefix = table_html[:body_start]
+    body = table_html[body_start:]
+
+    for problem_id in problem_ids:
+        safe_id = escape(problem_id, quote=True)
+        body = body.replace("<tr>", f'<tr data-problem-id="{safe_id}">', 1)
+
+    return prefix + body
+
 yaml_file = "problems.yaml"
 
 html_dir = "docs/"
@@ -33,7 +59,15 @@ def repl(m):
 
 # Load data
 with open(yaml_file) as yaml_input:
-    data = pd.json_normalize(yaml.safe_load(yaml_input))
+    raw_data = pd.json_normalize(yaml.safe_load(yaml_input))
+
+if "problem_id" in raw_data.columns:
+    problem_ids = raw_data["problem_id"].fillna("").map(str)
+else:
+    problem_ids = raw_data["name"].fillna("").map(str).map(to_problem_id)
+
+problem_ids = problem_ids.where(problem_ids.str.len() > 0, raw_data["name"].fillna("").map(str).map(to_problem_id))
+data = raw_data.copy()
 
 # Choose desired columns
 all_columns = False
@@ -66,6 +100,8 @@ def repl(m):
                      border=0,
                      na_rep="")  # Leave NaN cells empty
 
+table = add_problem_id_row_attributes(table, problem_ids.tolist())
+
 # Add footer to facilitate individual column search
 idx = table.index('</table>')
 final_table = table[:idx] + "<tfoot><tr>" + " ".join(["<th>"+ i +"</th>" for i in data.columns])+"</tr> </tfoot>" + table[idx:]

From 4da69487c86bbb11cfb485ca0bf5cf2b5688959b Mon Sep 17 00:00:00 2001
From: Olaf Mersmann <olafm@p-value.net>
Date: Wed, 22 Apr 2026 13:50:16 +0200
Subject: [PATCH 6/9] chore: Convert problem.yaml to new schema

Adds an LLM generated generator for the problems defined in the old
problem.yaml to generate a new style problem.yaml. For future fixes, it is
probably best to update examples/problem.py, regenerate the yaml and then merge
it with the old yaml by replacing entries based on id. That way additional
entries added later are not clobbbered.
---
 examples/problems.py | 2995 ++++++++++++++++++++++++++
 problems.yaml        | 4754 +++++++++++++++++++++++++++++++-----------
 2 files changed, 6488 insertions(+), 1261 deletions(-)
 create mode 100644 examples/problems.py

diff --git a/examples/problems.py b/examples/problems.py
new file mode 100644
index 0000000..0e387d7
--- /dev/null
+++ b/examples/problems.py
@@ -0,0 +1,2995 @@
+"""Conversion of problems.yaml into the opltools schema.
+
+Every entry from the original YAML is preserved as a `#!` comment block
+directly above the Python object(s) it was converted into.  Where the
+original YAML carried information that does not fit into the new schema,
+a `FIXME` comment is used to flag the loss.
+
+IDs use the prefixes:
+    fn_    - single Problem
+    suite_ - Suite
+    gen_   - Generator
+    impl_  - Implementation
+"""
+
+from opltools import (
+    Library,
+    Problem,
+    Suite,
+    Generator,
+    Implementation,
+    Reference,
+    Link,
+    Variable,
+    Constraint,
+    ValueRange,
+)
+from pydantic_yaml import to_yaml_str
+
+things = {}
+
+
+# =====================================================================
+# Shared implementations (reused by multiple YAML entries).
+# =====================================================================
+
+things["impl_coco"] = Implementation(
+    name="COCO framework",
+    description="Comparing Continuous Optimizers: black-box optimization benchmarking platform",
+    language="C/Python",
+    links=[Link(type="repository", url="https://github.com/numbbo/coco")],
+)
+
+things["impl_coco_legacy"] = Implementation(
+    name="COCO legacy (bbob-noisy)",
+    description="Archived COCO download page that hosted the bbob-noisy suite",
+    language="C/Python",
+    links=[
+        Link(
+            type="archive",
+            url="https://web.archive.org/web/20210416065610/https://coco.gforge.inria.fr/doku.php?id=downloads",
+        )
+    ],
+)
+
+things["impl_iohexperimenter"] = Implementation(
+    name="IOHexperimenter",
+    description="IOHprofiler experimenter framework",
+    language="C++/Python",
+    links=[Link(type="repository", url="https://github.com/IOHprofiler/IOHexperimenter")],
+)
+
+things["impl_pymoo"] = Implementation(
+    name="pymoo",
+    description="Multi-objective optimization in Python",
+    language="Python",
+    links=[Link(type="repository", url="https://github.com/anyoptimization/pymoo")],
+)
+
+things["impl_mocobench"] = Implementation(
+    name="mocobench",
+    description="Multi-objective combinatorial optimization benchmark",
+    language="C++",
+    links=[Link(type="repository", url="https://gitlab.com/aliefooghe/mocobench/")],
+)
+
+things["impl_reproblems"] = Implementation(
+    name="reproblems",
+    description="Real-world inspired multi-objective optimization problem suite",
+    language="Python",
+    links=[Link(type="repository", url="https://github.com/ryojitanabe/reproblems")],
+)
+
+
+# =====================================================================
+# Entries
+# =====================================================================
+
+#! - name: BBOB
+#!   suite/generator/single: suite
+#!   objectives: '1'
+#!   dimensionality: scalable
+#!   variable type: continuous
+#!   constraints: 'no'
+#!   dynamic: 'no'
+#!   noise: 'no'
+#!   multimodal: 'yes'
+#!   multi-fidelity: 'no'
+#!   reference: https://doi.org/10.1080/10556788.2020.1808977
+#!   implementation: https://github.com/numbbo/coco
+#!   source (real-world/artificial): ''
+#!   textual description: ''
+things["suite_bbob"] = Suite(
+    name="BBOB",
+    objectives={1},
+    variables=[Variable(type="continuous", dim=ValueRange(min=1))],
+    modality={"multimodal"},
+    references=[
+        Reference(
+            title="COCO: a platform for comparing continuous optimizers in a black-box setting",
+            authors=[],
+            link=Link(url="https://doi.org/10.1080/10556788.2020.1808977"),
+        )
+    ],
+    implementations={"impl_coco"},
+)
+
+#! - name: BBOB-biobj
+#!   suite/generator/single: suite
+#!   objectives: '2'
+#!   dimensionality: 2-40
+#!   variable type: continuous
+#!   constraints: 'no'
+#!   dynamic: 'no'
+#!   noise: 'no'
+#!   multimodal: 'yes'
+#!   multi-fidelity: 'no'
+#!   reference: https://doi.org/10.48550/arXiv.1604.00359
+#!   implementation: https://github.com/numbbo/coco
+#!   source (real-world/artificial): ''
+#!   textual description: ''
+things["suite_bbob_biobj"] = Suite(
+    name="BBOB-biobj",
+    objectives={2},
+    variables=[Variable(type="continuous", dim=ValueRange(min=2, max=40))],
+    modality={"multimodal"},
+    references=[
+        Reference(
+            title="BBOB bi-objective test suite",
+            authors=[],
+            link=Link(url="https://doi.org/10.48550/arXiv.1604.00359"),
+        )
+    ],
+    implementations={"impl_coco"},
+)
+
+#! - name: BBOB-noisy
+#!   suite/generator/single: suite
+#!   objectives: '1'
+#!   dimensionality: scalable
+#!   variable type: continuous
+#!   constraints: 'no'
+#!   dynamic: 'no'
+#!   noise: 'yes'
+#!   multimodal: 'yes'
+#!   multi-fidelity: 'no'
+#!   reference: https://hal.inria.fr/inria-00369466
+#!   implementation: https://web.archive.org/web/20210416065610/https://coco.gforge.inria.fr/doku.php?id=downloads
+#!   source (real-world/artificial): ''
+#!   textual description: ''
+things["suite_bbob_noisy"] = Suite(
+    name="BBOB-noisy",
+    objectives={1},
+    variables=[Variable(type="continuous", dim=ValueRange(min=1))],
+    modality={"multimodal"},
+    noise_type={"noisy"},
+    references=[
+        Reference(
+            title="Real-parameter black-box optimization benchmarking: noisy functions definitions",
+            authors=[],
+            link=Link(url="https://hal.inria.fr/inria-00369466"),
+        )
+    ],
+    implementations={"impl_coco_legacy"},
+)
+
+#! - name: BBOB-largescale
+#!   suite/generator/single: suite
+#!   objectives: '1'
+#!   dimensionality: 20-640
+#!   variable type: continuous
+#!   constraints: 'no'
+#!   dynamic: 'no'
+#!   noise: 'no'
+#!   multimodal: 'yes'
+#!   multi-fidelity: 'no'
+#!   reference: https://doi.org/10.48550/arXiv.1903.06396
+#!   implementation: https://github.com/numbbo/coco
+#!   source (real-world/artificial): ''
+#!   textual description: ''
+things["suite_bbob_largescale"] = Suite(
+    name="BBOB-largescale",
+    objectives={1},
+    variables=[Variable(type="continuous", dim=ValueRange(min=20, max=640))],
+    modality={"multimodal"},
+    references=[
+        Reference(
+            title="BBOB large-scale test suite",
+            authors=[],
+            link=Link(url="https://doi.org/10.48550/arXiv.1903.06396"),
+        )
+    ],
+    implementations={"impl_coco"},
+)
+
+#! - name: BBOB-mixint
+#!   suite/generator/single: suite
+#!   objectives: '1'
+#!   dimensionality: 5-160
+#!   variable type: integer;continuous;mixed
+#!   constraints: 'no'
+#!   dynamic: 'no'
+#!   noise: 'no'
+#!   multimodal: 'yes'
+#!   multi-fidelity: 'no'
+#!   reference: https://doi.org/10.1145/3321707.3321868
+#!   implementation: https://github.com/numbbo/coco
+#!   source (real-world/artificial): ''
+#!   textual description: ''
+things["suite_bbob_mixint"] = Suite(
+    name="BBOB-mixint",
+    objectives={1},
+    variables=[
+        Variable(type="continuous", dim=ValueRange(min=5, max=160)),
+        Variable(type="integer", dim=ValueRange(min=5, max=160)),
+    ],
+    modality={"multimodal"},
+    references=[
+        Reference(
+            title="BBOB mixed-integer test suite",
+            authors=[],
+            link=Link(url="https://doi.org/10.1145/3321707.3321868"),
+        )
+    ],
+    implementations={"impl_coco"},
+)
+
+#! - name: BBOB-biobj-mixint
+#!   suite/generator/single: suite
+#!   objectives: '2'
+#!   dimensionality: 5-160
+#!   variable type: integer;continuous;mixed
+#!   constraints: 'no'
+#!   dynamic: 'no'
+#!   noise: 'no'
+#!   multimodal: 'yes'
+#!   multi-fidelity: 'no'
+#!   reference: https://doi.org/10.1145/3321707.3321868
+#!   implementation: https://github.com/numbbo/coco
+#!   source (real-world/artificial): ''
+#!   textual description: ''
+things["suite_bbob_biobj_mixint"] = Suite(
+    name="BBOB-biobj-mixint",
+    objectives={2},
+    variables=[
+        Variable(type="continuous", dim=ValueRange(min=5, max=160)),
+        Variable(type="integer", dim=ValueRange(min=5, max=160)),
+    ],
+    modality={"multimodal"},
+    references=[
+        Reference(
+            title="BBOB bi-objective mixed-integer test suite",
+            authors=[],
+            link=Link(url="https://doi.org/10.1145/3321707.3321868"),
+        )
+    ],
+    implementations={"impl_coco"},
+)
+
+#! - name: BBOB-constrained
+#!   suite/generator/single: suite
+#!   objectives: '1'
+#!   dimensionality: 2-40
+#!   variable type: continuous
+#!   constraints: 'yes'
+#!   dynamic: 'no'
+#!   noise: 'no'
+#!   multimodal: 'yes'
+#!   multi-fidelity: 'no'
+#!   reference: http://numbbo.github.io/coco-doc/bbob-constrained/
+#!   implementation: https://github.com/numbbo/coco
+#!   source (real-world/artificial): ''
+#!   textual description: ''
+things["suite_bbob_constrained"] = Suite(
+    name="BBOB-constrained",
+    objectives={1},
+    variables=[Variable(type="continuous", dim=ValueRange(min=2, max=40))],
+    constraints=[Constraint(hard="yes")],
+    modality={"multimodal"},
+    references=[
+        Reference(
+            title="bbob-constrained documentation",
+            authors=[],
+            link=Link(url="http://numbbo.github.io/coco-doc/bbob-constrained/"),
+        )
+    ],
+    implementations={"impl_coco"},
+)
+
+#! - name: MOrepo
+#!   suite/generator/single: suite
+#!   objectives: '2'
+#!   dimensionality: '?'
+#!   variable type: combinatorial
+#!   constraints: '?'
+#!   dynamic: '?'
+#!   noise: '?'
+#!   multimodal: '?'
+#!   multi-fidelity: 'no'
+#!   reference: ''
+#!   implementation: https://github.com/MCDMSociety/MOrepo
+#!   source (real-world/artificial): ''
+#!   textual description: ''
+things["impl_morepo"] = Implementation(
+    name="MOrepo",
+    description="Multi-objective optimisation problem repository",
+    links=[Link(type="repository", url="https://github.com/MCDMSociety/MOrepo")],
+)
+# FIXME: "combinatorial" has no direct VariableType; dimensionality "?" unknown.
+things["suite_morepo"] = Suite(
+    name="MOrepo",
+    objectives={2},
+    variables=[Variable(type="unknown")],
+    constraints=[Constraint(hard="?")],
+    dynamic_type={"unknown"},
+    noise_type={"unknown"},
+    implementations={"impl_morepo"},
+)
+
+#! - name: ZDT
+#!   suite/generator/single: suite
+#!   objectives: '2'
+#!   dimensionality: scalable
+#!   variable type: continuous;binary
+#!   constraints: 'no'
+#!   dynamic: 'no'
+#!   noise: 'no'
+#!   multimodal: '?'
+#!   multi-fidelity: 'no'
+#!   reference: https://doi.org/10.1162/106365600568202
+#!   implementation: https://github.com/anyoptimization/pymoo
+#!   source (real-world/artificial): ''
+#!   textual description: ''
+things["suite_zdt"] = Suite(
+    name="ZDT",
+    objectives={2},
+    variables=[
+        Variable(type="continuous", dim=ValueRange(min=1)),
+        Variable(type="binary", dim=ValueRange(min=1)),
+    ],
+    references=[
+        Reference(
+            title="Comparison of multiobjective evolutionary algorithms: empirical results",
+            authors=["Eckart Zitzler", "Kalyanmoy Deb", "Lothar Thiele"],
+            link=Link(url="https://doi.org/10.1162/106365600568202"),
+        )
+    ],
+    implementations={"impl_pymoo"},
+)
+
+#! - name: DTLZ
+#!   suite/generator/single: suite
+#!   objectives: 2+
+#!   dimensionality: scalable
+#!   variable type: continuous
+#!   constraints: 'no'
+#!   dynamic: 'no'
+#!   noise: 'no'
+#!   multimodal: '?'
+#!   multi-fidelity: 'no'
+#!   reference: https://doi.org/10.1109/CEC.2002.1007032
+#!   implementation: https://pymoo.org/problems/many/dtlz.html
+#!   source (real-world/artificial): ''
+#!   textual description: ''
+things["suite_dtlz"] = Suite(
+    name="DTLZ",
+    # FIXME: original "2+" - schema requires set[int]; truncated to 2..10.
+    objectives=set(range(2, 11)),
+    variables=[Variable(type="continuous", dim=ValueRange(min=1))],
+    references=[
+        Reference(
+            title="Scalable multi-objective optimization test problems",
+            authors=["Kalyanmoy Deb", "Lothar Thiele", "Marco Laumanns", "Eckart Zitzler"],
+            link=Link(url="https://doi.org/10.1109/CEC.2002.1007032"),
+        )
+    ],
+    implementations={"impl_pymoo"},
+)
+
+#! - name: WFG
+#!   suite/generator/single: suite
+#!   objectives: 2+
+#!   dimensionality: scalable
+#!   variable type: continuous
+#!   constraints: 'no'
+#!   dynamic: 'no'
+#!   noise: 'no'
+#!   multimodal: '?'
+#!   multi-fidelity: 'no'
+#!   reference: https://doi.org/10.1109/TEVC.2005.861417
+#!   implementation: https://pymoo.org/problems/many/wfg.html
+#!   source (real-world/artificial): ''
+#!   textual description: ''
+things["suite_wfg"] = Suite(
+    name="WFG",
+    # FIXME: original "2+" - truncated to 2..10.
+    objectives=set(range(2, 11)),
+    variables=[Variable(type="continuous", dim=ValueRange(min=1))],
+    references=[
+        Reference(
+            title="A review of multiobjective test problems and a scalable test problem toolkit",
+            authors=["Simon Huband", "Philip Hingston", "Luigi Barone", "Lyndon While"],
+            link=Link(url="https://doi.org/10.1109/TEVC.2005.861417"),
+        )
+    ],
+    implementations={"impl_pymoo"},
+)
+
+#! - name: CDMP
+#!   suite/generator/single: suite
+#!   objectives: 2+
+#!   dimensionality: scalable
+#!   variable type: continuous
+#!   constraints: 'yes'
+#!   dynamic: '?'
+#!   noise: '?'
+#!   multimodal: '?'
+#!   multi-fidelity: 'no'
+#!   reference: https://doi.org/10.1145/3321707.3321878
+#!   implementation: '?'
+#!   source (real-world/artificial): ''
+#!   textual description: ''
+# FIXME: implementation unknown.
+things["suite_cdmp"] = Suite(
+    name="CDMP",
+    # FIXME: original "2+" - truncated to 2..10.
+    objectives=set(range(2, 11)),
+    variables=[Variable(type="continuous", dim=ValueRange(min=1))],
+    constraints=[Constraint(hard="yes")],
+    dynamic_type={"unknown"},
+    noise_type={"unknown"},
+    references=[
+        Reference(
+            title="CDMP benchmark",
+            authors=[],
+            link=Link(url="https://doi.org/10.1145/3321707.3321878"),
+        )
+    ],
+)
+
+#! - name: SDP
+#!   suite/generator/single: suite
+#!   objectives: 2+
+#!   dimensionality: scalable
+#!   variable type: continuous
+#!   constraints: 'no'
+#!   dynamic: 'yes'
+#!   noise: '?'
+#!   multimodal: '?'
+#!   multi-fidelity: 'no'
+#!   reference: https://doi.org/10.1109/TCYB.2019.2896021
+#!   implementation: '?'
+#!   source (real-world/artificial): ''
+#!   textual description: ''
+# FIXME: implementation unknown.
+things["suite_sdp"] = Suite(
+    name="SDP",
+    # FIXME: original "2+" - truncated to 2..10.
+    objectives=set(range(2, 11)),
+    variables=[Variable(type="continuous", dim=ValueRange(min=1))],
+    dynamic_type={"dynamic"},
+    noise_type={"unknown"},
+    references=[
+        Reference(
+            title="SDP dynamic multi-objective benchmark",
+            authors=[],
+            link=Link(url="https://doi.org/10.1109/TCYB.2019.2896021"),
+        )
+    ],
+)
+
+#! - name: MaOP
+#!   suite/generator/single: suite
+#!   objectives: 2+
+#!   dimensionality: scalable
+#!   variable type: continuous
+#!   constraints: 'no'
+#!   dynamic: 'no'
+#!   noise: '?'
+#!   multimodal: '?'
+#!   multi-fidelity: 'no'
+#!   reference: https://doi.org/10.1016/j.swevo.2019.02.003
+#!   implementation: '?'
+#!   source (real-world/artificial): ''
+#!   textual description: ''
+# FIXME: implementation unknown.
+things["suite_maop"] = Suite(
+    name="MaOP",
+    # FIXME: original "2+" - truncated to 2..10.
+    objectives=set(range(2, 11)),
+    variables=[Variable(type="continuous", dim=ValueRange(min=1))],
+    noise_type={"unknown"},
+    references=[
+        Reference(
+            title="MaOP benchmark",
+            authors=[],
+            link=Link(url="https://doi.org/10.1016/j.swevo.2019.02.003"),
+        )
+    ],
+)
+
+#! - name: BP
+#!   suite/generator/single: suite
+#!   objectives: 2+
+#!   dimensionality: scalable
+#!   variable type: continuous
+#!   constraints: 'no'
+#!   dynamic: 'no'
+#!   noise: '?'
+#!   multimodal: '?'
+#!   multi-fidelity: 'no'
+#!   reference: https://doi.org/10.1109/CEC.2019.8790277
+#!   implementation: '?'
+#!   source (real-world/artificial): ''
+#!   textual description: ''
+# FIXME: implementation unknown.
+things["suite_bp"] = Suite(
+    name="BP",
+    # FIXME: original "2+" - truncated to 2..10.
+    objectives=set(range(2, 11)),
+    variables=[Variable(type="continuous", dim=ValueRange(min=1))],
+    noise_type={"unknown"},
+    references=[
+        Reference(
+            title="BP benchmark",
+            authors=[],
+            link=Link(url="https://doi.org/10.1109/CEC.2019.8790277"),
+        )
+    ],
+)
+
+#! - name: GPD
+#!   suite/generator/single: generator
+#!   objectives: 2+
+#!   dimensionality: scalable
+#!   variable type: continuous
+#!   constraints: optional
+#!   dynamic: 'no'
+#!   noise: optional
+#!   multimodal: '?'
+#!   multi-fidelity: 'no'
+#!   reference: https://doi.org/10.1016/j.asoc.2020.106139
+#!   implementation: '?'
+#!   source (real-world/artificial): ''
+#!   textual description: ''
+# FIXME: implementation unknown.
+things["gen_gpd"] = Generator(
+    name="GPD",
+    # FIXME: original "2+" - truncated to 2..10.
+    objectives=set(range(2, 11)),
+    variables=[Variable(type="continuous", dim=ValueRange(min=1))],
+    constraints=[Constraint(hard="some")],
+    noise_type={"optional"},
+    references=[
+        Reference(
+            title="GPD generator",
+            authors=[],
+            link=Link(url="https://doi.org/10.1016/j.asoc.2020.106139"),
+        )
+    ],
+)
+
+#! - name: ETMOF
+#!   suite/generator/single: suite
+#!   objectives: 2-50
+#!   dimensionality: 25-10000
+#!   variable type: continuous
+#!   constraints: 'no'
+#!   dynamic: 'yes'
+#!   noise: 'no'
+#!   multimodal: '?'
+#!   multi-fidelity: 'no'
+#!   reference: https://doi.org/10.48550/arXiv.2110.08033
+#!   implementation: https://github.com/songbai-liu/etmo
+#!   source (real-world/artificial): ''
+#!   textual description: ''
+things["impl_etmof"] = Implementation(
+    name="ETMOF",
+    description="Evolutionary many-task optimization framework",
+    links=[Link(type="repository", url="https://github.com/songbai-liu/etmo")],
+)
+things["suite_etmof"] = Suite(
+    name="ETMOF",
+    objectives=set(range(2, 51)),
+    variables=[Variable(type="continuous", dim=ValueRange(min=25, max=10000))],
+    dynamic_type={"dynamic"},
+    references=[
+        Reference(
+            title="Evolutionary many-task optimization framework",
+            authors=[],
+            link=Link(url="https://doi.org/10.48550/arXiv.2110.08033"),
+        )
+    ],
+    implementations={"impl_etmof"},
+)
+
+#! - name: MMOPP
+#!   suite/generator/single: suite
+#!   objectives: 2-7
+#!   dimensionality: '?'
+#!   variable type: '?'
+#!   constraints: 'yes'
+#!   dynamic: 'no'
+#!   noise: 'no'
+#!   multimodal: 'yes'
+#!   multi-fidelity: 'no'
+#!   reference: http://www5.zzu.edu.cn/system/_content/download.jsp?urltype=news.DownloadAttachUrl&owner=1327567121&wbfileid=4764412
+#!   implementation: http://www5.zzu.edu.cn/ecilab/info/1036/1251.htm
+#!   source (real-world/artificial): ''
+#!   textual description: ''
+things["impl_mmopp"] = Implementation(
+    name="MMOPP",
+    description="ECI lab distribution page for MMOPP",
+    links=[Link(type="website", url="http://www5.zzu.edu.cn/ecilab/info/1036/1251.htm")],
+)
+# FIXME: variable type and dimensionality unknown ("?").
+things["suite_mmopp"] = Suite(
+    name="MMOPP",
+    objectives=set(range(2, 8)),
+    variables=[Variable(type="unknown")],
+    constraints=[Constraint(hard="yes")],
+    modality={"multimodal"},
+    references=[
+        Reference(
+            title="MMOPP technical report",
+            authors=[],
+            link=Link(
+                url="http://www5.zzu.edu.cn/system/_content/download.jsp?urltype=news.DownloadAttachUrl&owner=1327567121&wbfileid=4764412"
+            ),
+        )
+    ],
+    implementations={"impl_mmopp"},
+)
+
+#! - name: CFD
+#!   suite/generator/single: suite
+#!   objectives: 1-2
+#!   dimensionality: scalable
+#!   variable type: '?'
+#!   constraints: 'yes'
+#!   dynamic: 'no'
+#!   noise: 'no'
+#!   multimodal: '?'
+#!   multi-fidelity: 'no'
+#!   reference: https://doi.org/10.1007/978-3-319-99259-4_24
+#!   implementation: https://bitbucket.org/arahat/cfd-test-problem-suite
+#!   source (real-world/artificial): real world
+#!   textual description: expensive evaluations 30s-15m
+things["impl_cfd"] = Implementation(
+    name="CFD test problem suite",
+    description="Expensive real-world CFD-based test problems",
+    evaluation_time="30s-15m",
+    links=[Link(type="repository", url="https://bitbucket.org/arahat/cfd-test-problem-suite")],
+)
+# FIXME: variable type unknown.
+things["suite_cfd"] = Suite(
+    name="CFD",
+    description="expensive evaluations 30s-15m",
+    objectives={1, 2},
+    variables=[Variable(type="unknown", dim=ValueRange(min=1))],
+    constraints=[Constraint(hard="yes")],
+    source={"real-world"},
+    references=[
+        Reference(
+            title="CFD test problem suite",
+            authors=[],
+            link=Link(url="https://doi.org/10.1007/978-3-319-99259-4_24"),
+        )
+    ],
+    implementations={"impl_cfd"},
+)
+
+#! - name: GBEA
+#!   suite/generator/single: suite
+#!   objectives: 1-2
+#!   dimensionality: scalable
+#!   variable type: continuous
+#!   constraints: 'no'
+#!   dynamic: 'no'
+#!   noise: 'yes'
+#!   multimodal: 'yes'
+#!   multi-fidelity: 'no'
+#!   reference: https://doi.org/10.1145/3321707.3321805
+#!   implementation: 'https://github.com/ttusar/coco-gbea'
+#!   source (real-world/artificial): real world
+#!   textual description: 'expensive evaluations 5s-35s, RW-GAN-Mario and TopTrumps are part of GBEA'
+things["impl_gbea"] = Implementation(
+    name="coco-gbea",
+    description="Game-Benchmark for Evolutionary Algorithms (COCO fork)",
+    evaluation_time="5s-35s",
+    links=[Link(type="repository", url="https://github.com/ttusar/coco-gbea")],
+)
+things["suite_gbea"] = Suite(
+    name="GBEA",
+    description="expensive evaluations 5s-35s, RW-GAN-Mario and TopTrumps are part of GBEA",
+    objectives={1, 2},
+    variables=[Variable(type="continuous", dim=ValueRange(min=1))],
+    noise_type={"noisy"},
+    modality={"multimodal"},
+    source={"real-world"},
+    references=[
+        Reference(
+            title="Game benchmark for evolutionary algorithms",
+            authors=[],
+            link=Link(url="https://doi.org/10.1145/3321707.3321805"),
+        )
+    ],
+    implementations={"impl_gbea"},
+)
+
+#! - name: Car structure
+#!   suite/generator/single: suite
+#!   objectives: '2'
+#!   dimensionality: 144-222
+#!   variable type: discrete
+#!   constraints: 'yes'
+#!   dynamic: 'no'
+#!   noise: 'no'
+#!   multimodal: '?'
+#!   multi-fidelity: 'no'
+#!   reference: https://doi.org/10.1145/3205651.3205702
+#!   implementation: http://ladse.eng.isas.jaxa.jp/benchmark/
+#!   source (real-world/artificial): real world
+#!   textual description: 54 constraints
+things["impl_car_structure"] = Implementation(
+    name="Car-structure benchmark",
+    description="JAXA LADSE benchmark problems",
+    links=[Link(type="website", url="http://ladse.eng.isas.jaxa.jp/benchmark/")],
+)
+# FIXME: "discrete" has no direct VariableType - using integer.
+things["suite_car_structure"] = Suite(
+    name="Car structure",
+    description="54 constraints",
+    objectives={2},
+    variables=[Variable(type="integer", dim=ValueRange(min=144, max=222))],
+    constraints=[Constraint(hard="yes", number=54)],
+    source={"real-world"},
+    references=[
+        Reference(
+            title="Car structure design benchmark",
+            authors=[],
+            link=Link(url="https://doi.org/10.1145/3205651.3205702"),
+        )
+    ],
+    implementations={"impl_car_structure"},
+)
+
+#! - name: EMO2017
+#!   suite/generator/single: suite
+#!   objectives: '2'
+#!   dimensionality: 4-24
+#!   variable type: continuous
+#!   constraints: 'no'
+#!   dynamic: 'no'
+#!   noise: 'no'
+#!   multimodal: '?'
+#!   multi-fidelity: 'no'
+#!   reference: https://www.ini.rub.de/PEOPLE/glasmtbl/projects/bbcomp/
+#!   implementation: https://www.ini.rub.de/PEOPLE/glasmtbl/projects/bbcomp/downloads/realworld-problems-bbcomp-EMO-2017.zip
+#!   source (real-world/artificial): real world
+#!   textual description: ''
+things["impl_emo2017"] = Implementation(
+    name="EMO 2017 real-world problems",
+    description="BBComp EMO-2017 real-world problem archive",
+    links=[
+        Link(
+            type="download",
+            url="https://www.ini.rub.de/PEOPLE/glasmtbl/projects/bbcomp/downloads/realworld-problems-bbcomp-EMO-2017.zip",
+        )
+    ],
+)
+things["suite_emo2017"] = Suite(
+    name="EMO2017",
+    objectives={2},
+    variables=[Variable(type="continuous", dim=ValueRange(min=4, max=24))],
+    source={"real-world"},
+    references=[
+        Reference(
+            title="BBComp EMO 2017",
+            authors=[],
+            link=Link(url="https://www.ini.rub.de/PEOPLE/glasmtbl/projects/bbcomp/"),
+        )
+    ],
+    implementations={"impl_emo2017"},
+)
+
+#! - name: JSEC2019
+#!   suite/generator/single: single
+#!   objectives: 1-5
+#!   dimensionality: '32'
+#!   variable type: continuous
+#!   constraints: 'yes'
+#!   dynamic: 'no'
+#!   noise: 'no'
+#!   multimodal: '?'
+#!   multi-fidelity: 'no'
+#!   reference: http://www.jpnsec.org/files/competition2019/EC-Symposium-2019-Competition-English.html
+#!   implementation: http://www.jpnsec.org/files/competition2019/EC-Symposium-2019-Competition-English.html
+#!   source (real-world/artificial): real world
+#!   textual description: expensive evaluations 3s; 22 constraints
+things["impl_jsec2019"] = Implementation(
+    name="JSEC 2019 competition",
+    description="JPNSEC EC-Symposium 2019 competition problem",
+    evaluation_time="3s",
+    links=[
+        Link(
+            type="website",
+            url="http://www.jpnsec.org/files/competition2019/EC-Symposium-2019-Competition-English.html",
+        )
+    ],
+)
+things["fn_jsec2019"] = Problem(
+    name="JSEC2019",
+    description="expensive evaluations 3s; 22 constraints",
+    objectives={1, 2, 3, 4, 5},
+    variables=[Variable(type="continuous", dim=32)],
+    constraints=[Constraint(hard="yes", number=22)],
+    source={"real-world"},
+    references=[
+        Reference(
+            title="JPNSEC EC-Symposium 2019 competition",
+            authors=[],
+            link=Link(
+                url="http://www.jpnsec.org/files/competition2019/EC-Symposium-2019-Competition-English.html"
+            ),
+        )
+    ],
+    implementations={"impl_jsec2019"},
+)
+
+#! - name: RE
+#!   suite/generator/single: suite
+#!   objectives: 2-9
+#!   dimensionality: 2-7
+#!   variable type: continuous;integer;mixed
+#!   constraints: 'no'
+#!   dynamic: 'no'
+#!   noise: 'no'
+#!   multimodal: '?'
+#!   multi-fidelity: 'no'
+#!   reference: https://doi.org/10.1016/j.asoc.2020.106078
+#!   implementation: https://github.com/ryojitanabe/reproblems
+#!   source (real-world/artificial): real world like
+#!   textual description: ''
+things["suite_re"] = Suite(
+    name="RE",
+    objectives=set(range(2, 10)),
+    variables=[
+        Variable(type="continuous", dim=ValueRange(min=2, max=7)),
+        Variable(type="integer", dim=ValueRange(min=2, max=7)),
+    ],
+    source={"real-world-like"},
+    references=[
+        Reference(
+            title="Easy-to-evaluate real-world multi-objective optimization problems",
+            authors=["Ryoji Tanabe", "Hisao Ishibuchi"],
+            link=Link(url="https://doi.org/10.1016/j.asoc.2020.106078"),
+        )
+    ],
+    implementations={"impl_reproblems"},
+)
+
+#! - name: CRE
+#!   suite/generator/single: suite
+#!   objectives: 2-5
+#!   dimensionality: 3-7
+#!   variable type: continuous;integer;mixed
+#!   constraints: 'yes'
+#!   dynamic: 'no'
+#!   noise: 'no'
+#!   multimodal: '?'
+#!   multi-fidelity: 'no'
+#!   reference: https://doi.org/10.1016/j.asoc.2020.106078
+#!   implementation: https://github.com/ryojitanabe/reproblems
+#!   source (real-world/artificial): real world like
+#!   textual description: ''
+things["suite_cre"] = Suite(
+    name="CRE",
+    objectives={2, 3, 4, 5},
+    variables=[
+        Variable(type="continuous", dim=ValueRange(min=3, max=7)),
+        Variable(type="integer", dim=ValueRange(min=3, max=7)),
+    ],
+    constraints=[Constraint(hard="yes")],
+    source={"real-world-like"},
+    references=[
+        Reference(
+            title="Easy-to-evaluate real-world multi-objective optimization problems",
+            authors=["Ryoji Tanabe", "Hisao Ishibuchi"],
+            link=Link(url="https://doi.org/10.1016/j.asoc.2020.106078"),
+        )
+    ],
+    implementations={"impl_reproblems"},
+)
+
+#! - name: Radar waveform
+#!   suite/generator/single: single
+#!   objectives: '9'
+#!   dimensionality: 4-12
+#!   variable type: integer
+#!   constraints: 'yes'
+#!   dynamic: 'no'
+#!   noise: 'no'
+#!   multimodal: '?'
+#!   multi-fidelity: 'no'
+#!   reference: https://doi.org/10.1007/978-3-540-70928-2_53
+#!   implementation: http://code.evanhughes.org/
+#!   source (real-world/artificial): real world
+#!   textual description: ''
+things["impl_radar_waveform"] = Implementation(
+    name="Evan Hughes radar waveform code",
+    description="Radar waveform design reference implementation",
+    links=[Link(type="website", url="http://code.evanhughes.org/")],
+)
+things["fn_radar_waveform"] = Problem(
+    name="Radar waveform",
+    objectives={9},
+    variables=[Variable(type="integer", dim=ValueRange(min=4, max=12))],
+    constraints=[Constraint(hard="yes")],
+    source={"real-world"},
+    references=[
+        Reference(
+            title="Radar waveform design",
+            authors=[],
+            link=Link(url="https://doi.org/10.1007/978-3-540-70928-2_53"),
+        )
+    ],
+    implementations={"impl_radar_waveform"},
+)
+
+#! - name: MF2
+#!   suite/generator/single: suite
+#!   objectives: '1'
+#!   dimensionality: 1-n
+#!   variable type: continuous
+#!   constraints: 'no'
+#!   dynamic: 'no'
+#!   noise: 'no'
+#!   multimodal: '?'
+#!   multi-fidelity: 'yes'
+#!   reference: https://doi.org/10.21105/joss.02049
+#!   implementation: https://github.com/sjvrijn/mf2
+#!   source (real-world/artificial): ''
+#!   textual description: ''
+things["impl_mf2"] = Implementation(
+    name="mf2",
+    description="Multi-fidelity test function collection",
+    language="Python",
+    links=[Link(type="repository", url="https://github.com/sjvrijn/mf2")],
+)
+things["suite_mf2"] = Suite(
+    name="MF2",
+    objectives={1},
+    variables=[Variable(type="continuous", dim=ValueRange(min=1))],
+    fidelity_levels={1, 2},
+    references=[
+        Reference(
+            title="mf2: a collection of multi-fidelity benchmark functions in Python",
+            authors=[],
+            link=Link(url="https://doi.org/10.21105/joss.02049"),
+        )
+    ],
+    implementations={"impl_mf2"},
+)
+
+#! - name: AMVOP
+#!   suite/generator/single: suite
+#!   objectives: '1'
+#!   dimensionality: scalable
+#!   variable type: mixed continuous+ordinal+categorical+both
+#!   constraints: 'no'
+#!   dynamic: 'no'
+#!   noise: 'no'
+#!   multimodal: 'yes'
+#!   multi-fidelity: 'no'
+#!   reference: https://doi.org/10.1109/TEVC.2013.2281531
+#!   implementation: '?'
+#!   source (real-world/artificial): ''
+#!   textual description: ''
+# FIXME: implementation unknown. "ordinal" not representable, using integer+categorical+continuous.
+things["suite_amvop"] = Suite(
+    name="AMVOP",
+    objectives={1},
+    variables=[
+        Variable(type="continuous", dim=ValueRange(min=1)),
+        Variable(type="integer", dim=ValueRange(min=1)),
+        Variable(type="categorical", dim=ValueRange(min=1)),
+    ],
+    modality={"multimodal"},
+    references=[
+        Reference(
+            title="AMVOP",
+            authors=[],
+            link=Link(url="https://doi.org/10.1109/TEVC.2013.2281531"),
+        )
+    ],
+)
+
+#! - name: RWMVOP
+#!   suite/generator/single: suite
+#!   objectives: '1'
+#!   dimensionality: scalable
+#!   variable type: continuous;mixed continuous+ordinal+categorical+both
+#!   constraints: 'yes'
+#!   dynamic: 'no'
+#!   noise: 'no'
+#!   multimodal: '?'
+#!   multi-fidelity: 'no'
+#!   reference: https://doi.org/10.1109/TEVC.2013.2281531
+#!   implementation: '?'
+#!   source (real-world/artificial): real world
+#!   textual description: ''
+# FIXME: implementation unknown.
+things["suite_rwmvop"] = Suite(
+    name="RWMVOP",
+    objectives={1},
+    variables=[
+        Variable(type="continuous", dim=ValueRange(min=1)),
+        Variable(type="integer", dim=ValueRange(min=1)),
+        Variable(type="categorical", dim=ValueRange(min=1)),
+    ],
+    constraints=[Constraint(hard="yes")],
+    source={"real-world"},
+    references=[
+        Reference(
+            title="RWMVOP",
+            authors=[],
+            link=Link(url="https://doi.org/10.1109/TEVC.2013.2281531"),
+        )
+    ],
+)
+
+#! - name: SBOX-COST
+#!   suite/generator/single: suite
+#!   objectives: '1'
+#!   dimensionality: scalable
+#!   variable type: continuous
+#!   constraints: 'no'
+#!   dynamic: 'no'
+#!   noise: 'no'
+#!   multimodal: 'yes'
+#!   multi-fidelity: 'no'
+#!   reference: https://doi.org/10.48550/arXiv.2305.12221
+#!   implementation: https://github.com/IOHprofiler/IOHexperimenter/
+#!   source (real-world/artificial): ''
+#!   textual description: problems from BBOB but allows instances with the optimum close to the
+#!     boundary
+things["suite_sbox_cost"] = Suite(
+    name="SBOX-COST",
+    description="problems from BBOB but allows instances with the optimum close to the boundary",
+    objectives={1},
+    variables=[Variable(type="continuous", dim=ValueRange(min=1))],
+    modality={"multimodal"},
+    references=[
+        Reference(
+            title="SBOX-COST",
+            authors=[],
+            link=Link(url="https://doi.org/10.48550/arXiv.2305.12221"),
+        )
+    ],
+    implementations={"impl_iohexperimenter"},
+)
+
+#! - name: "\u03C1MNK-Landscapes"
+#!   suite/generator/single: generator
+#!   objectives: scalable
+#!   dimensionality: scalable
+#!   variable type: binary
+#!   constraints: 'no'
+#!   dynamic: 'no'
+#!   noise: 'no'
+#!   multimodal: 'yes'
+#!   multi-fidelity: 'no'
+#!   reference: https://doi.org/10.1016/j.ejor.2012.12.019
+#!   implementation: https://gitlab.com/aliefooghe/mocobench/
+#!   source (real-world/artificial): ''
+#!   textual description: tunable variable and objective dimensions; tunable multimodality and
+#!     correlation between objectives
+things["gen_rho_mnk_landscapes"] = Generator(
+    name="ρMNK-Landscapes",
+    description="tunable variable and objective dimensions; tunable multimodality and correlation between objectives",
+    # FIXME: original "scalable" - truncated to 1..10.
+    objectives=set(range(1, 11)),
+    variables=[Variable(type="binary", dim=ValueRange(min=1))],
+    modality={"multimodal"},
+    references=[
+        Reference(
+            title="On the design of multi-objective evolutionary algorithms based on NK-landscapes",
+            authors=[],
+            link=Link(url="https://doi.org/10.1016/j.ejor.2012.12.019"),
+        )
+    ],
+    implementations={"impl_mocobench"},
+)
+
+#! - name: mUBQP
+#!   suite/generator/single: generator
+#!   objectives: scalable
+#!   dimensionality: scalable
+#!   variable type: binary
+#!   constraints: 'no'
+#!   dynamic: 'no'
+#!   noise: 'no'
+#!   multimodal: yes (quadratic)
+#!   multi-fidelity: 'no'
+#!   reference: https://doi.org/10.1016/j.asoc.2013.11.008
+#!   implementation: https://gitlab.com/aliefooghe/mocobench/
+#!   source (real-world/artificial): ''
+#!   textual description: tunable variable and objective dimensions; tunable density and correlation
+#!     between objectives
+things["gen_mubqp"] = Generator(
+    name="mUBQP",
+    description="tunable variable and objective dimensions; tunable density and correlation between objectives",
+    # FIXME: original "scalable" - truncated to 1..10.
+    objectives=set(range(1, 11)),
+    variables=[Variable(type="binary", dim=ValueRange(min=1))],
+    modality={"multimodal", "quadratic"},
+    references=[
+        Reference(
+            title="mUBQP benchmark",
+            authors=[],
+            link=Link(url="https://doi.org/10.1016/j.asoc.2013.11.008"),
+        )
+    ],
+    implementations={"impl_mocobench"},
+)
+
+#! - name: "\u03C1mTSP"
+#!   suite/generator/single: generator
+#!   objectives: scalable
+#!   dimensionality: scalable
+#!   variable type: permutations
+#!   constraints: no (apart from being permutations)
+#!   dynamic: 'no'
+#!   noise: 'no'
+#!   multimodal: yes (quadratic)
+#!   multi-fidelity: 'no'
+#!   reference: https://doi.org/10.1007/978-3-319-45823-6_40
+#!   implementation: https://gitlab.com/aliefooghe/mocobench/
+#!   source (real-world/artificial): ''
+#!   textual description: tunable variable and objective dimensions; tunable instance type (euclidian/random);
+#!     tunable correlation between objectives
+# FIXME: "permutations" has no direct VariableType; constraints are implicit permutations.
+things["gen_rho_mtsp"] = Generator(
+    name="ρmTSP",
+    description="tunable variable and objective dimensions; tunable instance type (euclidean/random); tunable correlation between objectives",
+    # FIXME: original "scalable" - truncated to 1..10.
+    objectives=set(range(1, 11)),
+    variables=[Variable(type="unknown", dim=ValueRange(min=1))],
+    modality={"multimodal", "quadratic"},
+    references=[
+        Reference(
+            title="On the impact of multi-objective scalability for the ρmTSP",
+            authors=[],
+            link=Link(url="https://doi.org/10.1007/978-3-319-45823-6_40"),
+        )
+    ],
+    implementations={"impl_mocobench"},
+)
+
+#! - name: CEC2015-DMOO
+#!   suite/generator/single: suite
+#!   objectives: 2-3
+#!   dimensionality: '?'
+#!   variable type: continuous
+#!   constraints: '?'
+#!   dynamic: 'yes'
+#!   noise: 'no'
+#!   multimodal: '?'
+#!   multi-fidelity: 'no'
+#!   reference: Benchmark Functions for CEC 2015 Special Session and Competition on Dynamic
+#!     Multi-objective Optimization
+#!   implementation: ''
+#!   source (real-world/artificial): ''
+#!   textual description: ''
+# FIXME: reference is a title-only string; implementation unavailable; dimensionality unknown.
+things["suite_cec2015_dmoo"] = Suite(
+    name="CEC2015-DMOO",
+    objectives={2, 3},
+    variables=[Variable(type="continuous")],
+    constraints=[Constraint(hard="?")],
+    dynamic_type={"dynamic"},
+    references=[
+        Reference(
+            title="Benchmark Functions for CEC 2015 Special Session and Competition on Dynamic Multi-objective Optimization",
+            authors=[],
+        )
+    ],
+)
+
+#! - name: Ealain
+#!   suite/generator/single: generator
+#!   objectives: 1+
+#!   dimensionality: scalable
+#!   variable type: continuous,binary,integer
+#!   constraints: optional
+#!   dynamic: optional
+#!   noise: 'no'
+#!   multimodal: '?'
+#!   multi-fidelity: optional
+#!   reference: https://doi.org/10.1145/3638530.3654299
+#!   implementation: https://github.com/qrenau/Ealain
+#!   source (real-world/artificial): Real-world-like
+#!   textual description: Real-world-like, easily extensible to increase complexity
+things["impl_ealain"] = Implementation(
+    name="Ealain",
+    description="Real-world-like extensible benchmark problem generator",
+    links=[Link(type="repository", url="https://github.com/qrenau/Ealain")],
+)
+things["gen_ealain"] = Generator(
+    name="Ealain",
+    description="Real-world-like, easily extensible to increase complexity",
+    # FIXME: original "1+" - truncated to 1..10.
+    objectives=set(range(1, 11)),
+    variables=[
+        Variable(type="continuous", dim=ValueRange(min=1)),
+        Variable(type="binary", dim=ValueRange(min=1)),
+        Variable(type="integer", dim=ValueRange(min=1)),
+    ],
+    constraints=[Constraint(hard="some")],
+    dynamic_type={"optional"},
+    fidelity_levels={1, 2},
+    source={"real-world-like"},
+    references=[
+        Reference(
+            title="Ealain",
+            authors=[],
+            link=Link(url="https://doi.org/10.1145/3638530.3654299"),
+        )
+    ],
+    implementations={"impl_ealain"},
+)
+
+#! - name: MA-BBOB
+#!   suite/generator/single: generator
+#!   objectives: '1'
+#!   dimensionality: scalable
+#!   variable type: continuous
+#!   constraints: 'no'
+#!   dynamic: 'no'
+#!   noise: 'no'
+#!   multimodal: 'yes'
+#!   multi-fidelity: 'no'
+#!   reference: https://doi.org/10.1145/3673908
+#!   implementation: https://github.com/IOHprofiler/IOHexperimenter/blob/master/example/Competitions/MA-BBOB/Example_MABBOB.ipynb
+#!   source (real-world/artificial): artificial
+#!   textual description: Generator that creates affine combinations of BBOB functions
+things["impl_ma_bbob"] = Implementation(
+    name="MA-BBOB (IOHexperimenter)",
+    description="Example notebook for MA-BBOB in IOHexperimenter",
+    links=[
+        Link(
+            type="example",
+            url="https://github.com/IOHprofiler/IOHexperimenter/blob/master/example/Competitions/MA-BBOB/Example_MABBOB.ipynb",
+        )
+    ],
+)
+things["gen_ma_bbob"] = Generator(
+    name="MA-BBOB",
+    description="Generator that creates affine combinations of BBOB functions",
+    objectives={1},
+    variables=[Variable(type="continuous", dim=ValueRange(min=1))],
+    modality={"multimodal"},
+    source={"artificial"},
+    references=[
+        Reference(
+            title="MA-BBOB",
+            authors=[],
+            link=Link(url="https://doi.org/10.1145/3673908"),
+        )
+    ],
+    implementations={"impl_ma_bbob", "impl_iohexperimenter"},
+)
+
+#! - name: MPM2
+#!   suite/generator/single: generator
+#!   objectives: '1'
+#!   dimensionality: scalable
+#!   variable type: continuous
+#!   constraints: 'no'
+#!   dynamic: 'no'
+#!   noise: 'no'
+#!   multimodal: 'yes'
+#!   multi-fidelity: 'no'
+#!   reference: https://ls11-www.cs.tu-dortmund.de/_media/techreports/tr15-01.pdf
+#!   implementation: https://github.com/jakobbossek/smoof/blob/master/inst/mpm2.py
+#!   source (real-world/artificial): ''
+#!   textual description: nonlinear nonseparable nonsymmetric; scalable in terms of time to evaluate
+#!     the objective function
+things["impl_mpm2"] = Implementation(
+    name="MPM2 (smoof)",
+    description="Python implementation of MPM2 distributed with smoof",
+    language="Python",
+    links=[
+        Link(
+            type="source",
+            url="https://github.com/jakobbossek/smoof/blob/master/inst/mpm2.py",
+        )
+    ],
+)
+things["gen_mpm2"] = Generator(
+    name="MPM2",
+    description="nonlinear nonseparable nonsymmetric; scalable in terms of time to evaluate the objective function",
+    objectives={1},
+    variables=[Variable(type="continuous", dim=ValueRange(min=1))],
+    modality={"multimodal"},
+    references=[
+        Reference(
+            title="MPM2 technical report TR15-01",
+            authors=[],
+            link=Link(url="https://ls11-www.cs.tu-dortmund.de/_media/techreports/tr15-01.pdf"),
+        )
+    ],
+    implementations={"impl_mpm2"},
+)
+
+#! - name: Convex DTLZ2
+#!   suite/generator/single: single
+#!   objectives: 2+
+#!   dimensionality: scalable
+#!   variable type: continuous
+#!   constraints: 'no'
+#!   dynamic: 'no'
+#!   noise: 'no'
+#!   multimodal: '?'
+#!   multi-fidelity: 'no'
+#!   reference: https://doi.org/10.1109/TEVC.2013.2281535
+#!   implementation: '?'
+#!   source (real-world/artificial): ''
+#!   textual description: Variant of DTLZ2 with a convex Pareto front (instead of concave)
+# FIXME: implementation unknown.
+things["fn_convex_dtlz2"] = Problem(
+    name="Convex DTLZ2",
+    description="Variant of DTLZ2 with a convex Pareto front (instead of concave)",
+    # FIXME: original "2+" - truncated to 2..10.
+    objectives=set(range(2, 11)),
+    variables=[Variable(type="continuous", dim=ValueRange(min=1))],
+    references=[
+        Reference(
+            title="Convex DTLZ2",
+            authors=[],
+            link=Link(url="https://doi.org/10.1109/TEVC.2013.2281535"),
+        )
+    ],
+)
+
+#! - name: Inverted DTLZ1
+#!   suite/generator/single: single
+#!   objectives: 2+
+#!   dimensionality: scalable
+#!   variable type: continuous
+#!   constraints: 'no'
+#!   dynamic: 'no'
+#!   noise: 'no'
+#!   multimodal: '?'
+#!   multi-fidelity: 'no'
+#!   reference: https://doi.org/10.1109/TEVC.2013.2281534
+#!   implementation: '?'
+#!   source (real-world/artificial): ''
+#!   textual description: Variant of DTLZ1 with an inverted Pareto front
+# FIXME: implementation unknown.
+things["fn_inverted_dtlz1"] = Problem(
+    name="Inverted DTLZ1",
+    description="Variant of DTLZ1 with an inverted Pareto front",
+    # FIXME: original "2+" - truncated to 2..10.
+    objectives=set(range(2, 11)),
+    variables=[Variable(type="continuous", dim=ValueRange(min=1))],
+    references=[
+        Reference(
+            title="Inverted DTLZ1",
+            authors=[],
+            link=Link(url="https://doi.org/10.1109/TEVC.2013.2281534"),
+        )
+    ],
+)
+
+#! - name: Minus DTLZ
+#!   suite/generator/single: suite
+#!   objectives: 2+
+#!   dimensionality: scalable
+#!   variable type: continuous
+#!   constraints: 'no'
+#!   dynamic: 'no'
+#!   noise: 'no'
+#!   multimodal: '?'
+#!   multi-fidelity: 'no'
+#!   reference: https://doi.org/10.1109/TEVC.2016.2587749
+#!   implementation: '?'
+#!   source (real-world/artificial): ''
+#!   textual description: Variant of DTLZ that minimises the inverse of the base DTLZ functions
+# FIXME: implementation unknown.
+things["suite_minus_dtlz"] = Suite(
+    name="Minus DTLZ",
+    description="Variant of DTLZ that minimises the inverse of the base DTLZ functions",
+    # FIXME: original "2+" - truncated to 2..10.
+    objectives=set(range(2, 11)),
+    variables=[Variable(type="continuous", dim=ValueRange(min=1))],
+    references=[
+        Reference(
+            title="Minus DTLZ / Minus WFG",
+            authors=[],
+            link=Link(url="https://doi.org/10.1109/TEVC.2016.2587749"),
+        )
+    ],
+)
+
+#! - name: Minus WFG
+#!   suite/generator/single: suite
+#!   objectives: 2+
+#!   dimensionality: scalable
+#!   variable type: continuous
+#!   constraints: 'no'
+#!   dynamic: 'no'
+#!   noise: 'no'
+#!   multimodal: '?'
+#!   multi-fidelity: 'no'
+#!   reference: https://doi.org/10.1109/TEVC.2016.2587749
+#!   implementation: '?'
+#!   source (real-world/artificial): ''
+#!   textual description: Variant of WFG that minimises the inverse of the base WFG functions
+# FIXME: implementation unknown.
+things["suite_minus_wfg"] = Suite(
+    name="Minus WFG",
+    description="Variant of WFG that minimises the inverse of the base WFG functions",
+    # FIXME: original "2+" - truncated to 2..10.
+    objectives=set(range(2, 11)),
+    variables=[Variable(type="continuous", dim=ValueRange(min=1))],
+    references=[
+        Reference(
+            title="Minus DTLZ / Minus WFG",
+            authors=[],
+            link=Link(url="https://doi.org/10.1109/TEVC.2016.2587749"),
+        )
+    ],
+)
+
+#! - name: L1-ZDT
+#!   suite/generator/single: suite
+#!   objectives: '2'
+#!   dimensionality: scalable
+#!   variable type: continuous;binary
+#!   constraints: 'no'
+#!   dynamic: 'no'
+#!   noise: 'no'
+#!   multimodal: '?'
+#!   multi-fidelity: 'no'
+#!   reference: https://doi.org/10.1145/1143997.1144179
+#!   implementation: '?'
+#!   source (real-world/artificial): ''
+#!   textual description: Variant of ZDT with linkages between variables within one of two groups
+#!     but not between variables in a different group; Linear recombination operators
+#!     can potentially take advantage of the problem structure
+# FIXME: implementation unknown.
+things["suite_l1_zdt"] = Suite(
+    name="L1-ZDT",
+    description="Variant of ZDT with linkages between variables within groups",
+    objectives={2},
+    variables=[
+        Variable(type="continuous", dim=ValueRange(min=1)),
+        Variable(type="binary", dim=ValueRange(min=1)),
+    ],
+    references=[
+        Reference(
+            title="Linkage ZDT/DTLZ variants",
+            authors=[],
+            link=Link(url="https://doi.org/10.1145/1143997.1144179"),
+        )
+    ],
+)
+
+#! - name: L2-ZDT
+#!   suite/generator/single: suite
+#!   objectives: '2'
+#!   dimensionality: scalable
+#!   variable type: continuous;binary
+#!   constraints: 'no'
+#!   dynamic: 'no'
+#!   noise: 'no'
+#!   multimodal: '?'
+#!   multi-fidelity: 'no'
+#!   reference: https://doi.org/10.1145/1143997.1144179
+#!   implementation: '?'
+#!   source (real-world/artificial): ''
+#!   textual description: Variant of ZDT with linkages between all variables; Linear recombination
+#!     operators can potentially take advantage of the problem structure
+# FIXME: implementation unknown.
+things["suite_l2_zdt"] = Suite(
+    name="L2-ZDT",
+    description="Variant of ZDT with linkages between all variables",
+    objectives={2},
+    variables=[
+        Variable(type="continuous", dim=ValueRange(min=1)),
+        Variable(type="binary", dim=ValueRange(min=1)),
+    ],
+    references=[
+        Reference(
+            title="Linkage ZDT/DTLZ variants",
+            authors=[],
+            link=Link(url="https://doi.org/10.1145/1143997.1144179"),
+        )
+    ],
+)
+
+#! - name: L3-ZDT
+#!   suite/generator/single: suite
+#!   objectives: '2'
+#!   dimensionality: scalable
+#!   variable type: continuous;binary
+#!   constraints: 'no'
+#!   dynamic: 'no'
+#!   noise: 'no'
+#!   multimodal: '?'
+#!   multi-fidelity: 'no'
+#!   reference: https://doi.org/10.1145/1143997.1144179
+#!   implementation: '?'
+#!   source (real-world/artificial): ''
+#!   textual description: Variant of L2-ZDT using a mapping to prevent linear recombination operators
+#!     from potentially taking advantage of the problem structure
+# FIXME: implementation unknown.
+things["suite_l3_zdt"] = Suite(
+    name="L3-ZDT",
+    description="Variant of L2-ZDT with anti-linkage mapping",
+    objectives={2},
+    variables=[
+        Variable(type="continuous", dim=ValueRange(min=1)),
+        Variable(type="binary", dim=ValueRange(min=1)),
+    ],
+    references=[
+        Reference(
+            title="Linkage ZDT/DTLZ variants",
+            authors=[],
+            link=Link(url="https://doi.org/10.1145/1143997.1144179"),
+        )
+    ],
+)
+
+#! - name: L2-DTLZ
+#!   suite/generator/single: suite
+#!   objectives: 2+
+#!   dimensionality: scalable
+#!   variable type: continuous
+#!   constraints: 'no'
+#!   dynamic: 'no'
+#!   noise: 'no'
+#!   multimodal: '?'
+#!   multi-fidelity: 'no'
+#!   reference: https://doi.org/10.1145/1143997.1144179
+#!   implementation: '?'
+#!   source (real-world/artificial): ''
+#!   textual description: Variant of DTLZ2 and DTLZ3 with linkages between all variables; Linear
+#!     recombination operators can potentially take advantage of the problem structure
+# FIXME: implementation unknown.
+things["suite_l2_dtlz"] = Suite(
+    name="L2-DTLZ",
+    description="Variant of DTLZ2/DTLZ3 with linkages between all variables",
+    # FIXME: original "2+" - truncated to 2..10.
+    objectives=set(range(2, 11)),
+    variables=[Variable(type="continuous", dim=ValueRange(min=1))],
+    references=[
+        Reference(
+            title="Linkage ZDT/DTLZ variants",
+            authors=[],
+            link=Link(url="https://doi.org/10.1145/1143997.1144179"),
+        )
+    ],
+)
+
+#! - name: L3-DTLZ
+#!   suite/generator/single: suite
+#!   objectives: 2+
+#!   dimensionality: scalable
+#!   variable type: continuous
+#!   constraints: 'no'
+#!   dynamic: 'no'
+#!   noise: 'no'
+#!   multimodal: '?'
+#!   multi-fidelity: 'no'
+#!   reference: https://doi.org/10.1145/1143997.1144179
+#!   implementation: '?'
+#!   source (real-world/artificial): ''
+#!   textual description: Variant of L2-DTLZ using a mapping to prevent linear recombination operators
+#!     from potentially taking advantage of the problem structure
+# FIXME: implementation unknown.
+things["suite_l3_dtlz"] = Suite(
+    name="L3-DTLZ",
+    description="Variant of L2-DTLZ with anti-linkage mapping",
+    # FIXME: original "2+" - truncated to 2..10.
+    objectives=set(range(2, 11)),
+    variables=[Variable(type="continuous", dim=ValueRange(min=1))],
+    references=[
+        Reference(
+            title="Linkage ZDT/DTLZ variants",
+            authors=[],
+            link=Link(url="https://doi.org/10.1145/1143997.1144179"),
+        )
+    ],
+)
+
+#! - name: CEC2018 DT - CEC2018 Competition on Dynamic Multiobjective Optimisation
+#!   suite/generator/single: suite
+#!   objectives: 2 or 3
+#!   dimensionality: scalable?
+#!   variable type: '?'
+#!   constraints: 'no'
+#!   dynamic: 'yes'
+#!   noise: 'no'
+#!   multimodal: '?'
+#!   multi-fidelity: 'no'
+#!   reference: https://www.academia.edu/download/94499025/TR-CEC2018-DMOP-Competition.pdf
+#!   implementation: https://pymoo.org/problems/dynamic/df.html
+#!   source (real-world/artificial): artificial
+#!   textual description: '14 problems. Time-dependent: Pareto front/Pareto set geometry;
+#!     irregular Pareto front shapes; variable-linkage; number of disconnected Pareto
+#!     front segments; etc.'
+# FIXME: variable type unknown.
+things["suite_cec2018_dt"] = Suite(
+    name="CEC2018 DT",
+    long_name="CEC2018 Competition on Dynamic Multiobjective Optimisation",
+    description="14 problems. Time-dependent: Pareto front/Pareto set geometry; irregular Pareto front shapes; variable-linkage; number of disconnected Pareto front segments; etc.",
+    objectives={2, 3},
+    variables=[Variable(type="unknown", dim=ValueRange(min=1))],
+    dynamic_type={"dynamic"},
+    source={"artificial"},
+    references=[
+        Reference(
+            title="CEC2018 DMOP Competition TR",
+            authors=[],
+            link=Link(url="https://www.academia.edu/download/94499025/TR-CEC2018-DMOP-Competition.pdf"),
+        )
+    ],
+    implementations={"impl_pymoo"},
+)
+
+#! - name: MODAct - multiobjective design of actuators
+#!   suite/generator/single: suite
+#!   objectives: 2 3 4 or 5
+#!   dimensionality: '20'
+#!   variable type: mixed; integer and continuous
+#!   constraints: 'yes'
+#!   dynamic: 'no'
+#!   noise: 'no'
+#!   multimodal: '?'
+#!   multi-fidelity: 'no'
+#!   reference: https://doi.org/10.1109/TEVC.2020.3020046
+#!   implementation: https://pymoo.org/problems/constrained/modact.html
+#!   source (real-world/artificial): real-world
+#!   textual description: Realistic Constrained Multi-Objective Optimization Benchmark
+#!     Problems from Design. Need the https://github.com/epfl-lamd/modact package installed; evaluation
+#!     times around 20ms
+things["impl_modact"] = Implementation(
+    name="modact",
+    description="EPFL-LAMD modact package",
+    evaluation_time="20ms",
+    links=[Link(type="repository", url="https://github.com/epfl-lamd/modact")],
+)
+things["suite_modact"] = Suite(
+    name="MODAct",
+    long_name="multiobjective design of actuators",
+    description="Realistic Constrained Multi-Objective Optimization Benchmark Problems from Design.",
+    objectives={2, 3, 4, 5},
+    variables=[
+        Variable(type="continuous", dim=20),
+        Variable(type="integer", dim=20),
+    ],
+    constraints=[Constraint(hard="yes")],
+    source={"real-world"},
+    references=[
+        Reference(
+            title="MODAct",
+            authors=[],
+            link=Link(url="https://doi.org/10.1109/TEVC.2020.3020046"),
+        )
+    ],
+    implementations={"impl_modact", "impl_pymoo"},
+)
+
+#! - name: IOHClustering
+#!   suite/generator/single: suite; generator
+#!   objectives: '1'
+#!   dimensionality: scalable
+#!   variable type: continuous
+#!   constraints: 'no'
+#!   dynamic: 'no'
+#!   noise: 'no'
+#!   multimodal: 'yes'
+#!   multi-fidelity: 'no '
+#!   reference: https://arxiv.org/pdf/2505.09233
+#!   implementation: https://github.com/IOHprofiler/IOHClustering
+#!   source (real-world/artificial): artificial, but based on real data
+#!   textual description: 'Set of benchmark problems from clustering: optimization task
+#!     is selecting cluster centers for a given set of data, with the number of clusters
+#!     defining problem dimensionality. Includes both a suite and a generator. Based on ML clustering datasets'
+things["impl_iohclustering"] = Implementation(
+    name="IOHClustering",
+    description="Clustering-based optimization benchmark built on ML datasets",
+    links=[Link(type="repository", url="https://github.com/IOHprofiler/IOHClustering")],
+)
+things["suite_iohclustering"] = Suite(
+    name="IOHClustering",
+    description="Set of benchmark problems from clustering: optimization task is selecting cluster centers for a given set of data.",
+    objectives={1},
+    variables=[Variable(type="continuous", dim=ValueRange(min=1))],
+    modality={"multimodal"},
+    source={"artificial-from-real-data"},
+    references=[
+        Reference(
+            title="IOHClustering",
+            authors=[],
+            link=Link(url="https://arxiv.org/pdf/2505.09233"),
+        )
+    ],
+    implementations={"impl_iohclustering"},
+)
+things["gen_iohclustering"] = Generator(
+    name="IOHClustering",
+    description="Generator counterpart of the IOHClustering suite.",
+    objectives={1},
+    variables=[Variable(type="continuous", dim=ValueRange(min=1))],
+    modality={"multimodal"},
+    source={"artificial-from-real-data"},
+    references=[
+        Reference(
+            title="IOHClustering",
+            authors=[],
+            link=Link(url="https://arxiv.org/pdf/2505.09233"),
+        )
+    ],
+    implementations={"impl_iohclustering"},
+)
+
+#! - name: GNBG-II
+#!   suite/generator/single: suite; generator
+#!   objectives: '1'
+#!   dimensionality: scalable
+#!   variable type: continuous
+#!   constraints: 'no'
+#!   dynamic: 'no'
+#!   noise: 'no'
+#!   multimodal: '?'
+#!   multi-fidelity: 'no'
+#!   reference: https://dl.acm.org/doi/pdf/10.1145/3712255.3734271
+#!   implementation: https://github.com/rohitsalgotra/GNBG-II
+#!   source (real-world/artificial): artificial
+#!   textual description: Generalized Numerical Benchmark Generator (version 2). Also in IOH https://github.com/IOHprofiler/IOHGNBG
+things["impl_gnbg_ii"] = Implementation(
+    name="GNBG-II",
+    description="Generalized Numerical Benchmark Generator version 2",
+    links=[Link(type="repository", url="https://github.com/rohitsalgotra/GNBG-II")],
+)
+things["impl_iohgnbg"] = Implementation(
+    name="IOHGNBG",
+    description="IOHprofiler version of GNBG",
+    links=[Link(type="repository", url="https://github.com/IOHprofiler/IOHGNBG")],
+)
+things["suite_gnbg_ii"] = Suite(
+    name="GNBG-II",
+    description="Generalized Numerical Benchmark Generator (version 2). Also available in IOH.",
+    objectives={1},
+    variables=[Variable(type="continuous", dim=ValueRange(min=1))],
+    source={"artificial"},
+    references=[
+        Reference(
+            title="GNBG-II",
+            authors=[],
+            link=Link(url="https://dl.acm.org/doi/pdf/10.1145/3712255.3734271"),
+        )
+    ],
+    implementations={"impl_gnbg_ii", "impl_iohgnbg"},
+)
+things["gen_gnbg_ii"] = Generator(
+    name="GNBG-II",
+    description="Generator counterpart of GNBG-II.",
+    objectives={1},
+    variables=[Variable(type="continuous", dim=ValueRange(min=1))],
+    source={"artificial"},
+    references=[
+        Reference(
+            title="GNBG-II",
+            authors=[],
+            link=Link(url="https://dl.acm.org/doi/pdf/10.1145/3712255.3734271"),
+        )
+    ],
+    implementations={"impl_gnbg_ii", "impl_iohgnbg"},
+)
+
+#! - name: GNBG
+#!   suite/generator/single: suite; generator
+#!   objectives: '1'
+#!   dimensionality: scalable
+#!   variable type: continuous
+#!   constraints: 'no'
+#!   dynamic: 'no'
+#!   noise: 'no'
+#!   multimodal: '?'
+#!   multi-fidelity: 'no'
+#!   reference: https://arxiv.org/abs/2312.07083
+#!   implementation: https://github.com/Danial-Yazdani/GNBG-Generator
+#!   source (real-world/artificial): artificial
+#!   textual description: Generalized Numerical Benchmark Generator
+things["impl_gnbg"] = Implementation(
+    name="GNBG Generator",
+    description="Generalized Numerical Benchmark Generator",
+    links=[Link(type="repository", url="https://github.com/Danial-Yazdani/GNBG-Generator")],
+)
+things["suite_gnbg"] = Suite(
+    name="GNBG",
+    description="Generalized Numerical Benchmark Generator",
+    objectives={1},
+    variables=[Variable(type="continuous", dim=ValueRange(min=1))],
+    source={"artificial"},
+    references=[
+        Reference(
+            title="GNBG",
+            authors=[],
+            link=Link(url="https://arxiv.org/abs/2312.07083"),
+        )
+    ],
+    implementations={"impl_gnbg"},
+)
+things["gen_gnbg"] = Generator(
+    name="GNBG",
+    description="Generator counterpart of GNBG.",
+    objectives={1},
+    variables=[Variable(type="continuous", dim=ValueRange(min=1))],
+    source={"artificial"},
+    references=[
+        Reference(
+            title="GNBG",
+            authors=[],
+            link=Link(url="https://arxiv.org/abs/2312.07083"),
+        )
+    ],
+    implementations={"impl_gnbg"},
+)
+
+#! - name: DynamicBinVal
+#!   suite/generator/single: suite
+#!   objectives: '1'
+#!   dimensionality: scalable
+#!   variable type: binary
+#!   constraints: 'no'
+#!   dynamic: 'yes'
+#!   noise: 'no'
+#!   multimodal: '?'
+#!   multi-fidelity: 'no'
+#!   reference: https://arxiv.org/pdf/2404.15837
+#!   implementation: https://github.com/IOHprofiler/IOHexperimenter
+#!   source (real-world/artificial): artificial
+#!   textual description: Four versions of the dynamic binary value problem
+things["suite_dynamicbinval"] = Suite(
+    name="DynamicBinVal",
+    description="Four versions of the dynamic binary value problem",
+    objectives={1},
+    variables=[Variable(type="binary", dim=ValueRange(min=1))],
+    dynamic_type={"dynamic"},
+    source={"artificial"},
+    references=[
+        Reference(
+            title="DynamicBinVal",
+            authors=[],
+            link=Link(url="https://arxiv.org/pdf/2404.15837"),
+        )
+    ],
+    implementations={"impl_iohexperimenter"},
+)
+
+#! - name: PBO
+#!   suite/generator/single: suite
+#!   objectives: '1'
+#!   dimensionality: scalable
+#!   variable type: binary
+#!   constraints: 'no'
+#!   dynamic: 'no'
+#!   noise: 'no'
+#!   multimodal: '?'
+#!   multi-fidelity: 'no'
+#!   reference: https://dl.acm.org/doi/pdf/10.1145/3319619.3326810
+#!   implementation: https://github.com/IOHprofiler/IOHexperimenter
+#!   source (real-world/artificial): artificial
+#!   textual description: Suite of 25 binary optimization problems
+things["suite_pbo"] = Suite(
+    name="PBO",
+    description="Suite of 25 binary optimization problems",
+    objectives={1},
+    variables=[Variable(type="binary", dim=ValueRange(min=1))],
+    source={"artificial"},
+    references=[
+        Reference(
+            title="PBO benchmarks",
+            authors=[],
+            link=Link(url="https://dl.acm.org/doi/pdf/10.1145/3319619.3326810"),
+        )
+    ],
+    implementations={"impl_iohexperimenter"},
+)
+
+#! - name: W-model
+#!   suite/generator/single: generator
+#!   objectives: '1'
+#!   dimensionality: scalable
+#!   variable type: binary
+#!   constraints: 'no'
+#!   dynamic: 'no'
+#!   noise: 'no'
+#!   multimodal: '?'
+#!   multi-fidelity: 'no'
+#!   reference: https://dl.acm.org/doi/abs/10.1145/3205651.3208240?casa_token=S4U_Pi9f6MwAAAAA:U9ztNTPwmupT8K3GamWZfBL7-8fqjxPtr_kprv51vdwA-REsp0EyOFGa99BtbANb0XbqyrVg795hIw
+#!   implementation: https://github.com/thomasWeise/BBDOB_W_Model
+#!   source (real-world/artificial): artificial
+#!   textual description: Tunable generator for binary optimization based on several
+#!     difficulty features
+things["impl_wmodel"] = Implementation(
+    name="BBDOB W-Model",
+    description="Tunable generator for binary optimization",
+    links=[Link(type="repository", url="https://github.com/thomasWeise/BBDOB_W_Model")],
+)
+things["gen_wmodel"] = Generator(
+    name="W-model",
+    description="Tunable generator for binary optimization based on several difficulty features",
+    objectives={1},
+    variables=[Variable(type="binary", dim=ValueRange(min=1))],
+    source={"artificial"},
+    references=[
+        Reference(
+            title="W-model",
+            authors=[],
+            link=Link(
+                url="https://dl.acm.org/doi/abs/10.1145/3205651.3208240"
+            ),
+        )
+    ],
+    implementations={"impl_wmodel"},
+)
+
+#! - name: Submodular Optimitzation
+#!   suite/generator/single: suite
+#!   objectives: '1'
+#!   dimensionality: scalable
+#!   variable type: binary
+#!   constraints: 'no'
+#!   dynamic: 'no'
+#!   noise: 'no'
+#!   multimodal: '?'
+#!   multi-fidelity: 'no'
+#!   reference: https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10254181
+#!   implementation: https://github.com/IOHprofiler/IOHexperimenter
+#!   source (real-world/artificial): artificial
+#!   textual description: set of graph-based submodular optimization problems from 4
+#!     problem types
+things["suite_submodular"] = Suite(
+    name="Submodular Optimization",
+    description="set of graph-based submodular optimization problems from 4 problem types",
+    objectives={1},
+    variables=[Variable(type="binary", dim=ValueRange(min=1))],
+    source={"artificial"},
+    references=[
+        Reference(
+            title="Submodular optimization benchmark",
+            authors=[],
+            link=Link(url="https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10254181"),
+        )
+    ],
+    implementations={"impl_iohexperimenter"},
+)
+
+#! - name: CEC2013
+#!   suite/generator/single: suite
+#!   objectives: '1'
+#!   dimensionality: scalable
+#!   variable type: continuous
+#!   constraints: 'no'
+#!   dynamic: 'no'
+#!   noise: 'no'
+#!   multimodal: '?'
+#!   multi-fidelity: 'no'
+#!   reference: https://peerj.com/articles/cs-2671/CEC2013.pdf
+#!   implementation: https://github.com/P-N-Suganthan/CEC2013
+#!   source (real-world/artificial): artificial
+#!   textual description: suite used for cec2013 competition. Also in IOH https://github.com/IOHprofiler/IOHexperimenter
+things["impl_cec2013"] = Implementation(
+    name="CEC2013 reference code",
+    description="Suganthan's reference implementation",
+    links=[Link(type="repository", url="https://github.com/P-N-Suganthan/CEC2013")],
+)
+things["suite_cec2013"] = Suite(
+    name="CEC2013",
+    description="suite used for cec2013 competition. Also in IOH.",
+    objectives={1},
+    variables=[Variable(type="continuous", dim=ValueRange(min=1))],
+    source={"artificial"},
+    references=[
+        Reference(
+            title="CEC2013 definitions",
+            authors=[],
+            link=Link(url="https://peerj.com/articles/cs-2671/CEC2013.pdf"),
+        )
+    ],
+    implementations={"impl_cec2013", "impl_iohexperimenter"},
+)
+
+#! - name: CEC2022
+#!   suite/generator/single: suite
+#!   objectives: '1'
+#!   dimensionality: scalable
+#!   variable type: continuous
+#!   constraints: 'no'
+#!   dynamic: 'no'
+#!   noise: 'no'
+#!   multimodal: '?'
+#!   multi-fidelity: 'no'
+#!   reference: https://github.com/P-N-Suganthan/2022-SO-BO/blob/main/CEC2022%20TR.pdf
+#!   implementation: https://github.com/P-N-Suganthan/2022-SO-BO
+#!   source (real-world/artificial): artificial
+#!   textual description: suite used for cec2022 competition. Also in IOH https://github.com/IOHprofiler/IOHexperimenter
+things["impl_cec2022"] = Implementation(
+    name="CEC2022 reference code",
+    description="Suganthan's reference implementation",
+    links=[Link(type="repository", url="https://github.com/P-N-Suganthan/2022-SO-BO")],
+)
+things["suite_cec2022"] = Suite(
+    name="CEC2022",
+    description="suite used for cec2022 competition. Also in IOH.",
+    objectives={1},
+    variables=[Variable(type="continuous", dim=ValueRange(min=1))],
+    source={"artificial"},
+    references=[
+        Reference(
+            title="CEC2022 TR",
+            authors=[],
+            link=Link(url="https://github.com/P-N-Suganthan/2022-SO-BO/blob/main/CEC2022%20TR.pdf"),
+        )
+    ],
+    implementations={"impl_cec2022", "impl_iohexperimenter"},
+)
+
+#! - name: Onemax+Sphere / Zeromax+Sphere
+#!   suite/generator/single: single
+#!   objectives: '2'
+#!   dimensionality: scalable
+#!   variable type: binary and continuous;mixed;
+#!   constraints: 'no'
+#!   dynamic: 'no'
+#!   noise: 'no'
+#!   multimodal: ''
+#!   multi-fidelity: 'no'
+#!   reference: https://doi.org/10.1145/3449726.3459521
+#!   implementation:
+#!   source (real-world/artificial): 'artificial'
+#!   textual description: ''
+# FIXME: no implementation provided.
+things["fn_onemax_sphere_zeromax_sphere"] = Problem(
+    name="Onemax+Sphere / Zeromax+Sphere",
+    objectives={2},
+    variables=[
+        Variable(type="binary", dim=ValueRange(min=1)),
+        Variable(type="continuous", dim=ValueRange(min=1)),
+    ],
+    source={"artificial"},
+    references=[
+        Reference(
+            title="Onemax+Sphere / Zeromax+Sphere",
+            authors=[],
+            link=Link(url="https://doi.org/10.1145/3449726.3459521"),
+        )
+    ],
+)
+
+#! - name: Onemax+Sphere / DeceptiveTrap+RotatedEllipsoid
+#!   suite/generator/single: single
+#!   objectives: '2'
+#!   dimensionality: scalable
+#!   variable type: binary and continuous;mixed;
+#!   constraints: 'no'
+#!   dynamic: 'no'
+#!   noise: 'no'
+#!   multimodal: ''
+#!   multi-fidelity: 'no'
+#!   reference: https://doi.org/10.1145/3449726.3459521
+#!   implementation:
+#!   source (real-world/artificial): 'artificial'
+#!   textual description: ''
+# FIXME: no implementation provided.
+things["fn_onemax_sphere_deceptive_rotell"] = Problem(
+    name="Onemax+Sphere / DeceptiveTrap+RotatedEllipsoid",
+    objectives={2},
+    variables=[
+        Variable(type="binary", dim=ValueRange(min=1)),
+        Variable(type="continuous", dim=ValueRange(min=1)),
+    ],
+    source={"artificial"},
+    references=[
+        Reference(
+            title="Mixed-variable multi-objective test problems",
+            authors=[],
+            link=Link(url="https://doi.org/10.1145/3449726.3459521"),
+        )
+    ],
+)
+
+#! - name: InverseDeceptiveTrap+RotatedEllipsoid / DeceptiveTrap+RotatedEllipsoid
+#!   suite/generator/single: single
+#!   objectives: '2'
+#!   dimensionality: scalable
+#!   variable type: binary and continuous;mixed;
+#!   constraints: 'no'
+#!   dynamic: 'no'
+#!   noise: 'no'
+#!   multimodal: ''
+#!   multi-fidelity: 'no'
+#!   reference: https://doi.org/10.1145/3449726.3459521
+#!   implementation:
+#!   source (real-world/artificial): 'artificial'
+#!   textual description: ''
+# FIXME: no implementation provided.
+things["fn_invdeceptive_deceptive_rotell"] = Problem(
+    name="InverseDeceptiveTrap+RotatedEllipsoid / DeceptiveTrap+RotatedEllipsoid",
+    objectives={2},
+    variables=[
+        Variable(type="binary", dim=ValueRange(min=1)),
+        Variable(type="continuous", dim=ValueRange(min=1)),
+    ],
+    source={"artificial"},
+    references=[
+        Reference(
+            title="Mixed-variable multi-objective test problems",
+            authors=[],
+            link=Link(url="https://doi.org/10.1145/3449726.3459521"),
+        )
+    ],
+)
+
+#! - name: PorkchopPlotInterplanetaryTrajectory
+#!   suite/generator/single: suite
+#!   objectives: '1'
+#!   dimensionality: 2
+#!   variable type: continuous
+#!   constraints: 'no'
+#!   dynamic: 'no'
+#!   noise: 'no'
+#!   multimodal: 'yes'
+#!   multi-fidelity: 'no'
+#!   reference: https://doi.org/10.1109/CEC65147.2025.11042973
+#!   implementation: https://github.com/ShuaiqunPan/Transfer_Random_forests_BBOB_Real_world
+#!   source (real-world/artificial): 'real-world'
+#!   textual description: ''
+things["impl_transfer_rf_bbob_rw"] = Implementation(
+    name="Transfer Random Forests BBOB Real-world",
+    description="Real-world BBOB-like problem implementations (Porkchop, KinematicsRobotArm)",
+    links=[
+        Link(
+            type="repository",
+            url="https://github.com/ShuaiqunPan/Transfer_Random_forests_BBOB_Real_world",
+        )
+    ],
+)
+things["suite_porkchop"] = Suite(
+    name="PorkchopPlotInterplanetaryTrajectory",
+    objectives={1},
+    variables=[Variable(type="continuous", dim=2)],
+    modality={"multimodal"},
+    source={"real-world"},
+    references=[
+        Reference(
+            title="Porkchop plot interplanetary trajectory benchmark",
+            authors=[],
+            link=Link(url="https://doi.org/10.1109/CEC65147.2025.11042973"),
+        )
+    ],
+    implementations={"impl_transfer_rf_bbob_rw"},
+)
+
+#! - name: KinematicsRobotArm
+#!   suite/generator/single: suite
+#!   objectives: '1'
+#!   dimensionality: 21
+#!   variable type: continuous
+#!   constraints: 'no'
+#!   dynamic: 'no'
+#!   noise: 'no'
+#!   multimodal: 'no'
+#!   multi-fidelity: 'no'
+#!   reference: https://doi.org/10.1023/A:1013258808932
+#!   implementation: https://github.com/ShuaiqunPan/Transfer_Random_forests_BBOB_Real_world
+#!   source (real-world/artificial): 'real-world'
+#!   textual description: ''
+things["suite_kinematics_robotarm"] = Suite(
+    name="KinematicsRobotArm",
+    objectives={1},
+    variables=[Variable(type="continuous", dim=21)],
+    modality={"unimodal"},
+    source={"real-world"},
+    references=[
+        Reference(
+            title="Kinematics of a robot arm",
+            authors=[],
+            link=Link(url="https://doi.org/10.1023/A:1013258808932"),
+        )
+    ],
+    implementations={"impl_transfer_rf_bbob_rw"},
+)
+
+#! - name:  VehicleDynamics
+#!   suite/generator/single: suite
+#!   objectives: '1'
+#!   dimensionality: 2
+#!   variable type: continuous
+#!   constraints: 'no'
+#!   dynamic: 'no'
+#!   noise: 'no'
+#!   multimodal: 'yes'
+#!   multi-fidelity: 'no'
+#!   reference: https://www.scitepress.org/Papers/2023/121580/121580.pdf
+#!   implementation: https://zenodo.org/records/8307853
+#!   source (real-world/artificial): 'real-world'
+#!   textual description: ''
+things["impl_vehicle_dynamics"] = Implementation(
+    name="VehicleDynamics (Zenodo)",
+    description="Zenodo archive for the vehicle dynamics benchmark",
+    links=[Link(type="archive", url="https://zenodo.org/records/8307853")],
+)
+things["suite_vehicle_dynamics"] = Suite(
+    name="VehicleDynamics",
+    objectives={1},
+    variables=[Variable(type="continuous", dim=2)],
+    modality={"multimodal"},
+    source={"real-world"},
+    references=[
+        Reference(
+            title="VehicleDynamics benchmark",
+            authors=[],
+            link=Link(url="https://www.scitepress.org/Papers/2023/121580/121580.pdf"),
+        )
+    ],
+    implementations={"impl_vehicle_dynamics"},
+)
+
+#! - name: MECHBench
+#!   suite/generator/single: Problem Suite
+#!   variable type: Continuous
+#!   dimensionality: scalable'
+#!   objectives: '1'
+#!   constraints: 'yes'
+#!   dynamic: 'no'
+#!   noise: 'no'
+#!   multimodal: 'yes'
+#!   multi-fidelity: 'no'
+#!   source (real-world/artificial): Real-World Application
+#!   implementation: https://github.com/BayesOptApp/MECHBench
+#!   textual description: This is a set of problems with inspiration from Structural
+#!     Mechanics Design Optimization. The suite comprises three physical models, from
+#!     which the user may define different kind of problems which impact the final design
+#!     output.
+#!   reference: https://arxiv.org/abs/2511.10821
+#!   other info:
+#!     partial evaluations: 'no'
+#!     full name: MECHBench
+#!     constraint properties: Hard Constraints
+#!     number of constraints: 1 or 2
+#!     description of multimodality: Unstructured or non isotropic multimodality
+#!     key challenges / characteristics: Embeds physical simulations and is flexible
+#!       and modular
+#!     scientific motivation: Bridge the black-box optimization techniques to a Mechanical
+#!       Design Problem which require these kinds of algorithms
+#!     limitations: The models do not include fracture or damage mechanics, just plasticity.
+#!     implementation languages: Python
+#!     approximate evaluation time: Times -> from 1 minute to 7 minutes
+things["impl_mechbench"] = Implementation(
+    name="MECHBench",
+    description="Structural mechanics design optimization benchmark",
+    language="Python",
+    evaluation_time="1-7 minutes",
+    links=[Link(type="repository", url="https://github.com/BayesOptApp/MECHBench")],
+)
+things["suite_mechbench"] = Suite(
+    name="MECHBench",
+    long_name="MECHBench",
+    description="Set of problems inspired by Structural Mechanics Design Optimization. Embeds physical simulations (plasticity only, no fracture/damage). Unstructured/non-isotropic multimodality.",
+    objectives={1},
+    variables=[Variable(type="continuous", dim=ValueRange(min=1))],
+    constraints=[Constraint(hard="yes", number={1, 2})],
+    modality={"multimodal"},
+    allows_partial_evaluation="no",
+    source={"real-world"},
+    references=[
+        Reference(
+            title="MECHBench",
+            authors=[],
+            link=Link(url="https://arxiv.org/abs/2511.10821"),
+        )
+    ],
+    implementations={"impl_mechbench"},
+)
+
+#! - name: EXPObench
+#!   suite/generator/single: Problem Suite
+#!   variable type: Continuous, Integer, Categorical, Conditional
+#!   dimensionality: 10 to 135
+#!   objectives: '1'
+#!   constraints: 'yes'
+#!   dynamic: 'no'
+#!   noise: 'yes'
+#!   multimodal: Unknown
+#!   multi-fidelity: 'no'
+#!   source (real-world/artificial): Real-World Application
+#!   implementation: https://github.com/AlgTUDelft/ExpensiveOptimBenchmark
+#!   textual description: Wind farm layout optimization, gas filter design, pipe shape
+#!     optimization, hyperparameter tuning, and hospital simulation
+#!   reference: https://doi.org/10.1016/j.asoc.2023.110744
+#!   other info:
+#!     partial evaluations: 'no'
+#!     full name: EXPensive Optimization benchmark library
+#!     constraint properties: Hard Constraints, Soft Constraints, Box Constraints, only
+#!       box constraints implemented, others appear as penalty in objective
+#!     number of constraints: 2 per variable (box), other constraints unknown (simulator
+#!       fails)
+#!     form of noise model: real-life (unknown)
+#!     type of noise space: Observational
+#!     key challenges / characteristics: Expensive objectives
+#!     scientific motivation: Address the lack of real-life expensive benchmarks
+#!     limitations: single-objective only, constraints are handled naively (penalty in
+#!       objective), no parallelization
+#!     implementation languages: Python
+#!     approximate evaluation time: 2 to 80 seconds
+# FIXME: "Conditional" variable type has no schema representation; box number expressed as 2 per variable cannot be encoded.
+things["impl_expobench"] = Implementation(
+    name="EXPObench",
+    description="EXPensive Optimization benchmark library (wind farm layout, gas filter design, pipe shape, hyperparameter tuning, hospital simulation)",
+    language="Python",
+    evaluation_time="2 to 80 seconds",
+    links=[Link(type="repository", url="https://github.com/AlgTUDelft/ExpensiveOptimBenchmark")],
+)
+things["suite_expobench"] = Suite(
+    name="EXPObench",
+    long_name="EXPensive Optimization benchmark library",
+    description="Wind farm layout optimization, gas filter design, pipe shape optimization, hyperparameter tuning, and hospital simulation",
+    objectives={1},
+    variables=[
+        Variable(type="continuous", dim=ValueRange(min=10, max=135)),
+        Variable(type="integer", dim=ValueRange(min=10, max=135)),
+        Variable(type="categorical", dim=ValueRange(min=10, max=135)),
+    ],
+    constraints=[
+        Constraint(type="box", hard="yes"),
+        Constraint(hard="some"),
+    ],
+    noise_type={"observational", "real-life"},
+    allows_partial_evaluation="no",
+    source={"real-world"},
+    references=[
+        Reference(
+            title="EXPObench",
+            authors=[],
+            link=Link(url="https://doi.org/10.1016/j.asoc.2023.110744"),
+        )
+    ],
+    implementations={"impl_expobench"},
+)
+
+#! - name: Gasoline direct injection engine design
+#!   suite/generator/single: Single Problem
+#!   variable type: Continuous, Ordinal
+#!   dimensionality: '7'
+#!   objectives: '2'
+#!   constraints: 'yes'
+#!   dynamic: 'no'
+#!   noise: 'no'
+#!   multimodal: Unknown
+#!   multi-fidelity: 'yes'
+#!   source (real-world/artificial): Real-World Application
+#!   implementation: https://doi.org/10.1016/j.ejor.2022.08.032
+#!   textual description: ...
+#!   other info:
+#!     partial evaluations: Unknown
+#!     constraint properties: Hard Constraints, Soft Constraints
+#!     number of constraints: '5'
+#!     key challenges / characteristics: Expensive
+#!     limitations: Proprietary
+#!     implementation languages: Matlab Simulink and Wave RT co-simulation
+# FIXME: "Ordinal" variable type not in schema; falling back to integer.
+things["impl_gasoline"] = Implementation(
+    name="Gasoline direct injection engine design",
+    description="Proprietary Matlab Simulink + Wave RT co-simulation",
+    language="Matlab Simulink / Wave RT",
+    links=[Link(type="paper", url="https://doi.org/10.1016/j.ejor.2022.08.032")],
+)
+things["fn_gasoline"] = Problem(
+    name="Gasoline direct injection engine design",
+    description="Multi-objective optimization to minimize fuel consumption and NOx emissions over a two-minute dynamic duty cycle, subject to five constraints (turbine inlet temperature, knock occurrences, peak cylinder pressure, peak cylinder pressure rise, total work). Seven decision variables cover hardware choices and engine control parameters.",
+    objectives={2},
+    variables=[
+        Variable(type="continuous", dim=7),
+        Variable(type="integer", dim=7),
+    ],
+    constraints=[Constraint(hard="yes", number=5)],
+    fidelity_levels={1, 2},
+    source={"real-world"},
+    references=[
+        Reference(
+            title="Gasoline direct injection engine design",
+            authors=[],
+            link=Link(url="https://doi.org/10.1016/j.ejor.2022.08.032"),
+        )
+    ],
+    implementations={"impl_gasoline"},
+)
+
+#! - name: BEACON
+#!   suite/generator/single: Generator
+#!   variable type: Continuous
+#!   dimensionality: scalable
+#!   objectives: '2'
+#!   constraints: 'no'
+#!   dynamic: 'no'
+#!   noise: 'no'
+#!   multimodal: 'yes'
+#!   multi-fidelity: 'no'
+#!   source (real-world/artificial): Artificially Generated
+#!   implementation: https://github.com/Stebbet/BEACON/
+#!   textual description: Generator for bi-objective benchmark problems with explicitly
+#!     controlled correlations in continuous spaces.
+#!   reference: https://dl.acm.org/doi/10.1145/3712255.3734303
+#!   other info:
+#!     partial evaluations: 'no'
+#!     full name: Continuous Bi-objective Benchmark problems with Explicit Adjustable
+#!       COrrelatioN control
+#!     constraint properties: Box Constraints
+#!     number of constraints: '0'
+#!     description of multimodality: Random
+#!     key challenges / characteristics: Multimodal, different correlations among objectives
+#!     scientific motivation: Controlled correlation among objectives
+#!     limitations: No analytical Pareto front, only bi-objective
+#!     implementation languages: Python
+#!     approximate evaluation time: Negligible
+things["impl_beacon"] = Implementation(
+    name="BEACON",
+    description="Continuous Bi-objective Benchmark with Explicit Adjustable COrrelatioN control",
+    language="Python",
+    evaluation_time="negligible",
+    links=[Link(type="repository", url="https://github.com/Stebbet/BEACON/")],
+)
+things["gen_beacon"] = Generator(
+    name="BEACON",
+    long_name="Continuous Bi-objective Benchmark problems with Explicit Adjustable COrrelatioN control",
+    description="Generator for bi-objective benchmark problems with explicitly controlled correlations in continuous spaces. Multimodal with random structure.",
+    objectives={2},
+    variables=[Variable(type="continuous", dim=ValueRange(min=1))],
+    constraints=[Constraint(type="box", hard="yes", number=0)],
+    modality={"multimodal"},
+    allows_partial_evaluation="no",
+    source={"artificial"},
+    references=[
+        Reference(
+            title="BEACON",
+            authors=[],
+            link=Link(url="https://dl.acm.org/doi/10.1145/3712255.3734303"),
+        )
+    ],
+    implementations={"impl_beacon"},
+)
+
+#! - name: TulipaEnergy
+#!   suite/generator/single: Problem Suite
+#!   variable type: Continuous
+#!   dimensionality: scalable
+#!   objectives: '1'
+#!   constraints: 'yes'
+#!   dynamic: 'no'
+#!   noise: 'yes'
+#!   multimodal: 'no'
+#!   multi-fidelity: 'yes'
+#!   source (real-world/artificial): Real-World Application
+#!   implementation: https://tulipaenergy.github.io/TulipaEnergyModel.jl/stable/
+#!   textual description: Determine the optimal investment and operation decisions  for
+#!     different types of assets in the energy system ... minimizing loss of load.
+#!   reference: See https://tulipaenergy.github.io/TulipaEnergyModel.jl/stable/40-scientific-foundation/45-scientific-references
+#!   other info:
+#!     partial evaluations: Unknown
+#!     full name: TulipaEnergyModel.jl
+#!     constraint properties: Hard Constraints, Soft Constraints
+#!     number of constraints: millions
+#!     type of dynamicism: none
+#!     form of noise model: depends on input — still working on stochastic inputs
+#!     type of noise space: Parameter
+#!     key challenges / characteristics: modeled as a potentially very large linear program
+#!     scientific motivation: new techniques for solving large whitebox linear optimization problems
+#!     limitations: not yet stochastic
+#!     implementation languages: Julia / JMP
+#!     approximate evaluation time: from minutes to hours
+#!     links to usage examples: https://github.com/TulipaEnergy/Tulipa-OBZ-CaseStudy
+# FIXME: "number of constraints: millions" cannot be expressed precisely.
+things["impl_tulipa"] = Implementation(
+    name="TulipaEnergyModel.jl",
+    description="Large linear program for optimal investment and operation of energy systems",
+    language="Julia / JuMP",
+    evaluation_time="minutes to hours",
+    links=[
+        Link(type="website", url="https://tulipaenergy.github.io/TulipaEnergyModel.jl/stable/"),
+        Link(type="example", url="https://github.com/TulipaEnergy/Tulipa-OBZ-CaseStudy"),
+    ],
+)
+things["suite_tulipa_energy"] = Suite(
+    name="TulipaEnergy",
+    long_name="TulipaEnergyModel.jl",
+    description="Determine the optimal investment and operation decisions for different assets in the energy system (production, consumption, conversion, storage, transport) while minimizing loss of load. Modelled as a potentially very large linear program with multiple fidelity levels.",
+    objectives={1},
+    variables=[Variable(type="continuous", dim=ValueRange(min=1))],
+    constraints=[Constraint(hard="yes"), Constraint(hard="some")],
+    noise_type={"parameter"},
+    modality={"unimodal"},
+    fidelity_levels={1, 2},
+    source={"real-world"},
+    references=[
+        Reference(
+            title="TulipaEnergyModel.jl scientific references",
+            authors=[],
+            link=Link(
+                url="https://tulipaenergy.github.io/TulipaEnergyModel.jl/stable/40-scientific-foundation/45-scientific-references"
+            ),
+        )
+    ],
+    implementations={"impl_tulipa"},
+)
+
+#! - name: ATO
+#!   suite/generator/single: Single Problem
+#!   variable type: Continuous
+#!   dimensionality: '10'
+#!   objectives: '2'
+#!   constraints: 'no'
+#!   dynamic: 'no'
+#!   noise: 'no'
+#!   multimodal: 'no'
+#!   multi-fidelity: 'no'
+#!   source (real-world/artificial): Real-World Application
+#!   implementation: '-'
+#!   textual description: Parameters of the Modules of the Automatic Train Operation
+#!     should be optimized. The parameters are continuous with different ranges. There
+#!     are two objectives (minimizing energy consumption, minimizing driving duration.
+#!   other info:
+#!     partial evaluations: 'no'
+# FIXME: no implementation available.
+things["fn_ato"] = Problem(
+    name="ATO",
+    description="Parameters of the Modules of the Automatic Train Operation are optimized; two objectives: minimizing energy consumption and minimizing driving duration.",
+    objectives={2},
+    variables=[Variable(type="continuous", dim=10)],
+    modality={"unimodal"},
+    allows_partial_evaluation="no",
+    source={"real-world"},
+)
+
+#! - name: Brachytherapy treatment planning
+#!   suite/generator/single: Problem Suite
+#!   variable type: Continuous
+#!   dimensionality: 100-500
+#!   objectives: 2-3
+#!   constraints: 'yes'
+#!   dynamic: 'no'
+#!   noise: 'no'
+#!   multimodal: 'yes'
+#!   multi-fidelity: 'yes'
+#!   source (real-world/artificial): Real-World Application
+#!   textual description: Treatment planning for internal radiation therapy
+#!   reference: https://www.sciencedirect.com/science/article/pii/S1538472123016781
+#!   other info:
+#!     partial evaluations: 'yes'
+#!     full name: Brachytherapy treatment planning
+#!     constraint properties: Hard Constraints
+#!     number of constraints: scalable
+#!     key challenges / characteristics: Multi-objective; aggregated objectives
+#!     limitations: No public source code
+# FIXME: no public source code; no implementation URL.
+things["suite_brachytherapy"] = Suite(
+    name="Brachytherapy treatment planning",
+    long_name="Brachytherapy treatment planning",
+    description="Treatment planning for internal radiation therapy. Multi-objective with aggregated objectives; no public source code.",
+    objectives={2, 3},
+    variables=[Variable(type="continuous", dim=ValueRange(min=100, max=500))],
+    constraints=[Constraint(hard="yes", number=ValueRange(min=1))],
+    modality={"multimodal"},
+    fidelity_levels={1, 2},
+    allows_partial_evaluation="yes",
+    source={"real-world"},
+    references=[
+        Reference(
+            title="Brachytherapy treatment planning",
+            authors=[],
+            link=Link(url="https://www.sciencedirect.com/science/article/pii/S1538472123016781"),
+        )
+    ],
+)
+
+#! - name: FleetOpt
+#!   suite/generator/single: Single Problem
+#!   variable type: Integer
+#!   dimensionality: 'Upper level: 54; lower level: 13208'
+#!   objectives: '1'
+#!   constraints: 'yes'
+#!   dynamic: 'no'
+#!   noise: 'no'
+#!   multimodal: Unknown
+#!   multi-fidelity: 'no'
+#!   source (real-world/artificial): Real-World Application
+#!   implementation: 'Not public: was done for real client with their private data'
+#!   textual description: Healthcare organisation in the UK ...
+#!   reference: https://dl.acm.org/doi/abs/10.1145/3638530.3664137
+#!   other info:
+#!     partial evaluations: 'yes'
+# FIXME: bilevel dimensionality (upper 54 / lower 13208) expressed as {54, 13208}; impl not public.
+things["fn_fleetopt"] = Problem(
+    name="FleetOpt",
+    description="UK healthcare organisation fleet optimisation: reduce the fleet of non-emergency healthcare trip vehicles while still ensuring all trips can be covered. Bilevel: upper level 54 vars, lower level 13208 vars.",
+    objectives={1},
+    variables=[Variable(type="integer", dim={54, 13208})],
+    constraints=[Constraint(hard="yes")],
+    allows_partial_evaluation="yes",
+    source={"real-world"},
+    references=[
+        Reference(
+            title="FleetOpt",
+            authors=[],
+            link=Link(url="https://dl.acm.org/doi/abs/10.1145/3638530.3664137"),
+        )
+    ],
+)
+
+#! - name: Building spatial design
+#!   suite/generator/single: Single Problem
+#!   variable type: Continuous, Boolean
+#!   dimensionality: scalable depending on problem size (e.g. 90 for)
+#!   objectives: '2'
+#!   constraints: 'yes'
+#!   dynamic: 'no'
+#!   noise: 'no'
+#!   multimodal: Unknown
+#!   multi-fidelity: 'no'
+#!   source (real-world/artificial): Real-World Application
+#!   implementation: https://github.com/TUe-excellent-buildings/BSO-toolbox
+#!   textual description: 'Optimise the spatial layout of a building to: minimise energy
+#!     consumption for climate control, and minimise the strain on the structure'
+#!   reference: https://hdl.handle.net/1887/81789
+#!   other info:
+#!     partial evaluations: 'no'
+#!     constraint properties: Hard Constraints, Box Constraints, Permutation Constraints
+#!     number of constraints: 2065 (as example, depends on problem size)
+#!     implementation languages: C++
+#!     approximate evaluation time: Roughly 1 second per evaluation for the smallest
+#!       considered design, and roughly 40 seconds for the larger designs we considered.
+# FIXME: Permutation Constraints not representable in ConstraintType; using multiple Constraint objects.
+things["impl_bso_toolbox"] = Implementation(
+    name="BSO-toolbox",
+    description="Building Spatial Design toolbox (TU/e)",
+    language="C++",
+    evaluation_time="~1s (smallest) to ~40s (larger)",
+    links=[Link(type="repository", url="https://github.com/TUe-excellent-buildings/BSO-toolbox")],
+)
+things["fn_building_spatial"] = Problem(
+    name="Building spatial design",
+    description="Optimise the spatial layout of a building to minimise energy consumption for climate control and minimise the strain on the structure. Many hard constraints; mixed-variable (continuous+binary); expensive evaluations.",
+    objectives={2},
+    variables=[
+        Variable(type="continuous", dim=ValueRange(min=1)),
+        Variable(type="binary", dim=ValueRange(min=1)),
+    ],
+    constraints=[
+        Constraint(hard="yes"),
+        Constraint(type="box", hard="yes"),
+    ],
+    allows_partial_evaluation="no",
+    source={"real-world"},
+    references=[
+        Reference(
+            title="Building spatial design",
+            authors=[],
+            link=Link(url="https://hdl.handle.net/1887/81789"),
+        )
+    ],
+    implementations={"impl_bso_toolbox"},
+)
+
+#! - name: Electric Motor Design Optimization
+#!   suite/generator/single: Single Problem
+#!   variable type: Continuous, Integer
+#!   dimensionality: '13'
+#!   objectives: '1'
+#!   constraints: 'yes'
+#!   dynamic: 'no'
+#!   noise: 'yes'
+#!   multimodal: 'yes'
+#!   multi-fidelity: 'no'
+#!   source (real-world/artificial): Real-World Application
+#!   implementation: Implementation not freely available
+#!   textual description: The goal is to find a design of a synchronous electric motor
+#!     for power steering systems that minimizes costs and satisfies all constraints.
+#!   reference: https://dis.ijs.si/tea/Publications/Tusar23Multistep.pdf (paper in Slovene)
+#!   other info:
+#!     partial evaluations: 'no'
+#!     full name: Electric Motor Design Optimization
+#!     constraint properties: Hard Constraints, Soft Constraints, Box Constraints
+#!     number of constraints: '12'
+#!     description of multimodality: Constraints are multimodal
+#!     key challenges / characteristics: Time-consuming solution evaluation, highly-constrained
+#!     scientific motivation: Challenging to find good solutions in a limited time
+#!     limitations: 'Unavailability ...'
+#!     implementation languages: Python
+#!     approximate evaluation time: 8 minutes
+#!     general: This is not an available problem, but could be interesting to show to
+#!       researchers which difficulties appear in real-world problems
+things["impl_emdo"] = Implementation(
+    name="Electric Motor Design Optimization",
+    description="Not publicly available",
+    language="Python",
+    evaluation_time="8 minutes",
+)
+things["fn_emdo"] = Problem(
+    name="Electric Motor Design Optimization",
+    long_name="Electric Motor Design Optimization",
+    description="""# Goal
+Find a design of a synchronous electric motor for power steering systems that minimizes costs and satisfies all constraints.
+
+# Motivation
+Challenging to find good solutions in a limited time.
+
+# Key Challenges
+* Time-consuming solution evaluation
+* Highly-constrained problem
+* Constraints are multimodal
+
+This is not an available problem, but could be interesting to show to researchers which difficulties appear in real-world problems.""",
+    objectives={1},
+    variables=[
+        Variable(type="continuous", dim=13),
+        Variable(type="integer", dim=13),
+    ],
+    constraints=[
+        Constraint(hard="yes", number=12),
+        Constraint(hard="some"),
+        Constraint(type="box", hard="yes"),
+    ],
+    noise_type={"noisy"},
+    modality={"multimodal"},
+    allows_partial_evaluation="no",
+    source={"real-world"},
+    references=[
+        Reference(
+            title="A Multi-Step Evaluation Process in Electric Motor Design",
+            authors=["Tea Tušar", "Peter Korošec", "Bogdan Filipič"],
+            link=Link(url="https://dis.ijs.si/tea/Publications/Tusar23Multistep.pdf"),
+        )
+    ],
+    implementations={"impl_emdo"},
+)
+
+#! - name: BONO-Bench
+#!   suite/generator/single: Generator
+#!   variable type: Continuous
+#!   dimensionality: scalable
+#!   objectives: '2'
+#!   constraints: 'no'
+#!   dynamic: 'no'
+#!   noise: 'no'
+#!   multimodal: 'yes'
+#!   multi-fidelity: 'no'
+#!   source (real-world/artificial): Artificially Generated
+#!   implementation: https://github.com/schaepermeier/bonobench
+#!   textual description: Bi-objective problem generator and suite with scalable continuous
+#!     decision space. Features complex problem properties (different types of multimodality
+#!     and challenges in decision and objective space) as well as Pareto front approximations
+#!     with error guarantees for the hypervolume and exact R2 indicators.
+#!   other info:
+#!     partial evaluations: 'no'
+#!     full name: Bi-objective Numerical Optimization Benchmark (BONO-Bench)
+#!     constraint properties: Box Constraints
+#!     implementation languages: Python
+things["impl_bonobench"] = Implementation(
+    name="BONO-Bench",
+    description="Bi-objective Numerical Optimization Benchmark (BONO-Bench)",
+    language="Python",
+    links=[Link(type="repository", url="https://github.com/schaepermeier/bonobench")],
+)
+things["gen_bono_bench"] = Generator(
+    name="BONO-Bench",
+    long_name="Bi-objective Numerical Optimization Benchmark",
+    description="Bi-objective problem generator and suite with scalable continuous decision space. Features complex problem properties and Pareto front approximations with error guarantees for the hypervolume and exact R2 indicators.",
+    objectives={2},
+    variables=[Variable(type="continuous", dim=ValueRange(min=1))],
+    constraints=[Constraint(type="box", hard="yes")],
+    modality={"multimodal"},
+    allows_partial_evaluation="no",
+    source={"artificial"},
+    implementations={"impl_bonobench"},
+)
+
+#! - name: RandOptGen
+#!   suite/generator/single: Generator
+#!   variable type: Continuous, Integer, Boolean
+#!   dimensionality: scalable
+#!   objectives: scalable
+#!   constraints: 'no'
+#!   dynamic: 'no'
+#!   noise: 'no'
+#!   multimodal: 'yes'
+#!   multi-fidelity: 'no'
+#!   source (real-world/artificial): Artificially Generated
+#!   implementation: https://github.com/MALEO-research-group/RandOptGen
+#!   textual description: 'RandOptGen: A Unified Random Problem Generator for Single-and
+#!     Multi-Objective Optimization Problems with Mixed-Variable Input Spaces'
+#!   other info:
+#!     partial evaluations: 'no'
+#!     full name: RandOptGen
+#!     implementation languages: Python
+#!     approximate evaluation time: milliseconds
+#!     links to usage examples: https://doi.org/10.1145/3712256.3726478
+things["impl_randoptgen"] = Implementation(
+    name="RandOptGen",
+    description="Unified Random Problem Generator for Single- and Multi-Objective Optimization with Mixed-Variable Input Spaces",
+    language="Python",
+    evaluation_time="milliseconds",
+    links=[
+        Link(type="repository", url="https://github.com/MALEO-research-group/RandOptGen"),
+        Link(type="example", url="https://doi.org/10.1145/3712256.3726478"),
+    ],
+)
+things["gen_randoptgen"] = Generator(
+    name="RandOptGen",
+    long_name="RandOptGen",
+    description="A Unified Random Problem Generator for Single- and Multi-Objective Optimization Problems with Mixed-Variable Input Spaces.",
+    # FIXME: original "scalable" - truncated to 1..10.
+    objectives=set(range(1, 11)),
+    variables=[
+        Variable(type="continuous", dim=ValueRange(min=1)),
+        Variable(type="integer", dim=ValueRange(min=1)),
+        Variable(type="binary", dim=ValueRange(min=1)),
+    ],
+    modality={"multimodal"},
+    allows_partial_evaluation="no",
+    source={"artificial"},
+    implementations={"impl_randoptgen"},
+)
+
+#! - name: CUTEr
+#!   suite/generator/single: Problem Suite
+#!   variable type: Continuous, Integer, Boolean
+#!   dimensionality: scalable
+#!   objectives: '1'
+#!   constraints: 'yes'
+#!   dynamic: 'no'
+#!   noise: 'no'
+#!   multimodal: Unknown
+#!   multi-fidelity: 'no'
+#!   source (real-world/artificial): Artificially Generated
+#!   implementation: Not Found
+#!   textual description: A constrained and unconstrained testing environment
+#!   reference: https://dl.acm.org/doi/10.1145/962437.962439
+#!   other info:
+#!     partial evaluations: 'no'
+# FIXME: implementation not found.
+things["suite_cuter"] = Suite(
+    name="CUTEr",
+    description="A constrained and unconstrained testing environment.",
+    objectives={1},
+    variables=[
+        Variable(type="continuous", dim=ValueRange(min=1)),
+        Variable(type="integer", dim=ValueRange(min=1)),
+        Variable(type="binary", dim=ValueRange(min=1)),
+    ],
+    constraints=[Constraint(hard="yes")],
+    allows_partial_evaluation="no",
+    source={"artificial"},
+    references=[
+        Reference(
+            title="CUTEr",
+            authors=[],
+            link=Link(url="https://dl.acm.org/doi/10.1145/962437.962439"),
+        )
+    ],
+)
+
+#! - name: CUTEst
+#!   suite/generator/single: Problem Suite
+#!   variable type: Continuous, Integer, Boolean
+#!   dimensionality: scalable
+#!   objectives: '1'
+#!   constraints: 'yes'
+#!   dynamic: 'no'
+#!   noise: 'no'
+#!   multimodal: 'yes'
+#!   multi-fidelity: 'no'
+#!   source (real-world/artificial): Artificially Generated
+#!   implementation: https://github.com/jfowkes/pycutest
+#!   textual description: The Constrained and Unconstrained Testing Environment with
+#!     safe threads (CUTEst) for optimization software
+#!   reference: https://link.springer.com/article/10.1007/s10589-014-9687-3
+#!   other info:
+#!     partial evaluations: 'no'
+#!     full name: 'Constrained and Unconstrained Testing Environment with safe threads '
+#!     constraint properties: Soft Constraints, Box Constraints
+#!     number of constraints: scalable
+#!     implementation languages: Python, C++, Fortran
+#!     general: 'Python implementation: https://github.com/jfowkes/pycutest'
+things["impl_pycutest"] = Implementation(
+    name="pycutest",
+    description="Python interface to CUTEst",
+    language="Python / C++ / Fortran",
+    links=[Link(type="repository", url="https://github.com/jfowkes/pycutest")],
+)
+things["suite_cutest"] = Suite(
+    name="CUTEst",
+    long_name="Constrained and Unconstrained Testing Environment with safe threads",
+    description="CUTEst for optimization software",
+    objectives={1},
+    variables=[
+        Variable(type="continuous", dim=ValueRange(min=1)),
+        Variable(type="integer", dim=ValueRange(min=1)),
+        Variable(type="binary", dim=ValueRange(min=1)),
+    ],
+    constraints=[
+        Constraint(hard="some", number=ValueRange(min=1)),
+        Constraint(type="box", hard="yes"),
+    ],
+    modality={"multimodal"},
+    allows_partial_evaluation="no",
+    source={"artificial"},
+    references=[
+        Reference(
+            title="CUTEst",
+            authors=[],
+            link=Link(url="https://link.springer.com/article/10.1007/s10589-014-9687-3"),
+        )
+    ],
+    implementations={"impl_pycutest"},
+)
+
+#! - name: PUBOi
+#!   suite/generator/single: Generator
+#!   variable type: Boolean
+#!   dimensionality: scalable
+#!   objectives: '1'
+#!   constraints: 'no'
+#!   dynamic: 'no'
+#!   noise: 'no'
+#!   multimodal: Unknown
+#!   multi-fidelity: 'no'
+#!   source (real-world/artificial): Artificially Generated
+#!   implementation: https://gitlab.com/verel/pubo-importance-benchmark
+#!   textual description: A benchmark in which variable importance is tunable, based
+#!     on the Walsh function
+#!   reference: https://link.springer.com/chapter/10.1007/978-3-031-04148-8_12
+#!   other info:
+#!     partial evaluations: 'no'
+#!     full name: Polynomial Unconstrained Binary Optimization
+#!     key challenges / characteristics: Tunable variable importance
+#!     implementation languages: Python, C++
+things["impl_puboi"] = Implementation(
+    name="PUBO Importance Benchmark",
+    description="A benchmark in which variable importance is tunable, based on the Walsh function",
+    language="Python / C++",
+    links=[Link(type="repository", url="https://gitlab.com/verel/pubo-importance-benchmark")],
+)
+
+things["gen_puboi"] = Generator(
+    name="PUBOi",
+    long_name="Polynomial Unconstrained Binary Optimization with tunable importance",
+    description="A benchmark in which variable importance is tunable, based on the Walsh function.",
+    objectives={1},
+    variables=[Variable(type="binary", dim=ValueRange(min=1))],
+    allows_partial_evaluation="no",
+    source={"artificial"},
+    references=[
+        Reference(
+            title="PUBOi",
+            authors=[],
+            link=Link(url="https://link.springer.com/chapter/10.1007/978-3-031-04148-8_12"),
+        )
+    ],
+    implementations={"impl_puboi"},
+)
+
+
+library = Library(things)
+
+# Make sure model is really valid
+Library.model_validate(library)
+
+if __name__ == "__main__":
+    print(to_yaml_str(library))
diff --git a/problems.yaml b/problems.yaml
index 4328038..24caa52 100644
--- a/problems.yaml
+++ b/problems.yaml
@@ -1,1264 +1,3496 @@
-- name: BBOB
-  suite/generator/single: suite
-  objectives: '1'
-  dimensionality: scalable
-  variable type: continuous
-  constraints: 'no'
-  dynamic: 'no'
-  noise: 'no'
-  multimodal: 'yes'
-  multi-fidelity: 'no'
-  reference: https://doi.org/10.1080/10556788.2020.1808977
-  implementation: https://github.com/numbbo/coco
-  source (real-world/artificial): ''
-  textual description: ''
-- name: BBOB-biobj
-  suite/generator/single: suite
-  objectives: '2'
-  dimensionality: 2-40
-  variable type: continuous
-  constraints: 'no'
-  dynamic: 'no'
-  noise: 'no'
-  multimodal: 'yes'
-  multi-fidelity: 'no'
-  reference: https://doi.org/10.48550/arXiv.1604.00359
-  implementation: https://github.com/numbbo/coco
-  source (real-world/artificial): ''
-  textual description: ''
-- name: BBOB-noisy
-  suite/generator/single: suite
-  objectives: '1'
-  dimensionality: scalable
-  variable type: continuous
-  constraints: 'no'
-  dynamic: 'no'
-  noise: 'yes'
-  multimodal: 'yes'
-  multi-fidelity: 'no'
-  reference: https://hal.inria.fr/inria-00369466
-  implementation: https://web.archive.org/web/20210416065610/https://coco.gforge.inria.fr/doku.php?id=downloads
-  source (real-world/artificial): ''
-  textual description: ''
-- name: BBOB-largescale
-  suite/generator/single: suite
-  objectives: '1'
-  dimensionality: 20-640
-  variable type: continuous
-  constraints: 'no'
-  dynamic: 'no'
-  noise: 'no'
-  multimodal: 'yes'
-  multi-fidelity: 'no'
-  reference: https://doi.org/10.48550/arXiv.1903.06396
-  implementation: https://github.com/numbbo/coco
-  source (real-world/artificial): ''
-  textual description: ''
-- name: BBOB-mixint
-  suite/generator/single: suite
-  objectives: '1'
-  dimensionality: 5-160
-  variable type: integer;continuous;mixed
-  constraints: 'no'
-  dynamic: 'no'
-  noise: 'no'
-  multimodal: 'yes'
-  multi-fidelity: 'no'
-  reference: https://doi.org/10.1145/3321707.3321868
-  implementation: https://github.com/numbbo/coco
-  source (real-world/artificial): ''
-  textual description: ''
-- name: BBOB-biobj-mixint
-  suite/generator/single: suite
-  objectives: '2'
-  dimensionality: 5-160
-  variable type: integer;continuous;mixed
-  constraints: 'no'
-  dynamic: 'no'
-  noise: 'no'
-  multimodal: 'yes'
-  multi-fidelity: 'no'
-  reference: https://doi.org/10.1145/3321707.3321868
-  implementation: https://github.com/numbbo/coco
-  source (real-world/artificial): ''
-  textual description: ''
-- name: BBOB-constrained
-  suite/generator/single: suite
-  objectives: '1'
-  dimensionality: 2-40
-  variable type: continuous
-  constraints: 'yes'
-  dynamic: 'no'
-  noise: 'no'
-  multimodal: 'yes'
-  multi-fidelity: 'no'
-  reference: http://numbbo.github.io/coco-doc/bbob-constrained/
-  implementation: https://github.com/numbbo/coco
-  source (real-world/artificial): ''
-  textual description: ''
-- name: MOrepo
-  suite/generator/single: suite
-  objectives: '2'
-  dimensionality: '?'
-  variable type: combinatorial
-  constraints: '?'
-  dynamic: '?'
-  noise: '?'
-  multimodal: '?'
-  multi-fidelity: 'no'
-  reference: ''
-  implementation: https://github.com/MCDMSociety/MOrepo
-  source (real-world/artificial): ''
-  textual description: ''
-- name: ZDT
-  suite/generator/single: suite
-  objectives: '2'
-  dimensionality: scalable
-  variable type: continuous;binary
-  constraints: 'no'
-  dynamic: 'no'
-  noise: 'no'
-  multimodal: '?'
-  multi-fidelity: 'no'
-  reference: https://doi.org/10.1162/106365600568202
-  implementation: https://github.com/anyoptimization/pymoo
-  source (real-world/artificial): ''
-  textual description: ''
-- name: DTLZ
-  suite/generator/single: suite
-  objectives: 2+
-  dimensionality: scalable
-  variable type: continuous
-  constraints: 'no'
-  dynamic: 'no'
-  noise: 'no'
-  multimodal: '?'
-  multi-fidelity: 'no'
-  reference: https://doi.org/10.1109/CEC.2002.1007032
-  implementation: https://pymoo.org/problems/many/dtlz.html
-  source (real-world/artificial): ''
-  textual description: ''
-- name: WFG
-  suite/generator/single: suite
-  objectives: 2+
-  dimensionality: scalable
-  variable type: continuous
-  constraints: 'no'
-  dynamic: 'no'
-  noise: 'no'
-  multimodal: '?'
-  multi-fidelity: 'no'
-  reference: https://doi.org/10.1109/TEVC.2005.861417
-  implementation: https://pymoo.org/problems/many/wfg.html
-  source (real-world/artificial): ''
-  textual description: ''
-- name: CDMP
-  suite/generator/single: suite
-  objectives: 2+
-  dimensionality: scalable
-  variable type: continuous
-  constraints: 'yes'
-  dynamic: '?'
-  noise: '?'
-  multimodal: '?'
-  multi-fidelity: 'no'
-  reference: https://doi.org/10.1145/3321707.3321878
-  implementation: '?'
-  source (real-world/artificial): ''
-  textual description: ''
-- name: SDP
-  suite/generator/single: suite
-  objectives: 2+
-  dimensionality: scalable
-  variable type: continuous
-  constraints: 'no'
-  dynamic: 'yes'
-  noise: '?'
-  multimodal: '?'
-  multi-fidelity: 'no'
-  reference: https://doi.org/10.1109/TCYB.2019.2896021
-  implementation: '?'
-  source (real-world/artificial): ''
-  textual description: ''
-- name: MaOP
-  suite/generator/single: suite
-  objectives: 2+
-  dimensionality: scalable
-  variable type: continuous
-  constraints: 'no'
-  dynamic: 'no'
-  noise: '?'
-  multimodal: '?'
-  multi-fidelity: 'no'
-  reference: https://doi.org/10.1016/j.swevo.2019.02.003
-  implementation: '?'
-  source (real-world/artificial): ''
-  textual description: ''
-- name: BP
-  suite/generator/single: suite
-  objectives: 2+
-  dimensionality: scalable
-  variable type: continuous
-  constraints: 'no'
-  dynamic: 'no'
-  noise: '?'
-  multimodal: '?'
-  multi-fidelity: 'no'
-  reference: https://doi.org/10.1109/CEC.2019.8790277
-  implementation: '?'
-  source (real-world/artificial): ''
-  textual description: ''
-- name: GPD
-  suite/generator/single: generator
-  objectives: 2+
-  dimensionality: scalable
-  variable type: continuous
-  constraints: optional
-  dynamic: 'no'
-  noise: optional
-  multimodal: '?'
-  multi-fidelity: 'no'
-  reference: https://doi.org/10.1016/j.asoc.2020.106139
-  implementation: '?'
-  source (real-world/artificial): ''
-  textual description: ''
-- name: ETMOF
-  suite/generator/single: suite
-  objectives: 2-50
-  dimensionality: 25-10000
-  variable type: continuous
-  constraints: 'no'
-  dynamic: 'yes'
-  noise: 'no'
-  multimodal: '?'
-  multi-fidelity: 'no'
-  reference: https://doi.org/10.48550/arXiv.2110.08033
-  implementation: https://github.com/songbai-liu/etmo
-  source (real-world/artificial): ''
-  textual description: ''
-- name: MMOPP
-  suite/generator/single: suite
-  objectives: 2-7
-  dimensionality: '?'
-  variable type: '?'
-  constraints: 'yes'
-  dynamic: 'no'
-  noise: 'no'
-  multimodal: 'yes'
-  multi-fidelity: 'no'
-  reference: http://www5.zzu.edu.cn/system/_content/download.jsp?urltype=news.DownloadAttachUrl&owner=1327567121&wbfileid=4764412
-  implementation: http://www5.zzu.edu.cn/ecilab/info/1036/1251.htm
-  source (real-world/artificial): ''
-  textual description: ''
-- name: CFD
-  suite/generator/single: suite
-  objectives: 1-2
-  dimensionality: scalable
-  variable type: '?'
-  constraints: 'yes'
-  dynamic: 'no'
-  noise: 'no'
-  multimodal: '?'
-  multi-fidelity: 'no'
-  reference: https://doi.org/10.1007/978-3-319-99259-4_24
-  implementation: https://bitbucket.org/arahat/cfd-test-problem-suite
-  source (real-world/artificial): real world
-  textual description: expensive evaluations 30s-15m
-- name: GBEA
-  suite/generator/single: suite
-  objectives: 1-2
-  dimensionality: scalable
-  variable type: continuous
-  constraints: 'no'
-  dynamic: 'no'
-  noise: 'yes'
-  multimodal: 'yes'
-  multi-fidelity: 'no'
-  reference: https://doi.org/10.1145/3321707.3321805
-  implementation: 'https://github.com/ttusar/coco-gbea'
-  source (real-world/artificial): real world
-  textual description: 'expensive evaluations 5s-35s, RW-GAN-Mario and TopTrumps are part of GBEA'
-- name: Car structure
-  suite/generator/single: suite
-  objectives: '2'
-  dimensionality: 144-222
-  variable type: discrete
-  constraints: 'yes'
-  dynamic: 'no'
-  noise: 'no'
-  multimodal: '?'
-  multi-fidelity: 'no'
-  reference: https://doi.org/10.1145/3205651.3205702
-  implementation: http://ladse.eng.isas.jaxa.jp/benchmark/
-  source (real-world/artificial): real world
-  textual description: 54 constraints
-- name: EMO2017
-  suite/generator/single: suite
-  objectives: '2'
-  dimensionality: 4-24
-  variable type: continuous
-  constraints: 'no'
-  dynamic: 'no'
-  noise: 'no'
-  multimodal: '?'
-  multi-fidelity: 'no'
-  reference: https://www.ini.rub.de/PEOPLE/glasmtbl/projects/bbcomp/
-  implementation: https://www.ini.rub.de/PEOPLE/glasmtbl/projects/bbcomp/downloads/realworld-problems-bbcomp-EMO-2017.zip
-  source (real-world/artificial): real world
-  textual description: ''
-- name: JSEC2019
-  suite/generator/single: single
-  objectives: 1-5
-  dimensionality: '32'
-  variable type: continuous
-  constraints: 'yes'
-  dynamic: 'no'
-  noise: 'no'
-  multimodal: '?'
-  multi-fidelity: 'no'
-  reference: http://www.jpnsec.org/files/competition2019/EC-Symposium-2019-Competition-English.html
-  implementation: http://www.jpnsec.org/files/competition2019/EC-Symposium-2019-Competition-English.html
-  source (real-world/artificial): real world
-  textual description: expensive evaluations 3s; 22 constraints
-- name: RE
-  suite/generator/single: suite
-  objectives: 2-9
-  dimensionality: 2-7
-  variable type: continuous;integer;mixed
-  constraints: 'no'
-  dynamic: 'no'
-  noise: 'no'
-  multimodal: '?'
-  multi-fidelity: 'no'
-  reference: https://doi.org/10.1016/j.asoc.2020.106078
-  implementation: https://github.com/ryojitanabe/reproblems
-  source (real-world/artificial): real world like
-  textual description: ''
-- name: CRE
-  suite/generator/single: suite
-  objectives: 2-5
-  dimensionality: 3-7
-  variable type: continuous;integer;mixed
-  constraints: 'yes'
-  dynamic: 'no'
-  noise: 'no'
-  multimodal: '?'
-  multi-fidelity: 'no'
-  reference: https://doi.org/10.1016/j.asoc.2020.106078
-  implementation: https://github.com/ryojitanabe/reproblems
-  source (real-world/artificial): real world like
-  textual description: ''
-- name: Radar waveform
-  suite/generator/single: single
-  objectives: '9'
-  dimensionality: 4-12
-  variable type: integer
-  constraints: 'yes'
-  dynamic: 'no'
-  noise: 'no'
-  multimodal: '?'
-  multi-fidelity: 'no'
-  reference: https://doi.org/10.1007/978-3-540-70928-2_53
-  implementation: http://code.evanhughes.org/
-  source (real-world/artificial): real world
-  textual description: ''
-- name: MF2
-  suite/generator/single: suite
-  objectives: '1'
-  dimensionality: 1-n
-  variable type: continuous
-  constraints: 'no'
-  dynamic: 'no'
-  noise: 'no'
-  multimodal: '?'
-  multi-fidelity: 'yes'
-  reference: https://doi.org/10.21105/joss.02049
-  implementation: https://github.com/sjvrijn/mf2
-  source (real-world/artificial): ''
-  textual description: ''
-- name: AMVOP
-  suite/generator/single: suite
-  objectives: '1'
-  dimensionality: scalable
-  variable type: mixed continuous+ordinal+categorical+both
-  constraints: 'no'
-  dynamic: 'no'
-  noise: 'no'
-  multimodal: 'yes'
-  multi-fidelity: 'no'
-  reference: https://doi.org/10.1109/TEVC.2013.2281531
-  implementation: '?'
-  source (real-world/artificial): ''
-  textual description: ''
-- name: RWMVOP
-  suite/generator/single: suite
-  objectives: '1'
-  dimensionality: scalable
-  variable type: continuous;mixed continuous+ordinal+categorical+both
-  constraints: 'yes'
-  dynamic: 'no'
-  noise: 'no'
-  multimodal: '?'
-  multi-fidelity: 'no'
-  reference: https://doi.org/10.1109/TEVC.2013.2281531
-  implementation: '?'
-  source (real-world/artificial): real world
-  textual description: ''
-- name: SBOX-COST
-  suite/generator/single: suite
-  objectives: '1'
-  dimensionality: scalable
-  variable type: continuous
-  constraints: 'no'
-  dynamic: 'no'
-  noise: 'no'
-  multimodal: 'yes'
-  multi-fidelity: 'no'
-  reference: https://doi.org/10.48550/arXiv.2305.12221
-  implementation: https://github.com/IOHprofiler/IOHexperimenter/
-  source (real-world/artificial): ''
-  textual description: problems from BBOB but allows instances with the optimum close to the
-    boundary
-- name: "\u03C1MNK-Landscapes"
-  suite/generator/single: generator
-  objectives: scalable
-  dimensionality: scalable
-  variable type: binary
-  constraints: 'no'
-  dynamic: 'no'
-  noise: 'no'
-  multimodal: 'yes'
-  multi-fidelity: 'no'
-  reference: https://doi.org/10.1016/j.ejor.2012.12.019
-  implementation: https://gitlab.com/aliefooghe/mocobench/
-  source (real-world/artificial): ''
-  textual description: tunable variable and objective dimensions; tunable multimodality and
+fn_ato:
+  allows_partial_evaluation: no
+  can_evaluate_objectives_independently: null
+  code_examples: null
+  constraints: null
+  description: 'Parameters of the Modules of the Automatic Train Operation are optimized;
+    two objectives: minimizing energy consumption and minimizing driving duration.'
+  dynamic_type: null
+  fidelity_levels: null
+  implementations: null
+  instances: null
+  long_name: null
+  modality:
+  - unimodal
+  name: ATO
+  noise_type: null
+  objectives:
+  - 2
+  references: null
+  source:
+  - real-world
+  tags: null
+  type: problem
+  variables:
+  - dim: 10
+    type: continuous
+fn_building_spatial:
+  allows_partial_evaluation: no
+  can_evaluate_objectives_independently: null
+  code_examples: null
+  constraints:
+  - equality: null
+    hard: yes
+    number: null
+    type: box
+  - equality: null
+    hard: yes
+    number: null
+    type: unknown
+  description: Optimise the spatial layout of a building to minimise energy 
+    consumption for climate control and minimise the strain on the structure. 
+    Many hard constraints; mixed-variable (continuous+binary); expensive 
+    evaluations.
+  dynamic_type: null
+  fidelity_levels: null
+  implementations:
+  - impl_bso_toolbox
+  instances: null
+  long_name: null
+  modality: null
+  name: Building spatial design
+  noise_type: null
+  objectives:
+  - 2
+  references:
+  - authors: []
+    link:
+      type: null
+      url: https://hdl.handle.net/1887/81789
+    title: Building spatial design
+  source:
+  - real-world
+  tags: null
+  type: problem
+  variables:
+  - dim:
+      max: null
+      min: 1
+    type: binary
+  - dim:
+      max: null
+      min: 1
+    type: continuous
+fn_convex_dtlz2:
+  allows_partial_evaluation: null
+  can_evaluate_objectives_independently: null
+  code_examples: null
+  constraints: null
+  description: Variant of DTLZ2 with a convex Pareto front (instead of concave)
+  dynamic_type: null
+  fidelity_levels: null
+  implementations: null
+  instances: null
+  long_name: null
+  modality: null
+  name: Convex DTLZ2
+  noise_type: null
+  objectives:
+  - 2
+  - 3
+  - 4
+  - 5
+  - 6
+  - 7
+  - 8
+  - 9
+  - 10
+  references:
+  - authors: []
+    link:
+      type: null
+      url: https://doi.org/10.1109/TEVC.2013.2281535
+    title: Convex DTLZ2
+  source: null
+  tags: null
+  type: problem
+  variables:
+  - dim:
+      max: null
+      min: 1
+    type: continuous
+fn_emdo:
+  allows_partial_evaluation: no
+  can_evaluate_objectives_independently: null
+  code_examples: null
+  constraints:
+  - equality: null
+    hard: yes
+    number: 12
+    type: unknown
+  - equality: null
+    hard: yes
+    number: null
+    type: box
+  - equality: null
+    hard: some
+    number: null
+    type: unknown
+  description: "# Goal\nFind a design of a synchronous electric motor for power steering
+    systems that minimizes costs and satisfies all constraints.\n\n# Motivation\n\
+    Challenging to find good solutions in a limited time.\n\n# Key Challenges\n* Time-consuming
+    solution evaluation\n* Highly-constrained problem\n* Constraints are multimodal\n\
+    \nThis is not an available problem, but could be interesting to show to researchers
+    which difficulties appear in real-world problems."
+  dynamic_type: null
+  fidelity_levels: null
+  implementations:
+  - impl_emdo
+  instances: null
+  long_name: Electric Motor Design Optimization
+  modality:
+  - multimodal
+  name: Electric Motor Design Optimization
+  noise_type:
+  - noisy
+  objectives:
+  - 1
+  references:
+  - authors:
+    - Tea Tušar
+    - Peter Korošec
+    - Bogdan Filipič
+    link:
+      type: null
+      url: https://dis.ijs.si/tea/Publications/Tusar23Multistep.pdf
+    title: A Multi-Step Evaluation Process in Electric Motor Design
+  source:
+  - real-world
+  tags: null
+  type: problem
+  variables:
+  - dim: 13
+    type: integer
+  - dim: 13
+    type: continuous
+fn_fleetopt:
+  allows_partial_evaluation: yes
+  can_evaluate_objectives_independently: null
+  code_examples: null
+  constraints:
+  - equality: null
+    hard: yes
+    number: null
+    type: unknown
+  description: 'UK healthcare organisation fleet optimisation: reduce the fleet of
+    non-emergency healthcare trip vehicles while still ensuring all trips can be covered.
+    Bilevel: upper level 54 vars, lower level 13208 vars.'
+  dynamic_type: null
+  fidelity_levels: null
+  implementations: null
+  instances: null
+  long_name: null
+  modality: null
+  name: FleetOpt
+  noise_type: null
+  objectives:
+  - 1
+  references:
+  - authors: []
+    link:
+      type: null
+      url: https://dl.acm.org/doi/abs/10.1145/3638530.3664137
+    title: FleetOpt
+  source:
+  - real-world
+  tags: null
+  type: problem
+  variables:
+  - dim:
+    - 13208
+    - 54
+    type: integer
+fn_gasoline:
+  allows_partial_evaluation: null
+  can_evaluate_objectives_independently: null
+  code_examples: null
+  constraints:
+  - equality: null
+    hard: yes
+    number: 5
+    type: unknown
+  description: Multi-objective optimization to minimize fuel consumption and NOx
+    emissions over a two-minute dynamic duty cycle, subject to five constraints 
+    (turbine inlet temperature, knock occurrences, peak cylinder pressure, peak 
+    cylinder pressure rise, total work). Seven decision variables cover hardware
+    choices and engine control parameters.
+  dynamic_type: null
+  fidelity_levels:
+  - 1
+  - 2
+  implementations:
+  - impl_gasoline
+  instances: null
+  long_name: null
+  modality: null
+  name: Gasoline direct injection engine design
+  noise_type: null
+  objectives:
+  - 2
+  references:
+  - authors: []
+    link:
+      type: null
+      url: https://doi.org/10.1016/j.ejor.2022.08.032
+    title: Gasoline direct injection engine design
+  source:
+  - real-world
+  tags: null
+  type: problem
+  variables:
+  - dim: 7
+    type: integer
+  - dim: 7
+    type: continuous
+fn_invdeceptive_deceptive_rotell:
+  allows_partial_evaluation: null
+  can_evaluate_objectives_independently: null
+  code_examples: null
+  constraints: null
+  description: null
+  dynamic_type: null
+  fidelity_levels: null
+  implementations: null
+  instances: null
+  long_name: null
+  modality: null
+  name: InverseDeceptiveTrap+RotatedEllipsoid / DeceptiveTrap+RotatedEllipsoid
+  noise_type: null
+  objectives:
+  - 2
+  references:
+  - authors: []
+    link:
+      type: null
+      url: https://doi.org/10.1145/3449726.3459521
+    title: Mixed-variable multi-objective test problems
+  source:
+  - artificial
+  tags: null
+  type: problem
+  variables:
+  - dim:
+      max: null
+      min: 1
+    type: binary
+  - dim:
+      max: null
+      min: 1
+    type: continuous
+fn_inverted_dtlz1:
+  allows_partial_evaluation: null
+  can_evaluate_objectives_independently: null
+  code_examples: null
+  constraints: null
+  description: Variant of DTLZ1 with an inverted Pareto front
+  dynamic_type: null
+  fidelity_levels: null
+  implementations: null
+  instances: null
+  long_name: null
+  modality: null
+  name: Inverted DTLZ1
+  noise_type: null
+  objectives:
+  - 2
+  - 3
+  - 4
+  - 5
+  - 6
+  - 7
+  - 8
+  - 9
+  - 10
+  references:
+  - authors: []
+    link:
+      type: null
+      url: https://doi.org/10.1109/TEVC.2013.2281534
+    title: Inverted DTLZ1
+  source: null
+  tags: null
+  type: problem
+  variables:
+  - dim:
+      max: null
+      min: 1
+    type: continuous
+fn_jsec2019:
+  allows_partial_evaluation: null
+  can_evaluate_objectives_independently: null
+  code_examples: null
+  constraints:
+  - equality: null
+    hard: yes
+    number: 22
+    type: unknown
+  description: expensive evaluations 3s; 22 constraints
+  dynamic_type: null
+  fidelity_levels: null
+  implementations:
+  - impl_jsec2019
+  instances: null
+  long_name: null
+  modality: null
+  name: JSEC2019
+  noise_type: null
+  objectives:
+  - 1
+  - 2
+  - 3
+  - 4
+  - 5
+  references:
+  - authors: []
+    link:
+      type: null
+      url: 
+        http://www.jpnsec.org/files/competition2019/EC-Symposium-2019-Competition-English.html
+    title: JPNSEC EC-Symposium 2019 competition
+  source:
+  - real-world
+  tags: null
+  type: problem
+  variables:
+  - dim: 32
+    type: continuous
+fn_onemax_sphere_deceptive_rotell:
+  allows_partial_evaluation: null
+  can_evaluate_objectives_independently: null
+  code_examples: null
+  constraints: null
+  description: null
+  dynamic_type: null
+  fidelity_levels: null
+  implementations: null
+  instances: null
+  long_name: null
+  modality: null
+  name: Onemax+Sphere / DeceptiveTrap+RotatedEllipsoid
+  noise_type: null
+  objectives:
+  - 2
+  references:
+  - authors: []
+    link:
+      type: null
+      url: https://doi.org/10.1145/3449726.3459521
+    title: Mixed-variable multi-objective test problems
+  source:
+  - artificial
+  tags: null
+  type: problem
+  variables:
+  - dim:
+      max: null
+      min: 1
+    type: binary
+  - dim:
+      max: null
+      min: 1
+    type: continuous
+fn_onemax_sphere_zeromax_sphere:
+  allows_partial_evaluation: null
+  can_evaluate_objectives_independently: null
+  code_examples: null
+  constraints: null
+  description: null
+  dynamic_type: null
+  fidelity_levels: null
+  implementations: null
+  instances: null
+  long_name: null
+  modality: null
+  name: Onemax+Sphere / Zeromax+Sphere
+  noise_type: null
+  objectives:
+  - 2
+  references:
+  - authors: []
+    link:
+      type: null
+      url: https://doi.org/10.1145/3449726.3459521
+    title: Onemax+Sphere / Zeromax+Sphere
+  source:
+  - artificial
+  tags: null
+  type: problem
+  variables:
+  - dim:
+      max: null
+      min: 1
+    type: binary
+  - dim:
+      max: null
+      min: 1
+    type: continuous
+fn_radar_waveform:
+  allows_partial_evaluation: null
+  can_evaluate_objectives_independently: null
+  code_examples: null
+  constraints:
+  - equality: null
+    hard: yes
+    number: null
+    type: unknown
+  description: null
+  dynamic_type: null
+  fidelity_levels: null
+  implementations:
+  - impl_radar_waveform
+  instances: null
+  long_name: null
+  modality: null
+  name: Radar waveform
+  noise_type: null
+  objectives:
+  - 9
+  references:
+  - authors: []
+    link:
+      type: null
+      url: https://doi.org/10.1007/978-3-540-70928-2_53
+    title: Radar waveform design
+  source:
+  - real-world
+  tags: null
+  type: problem
+  variables:
+  - dim:
+      max: 12
+      min: 4
+    type: integer
+gen_beacon:
+  allows_partial_evaluation: no
+  can_evaluate_objectives_independently: null
+  code_examples: null
+  constraints:
+  - equality: null
+    hard: yes
+    number: 0
+    type: box
+  description: Generator for bi-objective benchmark problems with explicitly 
+    controlled correlations in continuous spaces. Multimodal with random 
+    structure.
+  dynamic_type: null
+  fidelity_levels: null
+  implementations:
+  - impl_beacon
+  long_name: Continuous Bi-objective Benchmark problems with Explicit Adjustable
+    COrrelatioN control
+  modality:
+  - multimodal
+  name: BEACON
+  noise_type: null
+  objectives:
+  - 2
+  references:
+  - authors: []
+    link:
+      type: null
+      url: https://dl.acm.org/doi/10.1145/3712255.3734303
+    title: BEACON
+  source:
+  - artificial
+  tags: null
+  type: generator
+  variables:
+  - dim:
+      max: null
+      min: 1
+    type: continuous
+gen_bono_bench:
+  allows_partial_evaluation: no
+  can_evaluate_objectives_independently: null
+  code_examples: null
+  constraints:
+  - equality: null
+    hard: yes
+    number: null
+    type: box
+  description: Bi-objective problem generator and suite with scalable continuous
+    decision space. Features complex problem properties and Pareto front 
+    approximations with error guarantees for the hypervolume and exact R2 
+    indicators.
+  dynamic_type: null
+  fidelity_levels: null
+  implementations:
+  - impl_bonobench
+  long_name: Bi-objective Numerical Optimization Benchmark
+  modality:
+  - multimodal
+  name: BONO-Bench
+  noise_type: null
+  objectives:
+  - 2
+  references: null
+  source:
+  - artificial
+  tags: null
+  type: generator
+  variables:
+  - dim:
+      max: null
+      min: 1
+    type: continuous
+gen_ealain:
+  allows_partial_evaluation: null
+  can_evaluate_objectives_independently: null
+  code_examples: null
+  constraints:
+  - equality: null
+    hard: some
+    number: null
+    type: unknown
+  description: Real-world-like, easily extensible to increase complexity
+  dynamic_type:
+  - optional
+  fidelity_levels:
+  - 1
+  - 2
+  implementations:
+  - impl_ealain
+  long_name: null
+  modality: null
+  name: Ealain
+  noise_type: null
+  objectives:
+  - 1
+  - 2
+  - 3
+  - 4
+  - 5
+  - 6
+  - 7
+  - 8
+  - 9
+  - 10
+  references:
+  - authors: []
+    link:
+      type: null
+      url: https://doi.org/10.1145/3638530.3654299
+    title: Ealain
+  source:
+  - real-world-like
+  tags: null
+  type: generator
+  variables:
+  - dim:
+      max: null
+      min: 1
+    type: binary
+  - dim:
+      max: null
+      min: 1
+    type: integer
+  - dim:
+      max: null
+      min: 1
+    type: continuous
+gen_gnbg:
+  allows_partial_evaluation: null
+  can_evaluate_objectives_independently: null
+  code_examples: null
+  constraints: null
+  description: Generator counterpart of GNBG.
+  dynamic_type: null
+  fidelity_levels: null
+  implementations:
+  - impl_gnbg
+  long_name: null
+  modality: null
+  name: GNBG
+  noise_type: null
+  objectives:
+  - 1
+  references:
+  - authors: []
+    link:
+      type: null
+      url: https://arxiv.org/abs/2312.07083
+    title: GNBG
+  source:
+  - artificial
+  tags: null
+  type: generator
+  variables:
+  - dim:
+      max: null
+      min: 1
+    type: continuous
+gen_gnbg_ii:
+  allows_partial_evaluation: null
+  can_evaluate_objectives_independently: null
+  code_examples: null
+  constraints: null
+  description: Generator counterpart of GNBG-II.
+  dynamic_type: null
+  fidelity_levels: null
+  implementations:
+  - impl_iohgnbg
+  - impl_gnbg_ii
+  long_name: null
+  modality: null
+  name: GNBG-II
+  noise_type: null
+  objectives:
+  - 1
+  references:
+  - authors: []
+    link:
+      type: null
+      url: https://dl.acm.org/doi/pdf/10.1145/3712255.3734271
+    title: GNBG-II
+  source:
+  - artificial
+  tags: null
+  type: generator
+  variables:
+  - dim:
+      max: null
+      min: 1
+    type: continuous
+gen_gpd:
+  allows_partial_evaluation: null
+  can_evaluate_objectives_independently: null
+  code_examples: null
+  constraints:
+  - equality: null
+    hard: some
+    number: null
+    type: unknown
+  description: null
+  dynamic_type: null
+  fidelity_levels: null
+  implementations: null
+  long_name: null
+  modality: null
+  name: GPD
+  noise_type:
+  - optional
+  objectives:
+  - 2
+  - 3
+  - 4
+  - 5
+  - 6
+  - 7
+  - 8
+  - 9
+  - 10
+  references:
+  - authors: []
+    link:
+      type: null
+      url: https://doi.org/10.1016/j.asoc.2020.106139
+    title: GPD generator
+  source: null
+  tags: null
+  type: generator
+  variables:
+  - dim:
+      max: null
+      min: 1
+    type: continuous
+gen_iohclustering:
+  allows_partial_evaluation: null
+  can_evaluate_objectives_independently: null
+  code_examples: null
+  constraints: null
+  description: Generator counterpart of the IOHClustering suite.
+  dynamic_type: null
+  fidelity_levels: null
+  implementations:
+  - impl_iohclustering
+  long_name: null
+  modality:
+  - multimodal
+  name: IOHClustering
+  noise_type: null
+  objectives:
+  - 1
+  references:
+  - authors: []
+    link:
+      type: null
+      url: https://arxiv.org/pdf/2505.09233
+    title: IOHClustering
+  source:
+  - artificial-from-real-data
+  tags: null
+  type: generator
+  variables:
+  - dim:
+      max: null
+      min: 1
+    type: continuous
+gen_ma_bbob:
+  allows_partial_evaluation: null
+  can_evaluate_objectives_independently: null
+  code_examples: null
+  constraints: null
+  description: Generator that creates affine combinations of BBOB functions
+  dynamic_type: null
+  fidelity_levels: null
+  implementations:
+  - impl_iohexperimenter
+  - impl_ma_bbob
+  long_name: null
+  modality:
+  - multimodal
+  name: MA-BBOB
+  noise_type: null
+  objectives:
+  - 1
+  references:
+  - authors: []
+    link:
+      type: null
+      url: https://doi.org/10.1145/3673908
+    title: MA-BBOB
+  source:
+  - artificial
+  tags: null
+  type: generator
+  variables:
+  - dim:
+      max: null
+      min: 1
+    type: continuous
+gen_mpm2:
+  allows_partial_evaluation: null
+  can_evaluate_objectives_independently: null
+  code_examples: null
+  constraints: null
+  description: nonlinear nonseparable nonsymmetric; scalable in terms of time to
+    evaluate the objective function
+  dynamic_type: null
+  fidelity_levels: null
+  implementations:
+  - impl_mpm2
+  long_name: null
+  modality:
+  - multimodal
+  name: MPM2
+  noise_type: null
+  objectives:
+  - 1
+  references:
+  - authors: []
+    link:
+      type: null
+      url: https://ls11-www.cs.tu-dortmund.de/_media/techreports/tr15-01.pdf
+    title: MPM2 technical report TR15-01
+  source: null
+  tags: null
+  type: generator
+  variables:
+  - dim:
+      max: null
+      min: 1
+    type: continuous
+gen_mubqp:
+  allows_partial_evaluation: null
+  can_evaluate_objectives_independently: null
+  code_examples: null
+  constraints: null
+  description: tunable variable and objective dimensions; tunable density and 
     correlation between objectives
-- name: mUBQP
-  suite/generator/single: generator
-  objectives: scalable
-  dimensionality: scalable
-  variable type: binary
-  constraints: 'no'
-  dynamic: 'no'
-  noise: 'no'
-  multimodal: yes (quadratic)
-  multi-fidelity: 'no'
-  reference: https://doi.org/10.1016/j.asoc.2013.11.008
-  implementation: https://gitlab.com/aliefooghe/mocobench/
-  source (real-world/artificial): ''
-  textual description: tunable variable and objective dimensions; tunable density and correlation
-    between objectives
-- name: "\u03C1mTSP"
-  suite/generator/single: generator
-  objectives: scalable
-  dimensionality: scalable
-  variable type: permutations
-  constraints: no (apart from being permutations)
-  dynamic: 'no'
-  noise: 'no'
-  multimodal: yes (quadratic)
-  multi-fidelity: 'no'
-  reference: https://doi.org/10.1007/978-3-319-45823-6_40
-  implementation: https://gitlab.com/aliefooghe/mocobench/
-  source (real-world/artificial): ''
-  textual description: tunable variable and objective dimensions; tunable instance type (euclidian/random);
-    tunable correlation between objectives
-- name: CEC2015-DMOO
-  suite/generator/single: suite
-  objectives: 2-3
-  dimensionality: '?'
-  variable type: continuous
-  constraints: '?'
-  dynamic: 'yes'
-  noise: 'no'
-  multimodal: '?'
-  multi-fidelity: 'no'
-  reference: Benchmark Functions for CEC 2015 Special Session and Competition on Dynamic
-    Multi-objective Optimization
-  implementation: ''
-  source (real-world/artificial): ''
-  textual description: ''
-- name: Ealain
-  suite/generator/single: generator
-  objectives: 1+
-  dimensionality: scalable
-  variable type: continuous,binary,integer
-  constraints: optional
-  dynamic: optional
-  noise: 'no'
-  multimodal: '?'
-  multi-fidelity: optional
-  reference: https://doi.org/10.1145/3638530.3654299
-  implementation: https://github.com/qrenau/Ealain
-  source (real-world/artificial): Real-world-like
-  textual description: Real-world-like, easily extensible to increase complexity
-- name: MA-BBOB
-  suite/generator/single: generator
-  objectives: '1'
-  dimensionality: scalable
-  variable type: continuous
-  constraints: 'no'
-  dynamic: 'no'
-  noise: 'no'
-  multimodal: 'yes'
-  multi-fidelity: 'no'
-  reference: https://doi.org/10.1145/3673908
-  implementation: https://github.com/IOHprofiler/IOHexperimenter/blob/master/example/Competitions/MA-BBOB/Example_MABBOB.ipynb
-  source (real-world/artificial): artificial
-  textual description: Generator that creates affine combinations of BBOB functions
-- name: MPM2
-  suite/generator/single: generator
-  objectives: '1'
-  dimensionality: scalable
-  variable type: continuous
-  constraints: 'no'
-  dynamic: 'no'
-  noise: 'no'
-  multimodal: 'yes'
-  multi-fidelity: 'no'
-  reference: https://ls11-www.cs.tu-dortmund.de/_media/techreports/tr15-01.pdf
-  implementation: https://github.com/jakobbossek/smoof/blob/master/inst/mpm2.py
-  source (real-world/artificial): ''
-  textual description: nonlinear nonseparable nonsymmetric; scalable in terms of time to evaluate
-    the objective function
-- name: Convex DTLZ2
-  suite/generator/single: single
-  objectives: 2+
-  dimensionality: scalable
-  variable type: continuous
-  constraints: 'no'
-  dynamic: 'no'
-  noise: 'no'
-  multimodal: '?'
-  multi-fidelity: 'no'
-  reference: https://doi.org/10.1109/TEVC.2013.2281535
-  implementation: '?'
-  source (real-world/artificial): ''
-  textual description: Variant of DTLZ2 with a convex Pareto front (instead of concave)
-- name: Inverted DTLZ1
-  suite/generator/single: single
-  objectives: 2+
-  dimensionality: scalable
-  variable type: continuous
-  constraints: 'no'
-  dynamic: 'no'
-  noise: 'no'
-  multimodal: '?'
-  multi-fidelity: 'no'
-  reference: https://doi.org/10.1109/TEVC.2013.2281534
-  implementation: '?'
-  source (real-world/artificial): ''
-  textual description: Variant of DTLZ1 with an inverted Pareto front
-- name: Minus DTLZ
-  suite/generator/single: suite
-  objectives: 2+
-  dimensionality: scalable
-  variable type: continuous
-  constraints: 'no'
-  dynamic: 'no'
-  noise: 'no'
-  multimodal: '?'
-  multi-fidelity: 'no'
-  reference: https://doi.org/10.1109/TEVC.2016.2587749
-  implementation: '?'
-  source (real-world/artificial): ''
-  textual description: Variant of DTLZ that minimises the inverse of the base DTLZ functions
-- name: Minus WFG
-  suite/generator/single: suite
-  objectives: 2+
-  dimensionality: scalable
-  variable type: continuous
-  constraints: 'no'
-  dynamic: 'no'
-  noise: 'no'
-  multimodal: '?'
-  multi-fidelity: 'no'
-  reference: https://doi.org/10.1109/TEVC.2016.2587749
-  implementation: '?'
-  source (real-world/artificial): ''
-  textual description: Variant of WFG that minimises the inverse of the base WFG functions
-- name: L1-ZDT
-  suite/generator/single: suite
-  objectives: '2'
-  dimensionality: scalable
-  variable type: continuous;binary
-  constraints: 'no'
-  dynamic: 'no'
-  noise: 'no'
-  multimodal: '?'
-  multi-fidelity: 'no'
-  reference: https://doi.org/10.1145/1143997.1144179
-  implementation: '?'
-  source (real-world/artificial): ''
-  textual description: Variant of ZDT with linkages between variables within one of two groups
-    but not between variables in a different group; Linear recombination operators
-    can potentially take advantage of the problem structure
-- name: L2-ZDT
-  suite/generator/single: suite
-  objectives: '2'
-  dimensionality: scalable
-  variable type: continuous;binary
-  constraints: 'no'
-  dynamic: 'no'
-  noise: 'no'
-  multimodal: '?'
-  multi-fidelity: 'no'
-  reference: https://doi.org/10.1145/1143997.1144179
-  implementation: '?'
-  source (real-world/artificial): ''
-  textual description: Variant of ZDT with linkages between all variables; Linear recombination
-    operators can potentially take advantage of the problem structure
-- name: L3-ZDT
-  suite/generator/single: suite
-  objectives: '2'
-  dimensionality: scalable
-  variable type: continuous;binary
-  constraints: 'no'
-  dynamic: 'no'
-  noise: 'no'
-  multimodal: '?'
-  multi-fidelity: 'no'
-  reference: https://doi.org/10.1145/1143997.1144179
-  implementation: '?'
-  source (real-world/artificial): ''
-  textual description: Variant of L2-ZDT using a mapping to prevent linear recombination operators
-    from potentially taking advantage of the problem structure
-- name: L2-DTLZ
-  suite/generator/single: suite
-  objectives: 2+
-  dimensionality: scalable
-  variable type: continuous
-  constraints: 'no'
-  dynamic: 'no'
-  noise: 'no'
-  multimodal: '?'
-  multi-fidelity: 'no'
-  reference: https://doi.org/10.1145/1143997.1144179
-  implementation: '?'
-  source (real-world/artificial): ''
-  textual description: Variant of DTLZ2 and DTLZ3 with linkages between all variables; Linear
-    recombination operators can potentially take advantage of the problem structure
-- name: L3-DTLZ
-  suite/generator/single: suite
-  objectives: 2+
-  dimensionality: scalable
-  variable type: continuous
-  constraints: 'no'
-  dynamic: 'no'
-  noise: 'no'
-  multimodal: '?'
-  multi-fidelity: 'no'
-  reference: https://doi.org/10.1145/1143997.1144179
-  implementation: '?'
-  source (real-world/artificial): ''
-  textual description: Variant of L2-DTLZ using a mapping to prevent linear recombination operators
-    from potentially taking advantage of the problem structure
-- name: CEC2018 DT - CEC2018 Competition on Dynamic Multiobjective Optimisation
-  suite/generator/single: suite
-  objectives: 2 or 3
-  dimensionality: scalable?
-  variable type: '?'
-  constraints: 'no'
-  dynamic: 'yes'
-  noise: 'no'
-  multimodal: '?'
-  multi-fidelity: 'no'
-  reference: https://www.academia.edu/download/94499025/TR-CEC2018-DMOP-Competition.pdf
-  implementation: https://pymoo.org/problems/dynamic/df.html
-  source (real-world/artificial): artificial
-  textual description: '14 problems. Time-dependent: Pareto front/Pareto set geometry;
-    irregular Pareto front shapes; variable-linkage; number of disconnected Pareto
-    front segments; etc.'
-- name: MODAct - multiobjective design of actuators
-  suite/generator/single: suite
-  objectives: 2 3 4 or 5
-  dimensionality: '20'
-  variable type: mixed; integer and continuous
-  constraints: 'yes'
-  dynamic: 'no'
-  noise: 'no'
-  multimodal: '?'
-  multi-fidelity: 'no'
-  reference: https://doi.org/10.1109/TEVC.2020.3020046
-  implementation: https://pymoo.org/problems/constrained/modact.html
-  source (real-world/artificial): real-world
-  textual description: Realistic Constrained Multi-Objective Optimization Benchmark
-    Problems from Design. Need the https://github.com/epfl-lamd/modact package installed; evaluation
-    times around 20ms
-- name: IOHClustering
-  suite/generator/single: suite; generator
-  objectives: '1'
-  dimensionality: scalable
-  variable type: continuous
-  constraints: 'no'
-  dynamic: 'no'
-  noise: 'no'
-  multimodal: 'yes'
-  multi-fidelity: 'no '
-  reference: https://arxiv.org/pdf/2505.09233
-  implementation: https://github.com/IOHprofiler/IOHClustering
-  source (real-world/artificial): artificial, but based on real data
-  textual description: 'Set of benchmark problems from clustering: optimization task
-    is selecting cluster centers for a given set of data, with the number of clusters
-    defining problem dimensionality. Includes both a suite and a generator. Based on ML clustering datasets'
-- name: GNBG-II
-  suite/generator/single: suite; generator
-  objectives: '1'
-  dimensionality: scalable
-  variable type: continuous
-  constraints: 'no'
-  dynamic: 'no'
-  noise: 'no'
-  multimodal: '?'
-  multi-fidelity: 'no'
-  reference: https://dl.acm.org/doi/pdf/10.1145/3712255.3734271
-  implementation: https://github.com/rohitsalgotra/GNBG-II
-  source (real-world/artificial): artificial
-  textual description: Generalized Numerical Benchmark Generator (version 2). Also in IOH https://github.com/IOHprofiler/IOHGNBG
-- name: GNBG
-  suite/generator/single: suite; generator
-  objectives: '1'
-  dimensionality: scalable
-  variable type: continuous
-  constraints: 'no'
-  dynamic: 'no'
-  noise: 'no'
-  multimodal: '?'
-  multi-fidelity: 'no'
-  reference: https://arxiv.org/abs/2312.07083
-  implementation: https://github.com/Danial-Yazdani/GNBG-Generator
-  source (real-world/artificial): artificial
-  textual description: Generalized Numerical Benchmark Generator
-- name: DynamicBinVal
-  suite/generator/single: suite
-  objectives: '1'
-  dimensionality: scalable
-  variable type: binary
-  constraints: 'no'
-  dynamic: 'yes'
-  noise: 'no'
-  multimodal: '?'
-  multi-fidelity: 'no'
-  reference: https://arxiv.org/pdf/2404.15837
-  implementation: https://github.com/IOHprofiler/IOHexperimenter
-  source (real-world/artificial): artificial
-  textual description: Four versions of the dynamic binary value problem
-- name: PBO
-  suite/generator/single: suite
-  objectives: '1'
-  dimensionality: scalable
-  variable type: binary
-  constraints: 'no'
-  dynamic: 'no'
-  noise: 'no'
-  multimodal: '?'
-  multi-fidelity: 'no'
-  reference: https://dl.acm.org/doi/pdf/10.1145/3319619.3326810
-  implementation: https://github.com/IOHprofiler/IOHexperimenter
-  source (real-world/artificial): artificial
-  textual description: Suite of 25 binary optimization problems
-- name: W-model
-  suite/generator/single: generator
-  objectives: '1'
-  dimensionality: scalable
-  variable type: binary
-  constraints: 'no'
-  dynamic: 'no'
-  noise: 'no'
-  multimodal: '?'
-  multi-fidelity: 'no'
-  reference: https://dl.acm.org/doi/abs/10.1145/3205651.3208240?casa_token=S4U_Pi9f6MwAAAAA:U9ztNTPwmupT8K3GamWZfBL7-8fqjxPtr_kprv51vdwA-REsp0EyOFGa99BtbANb0XbqyrVg795hIw
-  implementation: https://github.com/thomasWeise/BBDOB_W_Model
-  source (real-world/artificial): artificial
-  textual description: Tunable generator for binary optimization based on several
+  dynamic_type: null
+  fidelity_levels: null
+  implementations:
+  - impl_mocobench
+  long_name: null
+  modality:
+  - multimodal
+  - quadratic
+  name: mUBQP
+  noise_type: null
+  objectives:
+  - 1
+  - 2
+  - 3
+  - 4
+  - 5
+  - 6
+  - 7
+  - 8
+  - 9
+  - 10
+  references:
+  - authors: []
+    link:
+      type: null
+      url: https://doi.org/10.1016/j.asoc.2013.11.008
+    title: mUBQP benchmark
+  source: null
+  tags: null
+  type: generator
+  variables:
+  - dim:
+      max: null
+      min: 1
+    type: binary
+gen_puboi:
+  allows_partial_evaluation: no
+  can_evaluate_objectives_independently: null
+  code_examples: null
+  constraints: null
+  description: A benchmark in which variable importance is tunable, based on the
+    Walsh function.
+  dynamic_type: null
+  fidelity_levels: null
+  implementations:
+  - impl_puboi
+  long_name: Polynomial Unconstrained Binary Optimization with tunable 
+    importance
+  modality: null
+  name: PUBOi
+  noise_type: null
+  objectives:
+  - 1
+  references:
+  - authors: []
+    link:
+      type: null
+      url: https://link.springer.com/chapter/10.1007/978-3-031-04148-8_12
+    title: PUBOi
+  source:
+  - artificial
+  tags: null
+  type: generator
+  variables:
+  - dim:
+      max: null
+      min: 1
+    type: binary
+gen_randoptgen:
+  allows_partial_evaluation: no
+  can_evaluate_objectives_independently: null
+  code_examples: null
+  constraints: null
+  description: A Unified Random Problem Generator for Single- and 
+    Multi-Objective Optimization Problems with Mixed-Variable Input Spaces.
+  dynamic_type: null
+  fidelity_levels: null
+  implementations:
+  - impl_randoptgen
+  long_name: RandOptGen
+  modality:
+  - multimodal
+  name: RandOptGen
+  noise_type: null
+  objectives:
+  - 1
+  - 2
+  - 3
+  - 4
+  - 5
+  - 6
+  - 7
+  - 8
+  - 9
+  - 10
+  references: null
+  source:
+  - artificial
+  tags: null
+  type: generator
+  variables:
+  - dim:
+      max: null
+      min: 1
+    type: binary
+  - dim:
+      max: null
+      min: 1
+    type: integer
+  - dim:
+      max: null
+      min: 1
+    type: continuous
+gen_rho_mnk_landscapes:
+  allows_partial_evaluation: null
+  can_evaluate_objectives_independently: null
+  code_examples: null
+  constraints: null
+  description: tunable variable and objective dimensions; tunable multimodality 
+    and correlation between objectives
+  dynamic_type: null
+  fidelity_levels: null
+  implementations:
+  - impl_mocobench
+  long_name: null
+  modality:
+  - multimodal
+  name: ρMNK-Landscapes
+  noise_type: null
+  objectives:
+  - 1
+  - 2
+  - 3
+  - 4
+  - 5
+  - 6
+  - 7
+  - 8
+  - 9
+  - 10
+  references:
+  - authors: []
+    link:
+      type: null
+      url: https://doi.org/10.1016/j.ejor.2012.12.019
+    title: On the design of multi-objective evolutionary algorithms based on 
+      NK-landscapes
+  source: null
+  tags: null
+  type: generator
+  variables:
+  - dim:
+      max: null
+      min: 1
+    type: binary
+gen_rho_mtsp:
+  allows_partial_evaluation: null
+  can_evaluate_objectives_independently: null
+  code_examples: null
+  constraints: null
+  description: tunable variable and objective dimensions; tunable instance type 
+    (euclidean/random); tunable correlation between objectives
+  dynamic_type: null
+  fidelity_levels: null
+  implementations:
+  - impl_mocobench
+  long_name: null
+  modality:
+  - multimodal
+  - quadratic
+  name: ρmTSP
+  noise_type: null
+  objectives:
+  - 1
+  - 2
+  - 3
+  - 4
+  - 5
+  - 6
+  - 7
+  - 8
+  - 9
+  - 10
+  references:
+  - authors: []
+    link:
+      type: null
+      url: https://doi.org/10.1007/978-3-319-45823-6_40
+    title: On the impact of multi-objective scalability for the ρmTSP
+  source: null
+  tags: null
+  type: generator
+  variables:
+  - dim:
+      max: null
+      min: 1
+    type: unknown
+gen_wmodel:
+  allows_partial_evaluation: null
+  can_evaluate_objectives_independently: null
+  code_examples: null
+  constraints: null
+  description: Tunable generator for binary optimization based on several 
     difficulty features
-- name: Submodular Optimitzation
-  suite/generator/single: suite
-  objectives: '1'
-  dimensionality: scalable
-  variable type: binary
-  constraints: 'no'
-  dynamic: 'no'
-  noise: 'no'
-  multimodal: '?'
-  multi-fidelity: 'no'
-  reference: https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10254181
-  implementation: https://github.com/IOHprofiler/IOHexperimenter
-  source (real-world/artificial): artificial
-  textual description: set of graph-based submodular optimization problems from 4
-    problem types
-- name: CEC2013
-  suite/generator/single: suite
-  objectives: '1'
-  dimensionality: scalable
-  variable type: continuous
-  constraints: 'no'
-  dynamic: 'no'
-  noise: 'no'
-  multimodal: '?'
-  multi-fidelity: 'no'
-  reference: https://peerj.com/articles/cs-2671/CEC2013.pdf
-  implementation: https://github.com/P-N-Suganthan/CEC2013
-  source (real-world/artificial): artificial
-  textual description: suite used for cec2013 competition. Also in IOH https://github.com/IOHprofiler/IOHexperimenter
-- name: CEC2022
-  suite/generator/single: suite
-  objectives: '1'
-  dimensionality: scalable
-  variable type: continuous
-  constraints: 'no'
-  dynamic: 'no'
-  noise: 'no'
-  multimodal: '?'
-  multi-fidelity: 'no'
-  reference: https://github.com/P-N-Suganthan/2022-SO-BO/blob/main/CEC2022%20TR.pdf
-  implementation: https://github.com/P-N-Suganthan/2022-SO-BO
-  source (real-world/artificial): artificial
-  textual description: suite used for cec2022 competition. Also in IOH https://github.com/IOHprofiler/IOHexperimenter
-- name: Onemax+Sphere / Zeromax+Sphere
-  suite/generator/single: single
-  objectives: '2'
-  dimensionality: scalable
-  variable type: binary and continuous;mixed;
-  constraints: 'no'
-  dynamic: 'no'
-  noise: 'no'
-  multimodal: ''
-  multi-fidelity: 'no'
-  reference: https://doi.org/10.1145/3449726.3459521
-  implementation:
-  source (real-world/artificial): 'artificial'
-  textual description: ''
-- name: Onemax+Sphere / DeceptiveTrap+RotatedEllipsoid
-  suite/generator/single: single
-  objectives: '2'
-  dimensionality: scalable
-  variable type: binary and continuous;mixed;
-  constraints: 'no'
-  dynamic: 'no'
-  noise: 'no'
-  multimodal: ''
-  multi-fidelity: 'no'
-  reference: https://doi.org/10.1145/3449726.3459521
-  implementation:
-  source (real-world/artificial): 'artificial'
-  textual description: ''
-- name: InverseDeceptiveTrap+RotatedEllipsoid / DeceptiveTrap+RotatedEllipsoid
-  suite/generator/single: single
-  objectives: '2'
-  dimensionality: scalable
-  variable type: binary and continuous;mixed;
-  constraints: 'no'
-  dynamic: 'no'
-  noise: 'no'
-  multimodal: ''
-  multi-fidelity: 'no'
-  reference: https://doi.org/10.1145/3449726.3459521
-  implementation:
-  source (real-world/artificial): 'artificial'
-  textual description: ''
-- name: PorkchopPlotInterplanetaryTrajectory
-  suite/generator/single: suite
-  objectives: '1'
-  dimensionality: 2
-  variable type: continuous
-  constraints: 'no'
-  dynamic: 'no'
-  noise: 'no'
-  multimodal: 'yes'
-  multi-fidelity: 'no'
-  reference: https://doi.org/10.1109/CEC65147.2025.11042973
-  implementation: https://github.com/ShuaiqunPan/Transfer_Random_forests_BBOB_Real_world
-  source (real-world/artificial): 'real-world'
-  textual description: ''
-- name: KinematicsRobotArm
-  suite/generator/single: suite
-  objectives: '1'
-  dimensionality: 21
-  variable type: continuous
-  constraints: 'no'
-  dynamic: 'no'
-  noise: 'no'
-  multimodal: 'no'
-  multi-fidelity: 'no'
-  reference: https://doi.org/10.1023/A:1013258808932
-  implementation: https://github.com/ShuaiqunPan/Transfer_Random_forests_BBOB_Real_world
-  source (real-world/artificial): 'real-world'
-  textual description: ''
-- name:  VehicleDynamics
-  suite/generator/single: suite
-  objectives: '1'
-  dimensionality: 2
-  variable type: continuous
-  constraints: 'no'
-  dynamic: 'no'
-  noise: 'no'
-  multimodal: 'yes'
-  multi-fidelity: 'no'
-  reference: https://www.scitepress.org/Papers/2023/121580/121580.pdf
-  implementation: https://zenodo.org/records/8307853
-  source (real-world/artificial): 'real-world'
-  textual description: ''
-- name: MECHBench
-  suite/generator/single: Problem Suite
-  variable type: Continuous
-  dimensionality: scalable'
-  objectives: '1'
-  constraints: 'yes'
-  dynamic: 'no'
-  noise: 'no'
-  multimodal: 'yes'
-  multi-fidelity: 'no'
-  source (real-world/artificial): Real-World Application
-  implementation: https://github.com/BayesOptApp/MECHBench
-  textual description: This is a set of problems with inspiration from Structural
-    Mechanics Design Optimization. The suite comprises three physical models, from
-    which the user may define different kind of problems which impact the final design
-    output.
-  reference: https://arxiv.org/abs/2511.10821
-  other info:
-    partial evaluations: 'no'
-    full name: MECHBench
-    constraint properties: Hard Constraints
-    number of constraints: 1 or 2
-    description of multimodality: Unstructured or non isotropic multimodality
-    key challenges / characteristics: Embeds physical simulations and is flexible
-      and modular
-    scientific motivation: Bridge the black-box optimization techniques to a Mechanical
-      Design Problem which require these kinds of algorithms
-    limitations: The models do not include fracture or damage mechanics, just plasticity.
-    implementation languages: Python
-    approximate evaluation time: Times -> from 1 minute to 7 minutes
-- name: EXPObench
-  suite/generator/single: Problem Suite
-  variable type: Continuous, Integer, Categorical, Conditional
-  dimensionality: 10 to 135
-  objectives: '1'
-  constraints: 'yes'
-  dynamic: 'no'
-  noise: 'yes'
-  multimodal: Unknown
-  multi-fidelity: 'no'
-  source (real-world/artificial): Real-World Application
-  implementation: https://github.com/AlgTUDelft/ExpensiveOptimBenchmark
-  textual description: Wind farm layout optimization, gas filter design, pipe shape
+  dynamic_type: null
+  fidelity_levels: null
+  implementations:
+  - impl_wmodel
+  long_name: null
+  modality: null
+  name: W-model
+  noise_type: null
+  objectives:
+  - 1
+  references:
+  - authors: []
+    link:
+      type: null
+      url: https://dl.acm.org/doi/abs/10.1145/3205651.3208240
+    title: W-model
+  source:
+  - artificial
+  tags: null
+  type: generator
+  variables:
+  - dim:
+      max: null
+      min: 1
+    type: binary
+impl_beacon:
+  description: Continuous Bi-objective Benchmark with Explicit Adjustable 
+    COrrelatioN control
+  evaluation_time: negligible
+  language: Python
+  links:
+  - type: repository
+    url: https://github.com/Stebbet/BEACON/
+  name: BEACON
+  requirements: null
+  type: implementation
+impl_bonobench:
+  description: Bi-objective Numerical Optimization Benchmark (BONO-Bench)
+  evaluation_time: null
+  language: Python
+  links:
+  - type: repository
+    url: https://github.com/schaepermeier/bonobench
+  name: BONO-Bench
+  requirements: null
+  type: implementation
+impl_bso_toolbox:
+  description: Building Spatial Design toolbox (TU/e)
+  evaluation_time: ~1s (smallest) to ~40s (larger)
+  language: C++
+  links:
+  - type: repository
+    url: https://github.com/TUe-excellent-buildings/BSO-toolbox
+  name: BSO-toolbox
+  requirements: null
+  type: implementation
+impl_car_structure:
+  description: JAXA LADSE benchmark problems
+  evaluation_time: null
+  language: null
+  links:
+  - type: website
+    url: http://ladse.eng.isas.jaxa.jp/benchmark/
+  name: Car-structure benchmark
+  requirements: null
+  type: implementation
+impl_cec2013:
+  description: Suganthan's reference implementation
+  evaluation_time: null
+  language: null
+  links:
+  - type: repository
+    url: https://github.com/P-N-Suganthan/CEC2013
+  name: CEC2013 reference code
+  requirements: null
+  type: implementation
+impl_cec2022:
+  description: Suganthan's reference implementation
+  evaluation_time: null
+  language: null
+  links:
+  - type: repository
+    url: https://github.com/P-N-Suganthan/2022-SO-BO
+  name: CEC2022 reference code
+  requirements: null
+  type: implementation
+impl_cfd:
+  description: Expensive real-world CFD-based test problems
+  evaluation_time: 30s-15m
+  language: null
+  links:
+  - type: repository
+    url: https://bitbucket.org/arahat/cfd-test-problem-suite
+  name: CFD test problem suite
+  requirements: null
+  type: implementation
+impl_coco:
+  description: 'Comparing Continuous Optimizers: black-box optimization benchmarking
+    platform'
+  evaluation_time: null
+  language: C/Python
+  links:
+  - type: repository
+    url: https://github.com/numbbo/coco
+  name: COCO framework
+  requirements: null
+  type: implementation
+impl_coco_legacy:
+  description: Archived COCO download page that hosted the bbob-noisy suite
+  evaluation_time: null
+  language: C/Python
+  links:
+  - type: archive
+    url: 
+      https://web.archive.org/web/20210416065610/https://coco.gforge.inria.fr/doku.php?id=downloads
+  name: COCO legacy (bbob-noisy)
+  requirements: null
+  type: implementation
+impl_ealain:
+  description: Real-world-like extensible benchmark problem generator
+  evaluation_time: null
+  language: null
+  links:
+  - type: repository
+    url: https://github.com/qrenau/Ealain
+  name: Ealain
+  requirements: null
+  type: implementation
+impl_emdo:
+  description: Not publicly available
+  evaluation_time: 8 minutes
+  language: Python
+  links: null
+  name: Electric Motor Design Optimization
+  requirements: null
+  type: implementation
+impl_emo2017:
+  description: BBComp EMO-2017 real-world problem archive
+  evaluation_time: null
+  language: null
+  links:
+  - type: download
+    url: 
+      https://www.ini.rub.de/PEOPLE/glasmtbl/projects/bbcomp/downloads/realworld-problems-bbcomp-EMO-2017.zip
+  name: EMO 2017 real-world problems
+  requirements: null
+  type: implementation
+impl_etmof:
+  description: Evolutionary many-task optimization framework
+  evaluation_time: null
+  language: null
+  links:
+  - type: repository
+    url: https://github.com/songbai-liu/etmo
+  name: ETMOF
+  requirements: null
+  type: implementation
+impl_expobench:
+  description: EXPensive Optimization benchmark library (wind farm layout, gas 
+    filter design, pipe shape, hyperparameter tuning, hospital simulation)
+  evaluation_time: 2 to 80 seconds
+  language: Python
+  links:
+  - type: repository
+    url: https://github.com/AlgTUDelft/ExpensiveOptimBenchmark
+  name: EXPObench
+  requirements: null
+  type: implementation
+impl_gasoline:
+  description: Proprietary Matlab Simulink + Wave RT co-simulation
+  evaluation_time: null
+  language: Matlab Simulink / Wave RT
+  links:
+  - type: paper
+    url: https://doi.org/10.1016/j.ejor.2022.08.032
+  name: Gasoline direct injection engine design
+  requirements: null
+  type: implementation
+impl_gbea:
+  description: Game-Benchmark for Evolutionary Algorithms (COCO fork)
+  evaluation_time: 5s-35s
+  language: null
+  links:
+  - type: repository
+    url: https://github.com/ttusar/coco-gbea
+  name: coco-gbea
+  requirements: null
+  type: implementation
+impl_gnbg:
+  description: Generalized Numerical Benchmark Generator
+  evaluation_time: null
+  language: null
+  links:
+  - type: repository
+    url: https://github.com/Danial-Yazdani/GNBG-Generator
+  name: GNBG Generator
+  requirements: null
+  type: implementation
+impl_gnbg_ii:
+  description: Generalized Numerical Benchmark Generator version 2
+  evaluation_time: null
+  language: null
+  links:
+  - type: repository
+    url: https://github.com/rohitsalgotra/GNBG-II
+  name: GNBG-II
+  requirements: null
+  type: implementation
+impl_iohclustering:
+  description: Clustering-based optimization benchmark built on ML datasets
+  evaluation_time: null
+  language: null
+  links:
+  - type: repository
+    url: https://github.com/IOHprofiler/IOHClustering
+  name: IOHClustering
+  requirements: null
+  type: implementation
+impl_iohexperimenter:
+  description: IOHprofiler experimenter framework
+  evaluation_time: null
+  language: C++/Python
+  links:
+  - type: repository
+    url: https://github.com/IOHprofiler/IOHexperimenter
+  name: IOHexperimenter
+  requirements: null
+  type: implementation
+impl_iohgnbg:
+  description: IOHprofiler version of GNBG
+  evaluation_time: null
+  language: null
+  links:
+  - type: repository
+    url: https://github.com/IOHprofiler/IOHGNBG
+  name: IOHGNBG
+  requirements: null
+  type: implementation
+impl_jsec2019:
+  description: JPNSEC EC-Symposium 2019 competition problem
+  evaluation_time: 3s
+  language: null
+  links:
+  - type: website
+    url: 
+      http://www.jpnsec.org/files/competition2019/EC-Symposium-2019-Competition-English.html
+  name: JSEC 2019 competition
+  requirements: null
+  type: implementation
+impl_ma_bbob:
+  description: Example notebook for MA-BBOB in IOHexperimenter
+  evaluation_time: null
+  language: null
+  links:
+  - type: example
+    url: 
+      https://github.com/IOHprofiler/IOHexperimenter/blob/master/example/Competitions/MA-BBOB/Example_MABBOB.ipynb
+  name: MA-BBOB (IOHexperimenter)
+  requirements: null
+  type: implementation
+impl_mechbench:
+  description: Structural mechanics design optimization benchmark
+  evaluation_time: 1-7 minutes
+  language: Python
+  links:
+  - type: repository
+    url: https://github.com/BayesOptApp/MECHBench
+  name: MECHBench
+  requirements: null
+  type: implementation
+impl_mf2:
+  description: Multi-fidelity test function collection
+  evaluation_time: null
+  language: Python
+  links:
+  - type: repository
+    url: https://github.com/sjvrijn/mf2
+  name: mf2
+  requirements: null
+  type: implementation
+impl_mmopp:
+  description: ECI lab distribution page for MMOPP
+  evaluation_time: null
+  language: null
+  links:
+  - type: website
+    url: http://www5.zzu.edu.cn/ecilab/info/1036/1251.htm
+  name: MMOPP
+  requirements: null
+  type: implementation
+impl_mocobench:
+  description: Multi-objective combinatorial optimization benchmark
+  evaluation_time: null
+  language: C++
+  links:
+  - type: repository
+    url: https://gitlab.com/aliefooghe/mocobench/
+  name: mocobench
+  requirements: null
+  type: implementation
+impl_modact:
+  description: EPFL-LAMD modact package
+  evaluation_time: 20ms
+  language: null
+  links:
+  - type: repository
+    url: https://github.com/epfl-lamd/modact
+  name: modact
+  requirements: null
+  type: implementation
+impl_morepo:
+  description: Multi-objective optimisation problem repository
+  evaluation_time: null
+  language: null
+  links:
+  - type: repository
+    url: https://github.com/MCDMSociety/MOrepo
+  name: MOrepo
+  requirements: null
+  type: implementation
+impl_mpm2:
+  description: Python implementation of MPM2 distributed with smoof
+  evaluation_time: null
+  language: Python
+  links:
+  - type: source
+    url: https://github.com/jakobbossek/smoof/blob/master/inst/mpm2.py
+  name: MPM2 (smoof)
+  requirements: null
+  type: implementation
+impl_puboi:
+  description: A benchmark in which variable importance is tunable, based on the
+    Walsh function
+  evaluation_time: null
+  language: Python / C++
+  links:
+  - type: repository
+    url: https://gitlab.com/verel/pubo-importance-benchmark
+  name: PUBO Importance Benchmark
+  requirements: null
+  type: implementation
+impl_pycutest:
+  description: Python interface to CUTEst
+  evaluation_time: null
+  language: Python / C++ / Fortran
+  links:
+  - type: repository
+    url: https://github.com/jfowkes/pycutest
+  name: pycutest
+  requirements: null
+  type: implementation
+impl_pymoo:
+  description: Multi-objective optimization in Python
+  evaluation_time: null
+  language: Python
+  links:
+  - type: repository
+    url: https://github.com/anyoptimization/pymoo
+  name: pymoo
+  requirements: null
+  type: implementation
+impl_radar_waveform:
+  description: Radar waveform design reference implementation
+  evaluation_time: null
+  language: null
+  links:
+  - type: website
+    url: http://code.evanhughes.org/
+  name: Evan Hughes radar waveform code
+  requirements: null
+  type: implementation
+impl_randoptgen:
+  description: Unified Random Problem Generator for Single- and Multi-Objective 
+    Optimization with Mixed-Variable Input Spaces
+  evaluation_time: milliseconds
+  language: Python
+  links:
+  - type: repository
+    url: https://github.com/MALEO-research-group/RandOptGen
+  - type: example
+    url: https://doi.org/10.1145/3712256.3726478
+  name: RandOptGen
+  requirements: null
+  type: implementation
+impl_reproblems:
+  description: Real-world inspired multi-objective optimization problem suite
+  evaluation_time: null
+  language: Python
+  links:
+  - type: repository
+    url: https://github.com/ryojitanabe/reproblems
+  name: reproblems
+  requirements: null
+  type: implementation
+impl_transfer_rf_bbob_rw:
+  description: Real-world BBOB-like problem implementations (Porkchop, 
+    KinematicsRobotArm)
+  evaluation_time: null
+  language: null
+  links:
+  - type: repository
+    url: https://github.com/ShuaiqunPan/Transfer_Random_forests_BBOB_Real_world
+  name: Transfer Random Forests BBOB Real-world
+  requirements: null
+  type: implementation
+impl_tulipa:
+  description: Large linear program for optimal investment and operation of 
+    energy systems
+  evaluation_time: minutes to hours
+  language: Julia / JuMP
+  links:
+  - type: website
+    url: https://tulipaenergy.github.io/TulipaEnergyModel.jl/stable/
+  - type: example
+    url: https://github.com/TulipaEnergy/Tulipa-OBZ-CaseStudy
+  name: TulipaEnergyModel.jl
+  requirements: null
+  type: implementation
+impl_vehicle_dynamics:
+  description: Zenodo archive for the vehicle dynamics benchmark
+  evaluation_time: null
+  language: null
+  links:
+  - type: archive
+    url: https://zenodo.org/records/8307853
+  name: VehicleDynamics (Zenodo)
+  requirements: null
+  type: implementation
+impl_wmodel:
+  description: Tunable generator for binary optimization
+  evaluation_time: null
+  language: null
+  links:
+  - type: repository
+    url: https://github.com/thomasWeise/BBDOB_W_Model
+  name: BBDOB W-Model
+  requirements: null
+  type: implementation
+suite_amvop:
+  allows_partial_evaluation: null
+  can_evaluate_objectives_independently: null
+  code_examples: null
+  constraints: null
+  description: null
+  dynamic_type: null
+  fidelity_levels: null
+  implementations: null
+  long_name: null
+  modality:
+  - multimodal
+  name: AMVOP
+  noise_type: null
+  objectives:
+  - 1
+  problems: null
+  references:
+  - authors: []
+    link:
+      type: null
+      url: https://doi.org/10.1109/TEVC.2013.2281531
+    title: AMVOP
+  source: null
+  tags: null
+  type: suite
+  variables:
+  - dim:
+      max: null
+      min: 1
+    type: categorical
+  - dim:
+      max: null
+      min: 1
+    type: integer
+  - dim:
+      max: null
+      min: 1
+    type: continuous
+suite_bbob:
+  allows_partial_evaluation: null
+  can_evaluate_objectives_independently: null
+  code_examples: null
+  constraints: null
+  description: null
+  dynamic_type: null
+  fidelity_levels: null
+  implementations:
+  - impl_coco
+  long_name: null
+  modality:
+  - multimodal
+  name: BBOB
+  noise_type: null
+  objectives:
+  - 1
+  problems: null
+  references:
+  - authors: []
+    link:
+      type: null
+      url: https://doi.org/10.1080/10556788.2020.1808977
+    title: 'COCO: a platform for comparing continuous optimizers in a black-box setting'
+  source: null
+  tags: null
+  type: suite
+  variables:
+  - dim:
+      max: null
+      min: 1
+    type: continuous
+suite_bbob_biobj:
+  allows_partial_evaluation: null
+  can_evaluate_objectives_independently: null
+  code_examples: null
+  constraints: null
+  description: null
+  dynamic_type: null
+  fidelity_levels: null
+  implementations:
+  - impl_coco
+  long_name: null
+  modality:
+  - multimodal
+  name: BBOB-biobj
+  noise_type: null
+  objectives:
+  - 2
+  problems: null
+  references:
+  - authors: []
+    link:
+      type: null
+      url: https://doi.org/10.48550/arXiv.1604.00359
+    title: BBOB bi-objective test suite
+  source: null
+  tags: null
+  type: suite
+  variables:
+  - dim:
+      max: 40
+      min: 2
+    type: continuous
+suite_bbob_biobj_mixint:
+  allows_partial_evaluation: null
+  can_evaluate_objectives_independently: null
+  code_examples: null
+  constraints: null
+  description: null
+  dynamic_type: null
+  fidelity_levels: null
+  implementations:
+  - impl_coco
+  long_name: null
+  modality:
+  - multimodal
+  name: BBOB-biobj-mixint
+  noise_type: null
+  objectives:
+  - 2
+  problems: null
+  references:
+  - authors: []
+    link:
+      type: null
+      url: https://doi.org/10.1145/3321707.3321868
+    title: BBOB bi-objective mixed-integer test suite
+  source: null
+  tags: null
+  type: suite
+  variables:
+  - dim:
+      max: 160
+      min: 5
+    type: integer
+  - dim:
+      max: 160
+      min: 5
+    type: continuous
+suite_bbob_constrained:
+  allows_partial_evaluation: null
+  can_evaluate_objectives_independently: null
+  code_examples: null
+  constraints:
+  - equality: null
+    hard: yes
+    number: null
+    type: unknown
+  description: null
+  dynamic_type: null
+  fidelity_levels: null
+  implementations:
+  - impl_coco
+  long_name: null
+  modality:
+  - multimodal
+  name: BBOB-constrained
+  noise_type: null
+  objectives:
+  - 1
+  problems: null
+  references:
+  - authors: []
+    link:
+      type: null
+      url: http://numbbo.github.io/coco-doc/bbob-constrained/
+    title: bbob-constrained documentation
+  source: null
+  tags: null
+  type: suite
+  variables:
+  - dim:
+      max: 40
+      min: 2
+    type: continuous
+suite_bbob_largescale:
+  allows_partial_evaluation: null
+  can_evaluate_objectives_independently: null
+  code_examples: null
+  constraints: null
+  description: null
+  dynamic_type: null
+  fidelity_levels: null
+  implementations:
+  - impl_coco
+  long_name: null
+  modality:
+  - multimodal
+  name: BBOB-largescale
+  noise_type: null
+  objectives:
+  - 1
+  problems: null
+  references:
+  - authors: []
+    link:
+      type: null
+      url: https://doi.org/10.48550/arXiv.1903.06396
+    title: BBOB large-scale test suite
+  source: null
+  tags: null
+  type: suite
+  variables:
+  - dim:
+      max: 640
+      min: 20
+    type: continuous
+suite_bbob_mixint:
+  allows_partial_evaluation: null
+  can_evaluate_objectives_independently: null
+  code_examples: null
+  constraints: null
+  description: null
+  dynamic_type: null
+  fidelity_levels: null
+  implementations:
+  - impl_coco
+  long_name: null
+  modality:
+  - multimodal
+  name: BBOB-mixint
+  noise_type: null
+  objectives:
+  - 1
+  problems: null
+  references:
+  - authors: []
+    link:
+      type: null
+      url: https://doi.org/10.1145/3321707.3321868
+    title: BBOB mixed-integer test suite
+  source: null
+  tags: null
+  type: suite
+  variables:
+  - dim:
+      max: 160
+      min: 5
+    type: integer
+  - dim:
+      max: 160
+      min: 5
+    type: continuous
+suite_bbob_noisy:
+  allows_partial_evaluation: null
+  can_evaluate_objectives_independently: null
+  code_examples: null
+  constraints: null
+  description: null
+  dynamic_type: null
+  fidelity_levels: null
+  implementations:
+  - impl_coco_legacy
+  long_name: null
+  modality:
+  - multimodal
+  name: BBOB-noisy
+  noise_type:
+  - noisy
+  objectives:
+  - 1
+  problems: null
+  references:
+  - authors: []
+    link:
+      type: null
+      url: https://hal.inria.fr/inria-00369466
+    title: 'Real-parameter black-box optimization benchmarking: noisy functions definitions'
+  source: null
+  tags: null
+  type: suite
+  variables:
+  - dim:
+      max: null
+      min: 1
+    type: continuous
+suite_bp:
+  allows_partial_evaluation: null
+  can_evaluate_objectives_independently: null
+  code_examples: null
+  constraints: null
+  description: null
+  dynamic_type: null
+  fidelity_levels: null
+  implementations: null
+  long_name: null
+  modality: null
+  name: BP
+  noise_type:
+  - unknown
+  objectives:
+  - 2
+  - 3
+  - 4
+  - 5
+  - 6
+  - 7
+  - 8
+  - 9
+  - 10
+  problems: null
+  references:
+  - authors: []
+    link:
+      type: null
+      url: https://doi.org/10.1109/CEC.2019.8790277
+    title: BP benchmark
+  source: null
+  tags: null
+  type: suite
+  variables:
+  - dim:
+      max: null
+      min: 1
+    type: continuous
+suite_brachytherapy:
+  allows_partial_evaluation: yes
+  can_evaluate_objectives_independently: null
+  code_examples: null
+  constraints:
+  - equality: null
+    hard: yes
+    number:
+      max: null
+      min: 1
+    type: unknown
+  description: Treatment planning for internal radiation therapy. 
+    Multi-objective with aggregated objectives; no public source code.
+  dynamic_type: null
+  fidelity_levels:
+  - 1
+  - 2
+  implementations: null
+  long_name: Brachytherapy treatment planning
+  modality:
+  - multimodal
+  name: Brachytherapy treatment planning
+  noise_type: null
+  objectives:
+  - 2
+  - 3
+  problems: null
+  references:
+  - authors: []
+    link:
+      type: null
+      url: https://www.sciencedirect.com/science/article/pii/S1538472123016781
+    title: Brachytherapy treatment planning
+  source:
+  - real-world
+  tags: null
+  type: suite
+  variables:
+  - dim:
+      max: 500
+      min: 100
+    type: continuous
+suite_car_structure:
+  allows_partial_evaluation: null
+  can_evaluate_objectives_independently: null
+  code_examples: null
+  constraints:
+  - equality: null
+    hard: yes
+    number: 54
+    type: unknown
+  description: 54 constraints
+  dynamic_type: null
+  fidelity_levels: null
+  implementations:
+  - impl_car_structure
+  long_name: null
+  modality: null
+  name: Car structure
+  noise_type: null
+  objectives:
+  - 2
+  problems: null
+  references:
+  - authors: []
+    link:
+      type: null
+      url: https://doi.org/10.1145/3205651.3205702
+    title: Car structure design benchmark
+  source:
+  - real-world
+  tags: null
+  type: suite
+  variables:
+  - dim:
+      max: 222
+      min: 144
+    type: integer
+suite_cdmp:
+  allows_partial_evaluation: null
+  can_evaluate_objectives_independently: null
+  code_examples: null
+  constraints:
+  - equality: null
+    hard: yes
+    number: null
+    type: unknown
+  description: null
+  dynamic_type:
+  - unknown
+  fidelity_levels: null
+  implementations: null
+  long_name: null
+  modality: null
+  name: CDMP
+  noise_type:
+  - unknown
+  objectives:
+  - 2
+  - 3
+  - 4
+  - 5
+  - 6
+  - 7
+  - 8
+  - 9
+  - 10
+  problems: null
+  references:
+  - authors: []
+    link:
+      type: null
+      url: https://doi.org/10.1145/3321707.3321878
+    title: CDMP benchmark
+  source: null
+  tags: null
+  type: suite
+  variables:
+  - dim:
+      max: null
+      min: 1
+    type: continuous
+suite_cec2013:
+  allows_partial_evaluation: null
+  can_evaluate_objectives_independently: null
+  code_examples: null
+  constraints: null
+  description: suite used for cec2013 competition. Also in IOH.
+  dynamic_type: null
+  fidelity_levels: null
+  implementations:
+  - impl_cec2013
+  - impl_iohexperimenter
+  long_name: null
+  modality: null
+  name: CEC2013
+  noise_type: null
+  objectives:
+  - 1
+  problems: null
+  references:
+  - authors: []
+    link:
+      type: null
+      url: https://peerj.com/articles/cs-2671/CEC2013.pdf
+    title: CEC2013 definitions
+  source:
+  - artificial
+  tags: null
+  type: suite
+  variables:
+  - dim:
+      max: null
+      min: 1
+    type: continuous
+suite_cec2015_dmoo:
+  allows_partial_evaluation: null
+  can_evaluate_objectives_independently: null
+  code_examples: null
+  constraints:
+  - equality: null
+    hard: '?'
+    number: null
+    type: unknown
+  description: null
+  dynamic_type:
+  - dynamic
+  fidelity_levels: null
+  implementations: null
+  long_name: null
+  modality: null
+  name: CEC2015-DMOO
+  noise_type: null
+  objectives:
+  - 2
+  - 3
+  problems: null
+  references:
+  - authors: []
+    link: null
+    title: Benchmark Functions for CEC 2015 Special Session and Competition on 
+      Dynamic Multi-objective Optimization
+  source: null
+  tags: null
+  type: suite
+  variables:
+  - dim: 0
+    type: continuous
+suite_cec2018_dt:
+  allows_partial_evaluation: null
+  can_evaluate_objectives_independently: null
+  code_examples: null
+  constraints: null
+  description: '14 problems. Time-dependent: Pareto front/Pareto set geometry; irregular
+    Pareto front shapes; variable-linkage; number of disconnected Pareto front segments;
+    etc.'
+  dynamic_type:
+  - dynamic
+  fidelity_levels: null
+  implementations:
+  - impl_pymoo
+  long_name: CEC2018 Competition on Dynamic Multiobjective Optimisation
+  modality: null
+  name: CEC2018 DT
+  noise_type: null
+  objectives:
+  - 2
+  - 3
+  problems: null
+  references:
+  - authors: []
+    link:
+      type: null
+      url: 
+        https://www.academia.edu/download/94499025/TR-CEC2018-DMOP-Competition.pdf
+    title: CEC2018 DMOP Competition TR
+  source:
+  - artificial
+  tags: null
+  type: suite
+  variables:
+  - dim:
+      max: null
+      min: 1
+    type: unknown
+suite_cec2022:
+  allows_partial_evaluation: null
+  can_evaluate_objectives_independently: null
+  code_examples: null
+  constraints: null
+  description: suite used for cec2022 competition. Also in IOH.
+  dynamic_type: null
+  fidelity_levels: null
+  implementations:
+  - impl_iohexperimenter
+  - impl_cec2022
+  long_name: null
+  modality: null
+  name: CEC2022
+  noise_type: null
+  objectives:
+  - 1
+  problems: null
+  references:
+  - authors: []
+    link:
+      type: null
+      url: 
+        https://github.com/P-N-Suganthan/2022-SO-BO/blob/main/CEC2022%20TR.pdf
+    title: CEC2022 TR
+  source:
+  - artificial
+  tags: null
+  type: suite
+  variables:
+  - dim:
+      max: null
+      min: 1
+    type: continuous
+suite_cfd:
+  allows_partial_evaluation: null
+  can_evaluate_objectives_independently: null
+  code_examples: null
+  constraints:
+  - equality: null
+    hard: yes
+    number: null
+    type: unknown
+  description: expensive evaluations 30s-15m
+  dynamic_type: null
+  fidelity_levels: null
+  implementations:
+  - impl_cfd
+  long_name: null
+  modality: null
+  name: CFD
+  noise_type: null
+  objectives:
+  - 1
+  - 2
+  problems: null
+  references:
+  - authors: []
+    link:
+      type: null
+      url: https://doi.org/10.1007/978-3-319-99259-4_24
+    title: CFD test problem suite
+  source:
+  - real-world
+  tags: null
+  type: suite
+  variables:
+  - dim:
+      max: null
+      min: 1
+    type: unknown
+suite_cre:
+  allows_partial_evaluation: null
+  can_evaluate_objectives_independently: null
+  code_examples: null
+  constraints:
+  - equality: null
+    hard: yes
+    number: null
+    type: unknown
+  description: null
+  dynamic_type: null
+  fidelity_levels: null
+  implementations:
+  - impl_reproblems
+  long_name: null
+  modality: null
+  name: CRE
+  noise_type: null
+  objectives:
+  - 2
+  - 3
+  - 4
+  - 5
+  problems: null
+  references:
+  - authors:
+    - Ryoji Tanabe
+    - Hisao Ishibuchi
+    link:
+      type: null
+      url: https://doi.org/10.1016/j.asoc.2020.106078
+    title: Easy-to-evaluate real-world multi-objective optimization problems
+  source:
+  - real-world-like
+  tags: null
+  type: suite
+  variables:
+  - dim:
+      max: 7
+      min: 3
+    type: integer
+  - dim:
+      max: 7
+      min: 3
+    type: continuous
+suite_cuter:
+  allows_partial_evaluation: no
+  can_evaluate_objectives_independently: null
+  code_examples: null
+  constraints:
+  - equality: null
+    hard: yes
+    number: null
+    type: unknown
+  description: A constrained and unconstrained testing environment.
+  dynamic_type: null
+  fidelity_levels: null
+  implementations: null
+  long_name: null
+  modality: null
+  name: CUTEr
+  noise_type: null
+  objectives:
+  - 1
+  problems: null
+  references:
+  - authors: []
+    link:
+      type: null
+      url: https://dl.acm.org/doi/10.1145/962437.962439
+    title: CUTEr
+  source:
+  - artificial
+  tags: null
+  type: suite
+  variables:
+  - dim:
+      max: null
+      min: 1
+    type: binary
+  - dim:
+      max: null
+      min: 1
+    type: integer
+  - dim:
+      max: null
+      min: 1
+    type: continuous
+suite_cutest:
+  allows_partial_evaluation: no
+  can_evaluate_objectives_independently: null
+  code_examples: null
+  constraints:
+  - equality: null
+    hard: some
+    number:
+      max: null
+      min: 1
+    type: unknown
+  - equality: null
+    hard: yes
+    number: null
+    type: box
+  description: CUTEst for optimization software
+  dynamic_type: null
+  fidelity_levels: null
+  implementations:
+  - impl_pycutest
+  long_name: Constrained and Unconstrained Testing Environment with safe threads
+  modality:
+  - multimodal
+  name: CUTEst
+  noise_type: null
+  objectives:
+  - 1
+  problems: null
+  references:
+  - authors: []
+    link:
+      type: null
+      url: https://link.springer.com/article/10.1007/s10589-014-9687-3
+    title: CUTEst
+  source:
+  - artificial
+  tags: null
+  type: suite
+  variables:
+  - dim:
+      max: null
+      min: 1
+    type: binary
+  - dim:
+      max: null
+      min: 1
+    type: integer
+  - dim:
+      max: null
+      min: 1
+    type: continuous
+suite_dtlz:
+  allows_partial_evaluation: null
+  can_evaluate_objectives_independently: null
+  code_examples: null
+  constraints: null
+  description: null
+  dynamic_type: null
+  fidelity_levels: null
+  implementations:
+  - impl_pymoo
+  long_name: null
+  modality: null
+  name: DTLZ
+  noise_type: null
+  objectives:
+  - 2
+  - 3
+  - 4
+  - 5
+  - 6
+  - 7
+  - 8
+  - 9
+  - 10
+  problems: null
+  references:
+  - authors:
+    - Kalyanmoy Deb
+    - Lothar Thiele
+    - Marco Laumanns
+    - Eckart Zitzler
+    link:
+      type: null
+      url: https://doi.org/10.1109/CEC.2002.1007032
+    title: Scalable multi-objective optimization test problems
+  source: null
+  tags: null
+  type: suite
+  variables:
+  - dim:
+      max: null
+      min: 1
+    type: continuous
+suite_dynamicbinval:
+  allows_partial_evaluation: null
+  can_evaluate_objectives_independently: null
+  code_examples: null
+  constraints: null
+  description: Four versions of the dynamic binary value problem
+  dynamic_type:
+  - dynamic
+  fidelity_levels: null
+  implementations:
+  - impl_iohexperimenter
+  long_name: null
+  modality: null
+  name: DynamicBinVal
+  noise_type: null
+  objectives:
+  - 1
+  problems: null
+  references:
+  - authors: []
+    link:
+      type: null
+      url: https://arxiv.org/pdf/2404.15837
+    title: DynamicBinVal
+  source:
+  - artificial
+  tags: null
+  type: suite
+  variables:
+  - dim:
+      max: null
+      min: 1
+    type: binary
+suite_emo2017:
+  allows_partial_evaluation: null
+  can_evaluate_objectives_independently: null
+  code_examples: null
+  constraints: null
+  description: null
+  dynamic_type: null
+  fidelity_levels: null
+  implementations:
+  - impl_emo2017
+  long_name: null
+  modality: null
+  name: EMO2017
+  noise_type: null
+  objectives:
+  - 2
+  problems: null
+  references:
+  - authors: []
+    link:
+      type: null
+      url: https://www.ini.rub.de/PEOPLE/glasmtbl/projects/bbcomp/
+    title: BBComp EMO 2017
+  source:
+  - real-world
+  tags: null
+  type: suite
+  variables:
+  - dim:
+      max: 24
+      min: 4
+    type: continuous
+suite_etmof:
+  allows_partial_evaluation: null
+  can_evaluate_objectives_independently: null
+  code_examples: null
+  constraints: null
+  description: null
+  dynamic_type:
+  - dynamic
+  fidelity_levels: null
+  implementations:
+  - impl_etmof
+  long_name: null
+  modality: null
+  name: ETMOF
+  noise_type: null
+  objectives:
+  - 2
+  - 3
+  - 4
+  - 5
+  - 6
+  - 7
+  - 8
+  - 9
+  - 10
+  - 11
+  - 12
+  - 13
+  - 14
+  - 15
+  - 16
+  - 17
+  - 18
+  - 19
+  - 20
+  - 21
+  - 22
+  - 23
+  - 24
+  - 25
+  - 26
+  - 27
+  - 28
+  - 29
+  - 30
+  - 31
+  - 32
+  - 33
+  - 34
+  - 35
+  - 36
+  - 37
+  - 38
+  - 39
+  - 40
+  - 41
+  - 42
+  - 43
+  - 44
+  - 45
+  - 46
+  - 47
+  - 48
+  - 49
+  - 50
+  problems: null
+  references:
+  - authors: []
+    link:
+      type: null
+      url: https://doi.org/10.48550/arXiv.2110.08033
+    title: Evolutionary many-task optimization framework
+  source: null
+  tags: null
+  type: suite
+  variables:
+  - dim:
+      max: 10000
+      min: 25
+    type: continuous
+suite_expobench:
+  allows_partial_evaluation: no
+  can_evaluate_objectives_independently: null
+  code_examples: null
+  constraints:
+  - equality: null
+    hard: yes
+    number: null
+    type: box
+  - equality: null
+    hard: some
+    number: null
+    type: unknown
+  description: Wind farm layout optimization, gas filter design, pipe shape 
     optimization, hyperparameter tuning, and hospital simulation
-  reference: https://doi.org/10.1016/j.asoc.2023.110744
-  other info:
-    partial evaluations: 'no'
-    full name: EXPensive Optimization benchmark library
-    constraint properties: Hard Constraints, Soft Constraints, Box Constraints, only
-      box constraints implemented, others appear as penalty in objective
-    number of constraints: 2 per variable (box), other constraints unknown (simulator
-      fails)
-    form of noise model: real-life (unknown)
-    type of noise space: Observational
-    key challenges / characteristics: Expensive objectives
-    scientific motivation: Address the lack of real-life expensive benchmarks
-    limitations: single-objective only, constraints are handled naively (penalty in
-      objective), no parallelization
-    implementation languages: Python
-    approximate evaluation time: 2 to 80 seconds
-- name: Gasoline direct injection engine design
-  suite/generator/single: Single Problem
-  variable type: Continuous, Ordinal
-  dimensionality: '7'
-  objectives: '2'
-  constraints: 'yes'
-  dynamic: 'no'
-  noise: 'no'
-  multimodal: Unknown
-  multi-fidelity: 'yes'
-  source (real-world/artificial): Real-World Application
-  implementation: https://doi.org/10.1016/j.ejor.2022.08.032
-  textual description: 'A multi-objective optimization problem seeking to minimize
-    fuel consumption and NOx emissions over a two-minute dynamic duty cycle, subject
-    to five constraints (turbine inlet temperature, number of knock occurrences, peak
-    cylinder pressure, peak cylinder pressure rise, total work). Seven decision variables
-    are defined: four define the hardware choices of cylinder compression ratio, turbo
-    machinery and EGR cooler sizing; three relate to control variables that parameterise
-    the engine control logic.'
-  other info:
-    partial evaluations: Unknown
-    constraint properties: Hard Constraints, Soft Constraints
-    number of constraints: '5'
-    key challenges / characteristics: Expensive
-    limitations: Proprietary
-    implementation languages: Matlab Simulink and Wave RT co-simulation
-- name: BEACON
-  suite/generator/single: Generator
-  variable type: Continuous
-  dimensionality: scalable
-  objectives: '2'
-  constraints: 'no'
-  dynamic: 'no'
-  noise: 'no'
-  multimodal: 'yes'
-  multi-fidelity: 'no'
-  source (real-world/artificial): Artificially Generated
-  implementation: https://github.com/Stebbet/BEACON/
-  textual description: Generator for bi-objective benchmark problems with explicitly
-    controlled correlations in continuous spaces.
-  reference: https://dl.acm.org/doi/10.1145/3712255.3734303
-  other info:
-    partial evaluations: 'no'
-    full name: Continuous Bi-objective Benchmark problems with Explicit Adjustable
-      COrrelatioN control
-    constraint properties: Box Constraints
-    number of constraints: '0'
-    description of multimodality: Random
-    key challenges / characteristics: Multimodal, different correlations among objectives
-    scientific motivation: Controlled correlation among objectives
-    limitations: No analytical Pareto front, only bi-objective
-    implementation languages: Python
-    approximate evaluation time: Negligible
-- name: TulipaEnergy
-  suite/generator/single: Problem Suite
-  variable type: Continuous
-  dimensionality: scalable
-  objectives: '1'
-  constraints: 'yes'
-  dynamic: 'no'
-  noise: 'yes'
-  multimodal: 'no'
-  multi-fidelity: 'yes'
-  source (real-world/artificial): Real-World Application
-  implementation: https://tulipaenergy.github.io/TulipaEnergyModel.jl/stable/
-  textual description: Determine the optimal investment and operation decisions  for
-    different types of assets in the energy system (production, consumption, conversion,
-    storage, and transport), while minimizing loss of load.
-  reference: See https://tulipaenergy.github.io/TulipaEnergyModel.jl/stable/40-scientific-foundation/45-scientific-references
-  other info:
-    partial evaluations: Unknown
-    full name: TulipaEnergyModel.jl
-    constraint properties: Hard Constraints, Soft Constraints
-    number of constraints: millions
-    type of dynamicism: none
-    form of noise model: "depends on input \u2014 still working on stochastic inputs"
-    type of noise space: Parameter
-    key challenges / characteristics: modeled as a potentially very large linear program,
-      different fidelities possible
-    scientific motivation: new techniques for solving large whitebox linear optimization
-      problems
-    limitations: not yet stochastic
-    implementation languages: Julia / JMP
-    approximate evaluation time: from minutes to hours
-    links to usage examples: https://github.com/TulipaEnergy/Tulipa-OBZ-CaseStudy
-- name: ATO
-  suite/generator/single: Single Problem
-  variable type: Continuous
-  dimensionality: '10'
-  objectives: '2'
-  constraints: 'no'
-  dynamic: 'no'
-  noise: 'no'
-  multimodal: 'no'
-  multi-fidelity: 'no'
-  source (real-world/artificial): Real-World Application
-  implementation: '-'
-  textual description: Parameters of the Modules of the Automatic Train Operation
-    should be optimized. The parameters are continuous with different ranges. There
-    are two objectives (minimizing energy consumption, minimizing driving duration.
-  other info:
-    partial evaluations: 'no'
-- name: Brachytherapy treatment planning
-  suite/generator/single: Problem Suite
-  variable type: Continuous
-  dimensionality: 100-500
-  objectives: 2-3
-  constraints: 'yes'
-  dynamic: 'no'
-  noise: 'no'
-  multimodal: 'yes'
-  multi-fidelity: 'yes'
-  source (real-world/artificial): Real-World Application
-  textual description: Treatment planning for internal radiation therapy
-  reference: https://www.sciencedirect.com/science/article/pii/S1538472123016781
-  other info:
-    partial evaluations: 'yes'
-    full name: Brachytherapy treatment planning
-    constraint properties: Hard Constraints
-    number of constraints: scalable
-    key challenges / characteristics: Multi-objective; aggregated objectives
-    limitations: No public source code
-- name: FleetOpt
-  suite/generator/single: Single Problem
-  variable type: Integer
-  dimensionality: 'Upper level: 54; lower level: 13208'
-  objectives: '1'
-  constraints: 'yes'
-  dynamic: 'no'
-  noise: 'no'
-  multimodal: Unknown
-  multi-fidelity: 'no'
-  source (real-world/artificial): Real-World Application
-  implementation: 'Not public: was done for real client with their private data'
-  textual description: 'Healthcare organisation in the UK provided data about their
-    current fleet of vehicles to conduct non-emergency heathcare trips in the Argyll
-    and Bute region of Scotland, UK. They also provided historical data about the
-    trips the vehicles took and about the bases which the vehicles return to. The
-    aim is to reduce the existing fleet of vehicles while still ensuring all trips
-    can be covered. Moving a vehicle from one base to another to help cover trips
-    is OK as long as the original base can still cover its trips. Link to paper with
-    more details: https://dl.acm.org/doi/abs/10.1145/3638530.3664137'
-  reference: https://dl.acm.org/doi/abs/10.1145/3638530.3664137
-  other info:
-    partial evaluations: 'yes'
-- name: Building spatial design
-  suite/generator/single: Single Problem
-  variable type: Continuous, Boolean
-  dimensionality: scalable depending on problem size (e.g. 90 for)
-  objectives: '2'
-  constraints: 'yes'
-  dynamic: 'no'
-  noise: 'no'
-  multimodal: Unknown
-  multi-fidelity: 'no'
-  source (real-world/artificial): Real-World Application
-  implementation: https://github.com/TUe-excellent-buildings/BSO-toolbox
-  textual description: 'Optimise the spatial layout of a building to: minimise energy
-    consumption for climate control, and minimise the strain on the structure'
-  reference: https://hdl.handle.net/1887/81789
-  other info:
-    partial evaluations: 'no'
-    full name: Building spatial design
-    constraint properties: Hard Constraints, Box Constraints, Permutation Constraints
-    number of constraints: 2065 (as example, depends on problem size)
-    key challenges / characteristics: Many hard constraints (simulator cannot evaluate
-      the solution if these are violated); Mixed-variable search space (continuous
-      + binary); Multiple objectives; (Somewhat) expensive solution evaluations
-    implementation languages: C++
-    approximate evaluation time: Roughly 1 second per evaluation for the smallest
-      considered design, and roughly 40 seconds for the larger designs we considered.
-      (Even the larger designs we considered are still relatively small for the considered
-      problem.)
-- name: Electric Motor Design Optimization
-  suite/generator/single: Single Problem
-  variable type: Continuous, Integer
-  dimensionality: '13'
-  objectives: '1'
-  constraints: 'yes'
-  dynamic: 'no'
-  noise: 'yes'
-  multimodal: 'yes'
-  multi-fidelity: 'no'
-  source (real-world/artificial): Real-World Application
-  implementation: Implementation not freely available
-  textual description: The goal is to find a design of a synchronous electric motor
-    for power steering systems that minimizes costs and satisfies all constraints.
-  reference: https://dis.ijs.si/tea/Publications/Tusar23Multistep.pdf (paper in Slovene)
-  other info:
-    partial evaluations: 'no'
-    full name: Electric Motor Design Optimization
-    constraint properties: Hard Constraints, Soft Constraints, Box Constraints
-    number of constraints: '12'
-    description of multimodality: Constraints are multimodal
-    key challenges / characteristics: Time-consuming solution evaluation, highly-constrained
-      problem
-    scientific motivation: Challenging to find good solutions in a limited time
-    limitations: 'Unavailability, even if available, it wouldn''t be helpful to use
-      for benchmarking due taking a long time to evaluate a single solution '
-    implementation languages: Python
-    approximate evaluation time: 8 minutes
-    general: This is not an available problem, but could be interesting to show to
-      researchers which difficulties appear in real-world problems
-- name: BONO-Bench
-  suite/generator/single: Generator
-  variable type: Continuous
-  dimensionality: scalable
-  objectives: '2'
-  constraints: 'no'
-  dynamic: 'no'
-  noise: 'no'
-  multimodal: 'yes'
-  multi-fidelity: 'no'
-  source (real-world/artificial): Artificially Generated
-  implementation: https://github.com/schaepermeier/bonobench
-  textual description: Bi-objective problem generator and suite with scalable continuous
-    decision space. Features complex problem properties (different types of multimodality
-    and challenges in decision and objective space) as well as Pareto front approximations
-    with error guarantees for the hypervolume and exact R2 indicators.
-  other info:
-    partial evaluations: 'no'
-    full name: Bi-objective Numerical Optimization Benchmark (BONO-Bench)
-    constraint properties: Box Constraints
-    implementation languages: Python
-- name: RandOptGen
-  suite/generator/single: Generator
-  variable type: Continuous, Integer, Boolean
-  dimensionality: scalable
-  objectives: scalable
-  constraints: 'no'
-  dynamic: 'no'
-  noise: 'no'
-  multimodal: 'yes'
-  multi-fidelity: 'no'
-  source (real-world/artificial): Artificially Generated
-  implementation: https://github.com/MALEO-research-group/RandOptGen
-  textual description: 'RandOptGen: A Unified Random Problem Generator for Single-and
-    Multi-Objective Optimization Problems with Mixed-Variable Input Spaces'
-  other info:
-    partial evaluations: 'no'
-    full name: RandOptGen
-    implementation languages: Python
-    approximate evaluation time: milliseconds
-    links to usage examples: https://doi.org/10.1145/3712256.3726478
-- name: CUTEr
-  suite/generator/single: Problem Suite
-  variable type: Continuous, Integer, Boolean
-  dimensionality: scalable
-  objectives: '1'
-  constraints: 'yes'
-  dynamic: 'no'
-  noise: 'no'
-  multimodal: Unknown
-  multi-fidelity: 'no'
-  source (real-world/artificial): Artificially Generated
-  implementation: Not Found
-  textual description: A constrained and unconstrained testing environment
-  reference: https://dl.acm.org/doi/10.1145/962437.962439
-  other info:
-    partial evaluations: 'no'
-- name: CUTEst
-  suite/generator/single: Problem Suite
-  variable type: Continuous, Integer, Boolean
-  dimensionality: scalable
-  objectives: '1'
-  constraints: 'yes'
-  dynamic: 'no'
-  noise: 'no'
-  multimodal: 'yes'
-  multi-fidelity: 'no'
-  source (real-world/artificial): Artificially Generated
-  implementation: https://github.com/jfowkes/pycutest
-  textual description: The Constrained and Unconstrained Testing Environment with
-    safe threads (CUTEst) for optimization software
-  reference: https://link.springer.com/article/10.1007/s10589-014-9687-3
-  other info:
-    partial evaluations: 'no'
-    full name: 'Constrained and Unconstrained Testing Environment with safe threads '
-    constraint properties: Soft Constraints, Box Constraints
-    number of constraints: scalable
-    implementation languages: Python, C++, Fortran
-    general: 'Python implementation: https://github.com/jfowkes/pycutest'
-- name: PUBOi
-  suite/generator/single: Generator
-  variable type: Boolean
-  dimensionality: scalable
-  objectives: '1'
-  constraints: 'no'
-  dynamic: 'no'
-  noise: 'no'
-  multimodal: Unknown
-  multi-fidelity: 'no'
-  source (real-world/artificial): Artificially Generated
-  implementation: https://gitlab.com/verel/pubo-importance-benchmark
-  textual description: A benchmark in which variable importance is tunable, based
-    on the Walsh function
-  reference: https://link.springer.com/chapter/10.1007/978-3-031-04148-8_12
-  other info:
-    partial evaluations: 'no'
-    full name: Polynomial Unconstrained Binary Optimization
-    key challenges / characteristics: Tunable variable importance
-    implementation languages: Python, C++
+  dynamic_type: null
+  fidelity_levels: null
+  implementations:
+  - impl_expobench
+  long_name: EXPensive Optimization benchmark library
+  modality: null
+  name: EXPObench
+  noise_type:
+  - real-life
+  - observational
+  objectives:
+  - 1
+  problems: null
+  references:
+  - authors: []
+    link:
+      type: null
+      url: https://doi.org/10.1016/j.asoc.2023.110744
+    title: EXPObench
+  source:
+  - real-world
+  tags: null
+  type: suite
+  variables:
+  - dim:
+      max: 135
+      min: 10
+    type: integer
+  - dim:
+      max: 135
+      min: 10
+    type: categorical
+  - dim:
+      max: 135
+      min: 10
+    type: continuous
+suite_gbea:
+  allows_partial_evaluation: null
+  can_evaluate_objectives_independently: null
+  code_examples: null
+  constraints: null
+  description: expensive evaluations 5s-35s, RW-GAN-Mario and TopTrumps are part
+    of GBEA
+  dynamic_type: null
+  fidelity_levels: null
+  implementations:
+  - impl_gbea
+  long_name: null
+  modality:
+  - multimodal
+  name: GBEA
+  noise_type:
+  - noisy
+  objectives:
+  - 1
+  - 2
+  problems: null
+  references:
+  - authors: []
+    link:
+      type: null
+      url: https://doi.org/10.1145/3321707.3321805
+    title: Game benchmark for evolutionary algorithms
+  source:
+  - real-world
+  tags: null
+  type: suite
+  variables:
+  - dim:
+      max: null
+      min: 1
+    type: continuous
+suite_gnbg:
+  allows_partial_evaluation: null
+  can_evaluate_objectives_independently: null
+  code_examples: null
+  constraints: null
+  description: Generalized Numerical Benchmark Generator
+  dynamic_type: null
+  fidelity_levels: null
+  implementations:
+  - impl_gnbg
+  long_name: null
+  modality: null
+  name: GNBG
+  noise_type: null
+  objectives:
+  - 1
+  problems: null
+  references:
+  - authors: []
+    link:
+      type: null
+      url: https://arxiv.org/abs/2312.07083
+    title: GNBG
+  source:
+  - artificial
+  tags: null
+  type: suite
+  variables:
+  - dim:
+      max: null
+      min: 1
+    type: continuous
+suite_gnbg_ii:
+  allows_partial_evaluation: null
+  can_evaluate_objectives_independently: null
+  code_examples: null
+  constraints: null
+  description: Generalized Numerical Benchmark Generator (version 2). Also 
+    available in IOH.
+  dynamic_type: null
+  fidelity_levels: null
+  implementations:
+  - impl_iohgnbg
+  - impl_gnbg_ii
+  long_name: null
+  modality: null
+  name: GNBG-II
+  noise_type: null
+  objectives:
+  - 1
+  problems: null
+  references:
+  - authors: []
+    link:
+      type: null
+      url: https://dl.acm.org/doi/pdf/10.1145/3712255.3734271
+    title: GNBG-II
+  source:
+  - artificial
+  tags: null
+  type: suite
+  variables:
+  - dim:
+      max: null
+      min: 1
+    type: continuous
+suite_iohclustering:
+  allows_partial_evaluation: null
+  can_evaluate_objectives_independently: null
+  code_examples: null
+  constraints: null
+  description: 'Set of benchmark problems from clustering: optimization task is selecting
+    cluster centers for a given set of data.'
+  dynamic_type: null
+  fidelity_levels: null
+  implementations:
+  - impl_iohclustering
+  long_name: null
+  modality:
+  - multimodal
+  name: IOHClustering
+  noise_type: null
+  objectives:
+  - 1
+  problems: null
+  references:
+  - authors: []
+    link:
+      type: null
+      url: https://arxiv.org/pdf/2505.09233
+    title: IOHClustering
+  source:
+  - artificial-from-real-data
+  tags: null
+  type: suite
+  variables:
+  - dim:
+      max: null
+      min: 1
+    type: continuous
+suite_kinematics_robotarm:
+  allows_partial_evaluation: null
+  can_evaluate_objectives_independently: null
+  code_examples: null
+  constraints: null
+  description: null
+  dynamic_type: null
+  fidelity_levels: null
+  implementations:
+  - impl_transfer_rf_bbob_rw
+  long_name: null
+  modality:
+  - unimodal
+  name: KinematicsRobotArm
+  noise_type: null
+  objectives:
+  - 1
+  problems: null
+  references:
+  - authors: []
+    link:
+      type: null
+      url: https://doi.org/10.1023/A:1013258808932
+    title: Kinematics of a robot arm
+  source:
+  - real-world
+  tags: null
+  type: suite
+  variables:
+  - dim: 21
+    type: continuous
+suite_l1_zdt:
+  allows_partial_evaluation: null
+  can_evaluate_objectives_independently: null
+  code_examples: null
+  constraints: null
+  description: Variant of ZDT with linkages between variables within groups
+  dynamic_type: null
+  fidelity_levels: null
+  implementations: null
+  long_name: null
+  modality: null
+  name: L1-ZDT
+  noise_type: null
+  objectives:
+  - 2
+  problems: null
+  references:
+  - authors: []
+    link:
+      type: null
+      url: https://doi.org/10.1145/1143997.1144179
+    title: Linkage ZDT/DTLZ variants
+  source: null
+  tags: null
+  type: suite
+  variables:
+  - dim:
+      max: null
+      min: 1
+    type: binary
+  - dim:
+      max: null
+      min: 1
+    type: continuous
+suite_l2_dtlz:
+  allows_partial_evaluation: null
+  can_evaluate_objectives_independently: null
+  code_examples: null
+  constraints: null
+  description: Variant of DTLZ2/DTLZ3 with linkages between all variables
+  dynamic_type: null
+  fidelity_levels: null
+  implementations: null
+  long_name: null
+  modality: null
+  name: L2-DTLZ
+  noise_type: null
+  objectives:
+  - 2
+  - 3
+  - 4
+  - 5
+  - 6
+  - 7
+  - 8
+  - 9
+  - 10
+  problems: null
+  references:
+  - authors: []
+    link:
+      type: null
+      url: https://doi.org/10.1145/1143997.1144179
+    title: Linkage ZDT/DTLZ variants
+  source: null
+  tags: null
+  type: suite
+  variables:
+  - dim:
+      max: null
+      min: 1
+    type: continuous
+suite_l2_zdt:
+  allows_partial_evaluation: null
+  can_evaluate_objectives_independently: null
+  code_examples: null
+  constraints: null
+  description: Variant of ZDT with linkages between all variables
+  dynamic_type: null
+  fidelity_levels: null
+  implementations: null
+  long_name: null
+  modality: null
+  name: L2-ZDT
+  noise_type: null
+  objectives:
+  - 2
+  problems: null
+  references:
+  - authors: []
+    link:
+      type: null
+      url: https://doi.org/10.1145/1143997.1144179
+    title: Linkage ZDT/DTLZ variants
+  source: null
+  tags: null
+  type: suite
+  variables:
+  - dim:
+      max: null
+      min: 1
+    type: binary
+  - dim:
+      max: null
+      min: 1
+    type: continuous
+suite_l3_dtlz:
+  allows_partial_evaluation: null
+  can_evaluate_objectives_independently: null
+  code_examples: null
+  constraints: null
+  description: Variant of L2-DTLZ with anti-linkage mapping
+  dynamic_type: null
+  fidelity_levels: null
+  implementations: null
+  long_name: null
+  modality: null
+  name: L3-DTLZ
+  noise_type: null
+  objectives:
+  - 2
+  - 3
+  - 4
+  - 5
+  - 6
+  - 7
+  - 8
+  - 9
+  - 10
+  problems: null
+  references:
+  - authors: []
+    link:
+      type: null
+      url: https://doi.org/10.1145/1143997.1144179
+    title: Linkage ZDT/DTLZ variants
+  source: null
+  tags: null
+  type: suite
+  variables:
+  - dim:
+      max: null
+      min: 1
+    type: continuous
+suite_l3_zdt:
+  allows_partial_evaluation: null
+  can_evaluate_objectives_independently: null
+  code_examples: null
+  constraints: null
+  description: Variant of L2-ZDT with anti-linkage mapping
+  dynamic_type: null
+  fidelity_levels: null
+  implementations: null
+  long_name: null
+  modality: null
+  name: L3-ZDT
+  noise_type: null
+  objectives:
+  - 2
+  problems: null
+  references:
+  - authors: []
+    link:
+      type: null
+      url: https://doi.org/10.1145/1143997.1144179
+    title: Linkage ZDT/DTLZ variants
+  source: null
+  tags: null
+  type: suite
+  variables:
+  - dim:
+      max: null
+      min: 1
+    type: binary
+  - dim:
+      max: null
+      min: 1
+    type: continuous
+suite_maop:
+  allows_partial_evaluation: null
+  can_evaluate_objectives_independently: null
+  code_examples: null
+  constraints: null
+  description: null
+  dynamic_type: null
+  fidelity_levels: null
+  implementations: null
+  long_name: null
+  modality: null
+  name: MaOP
+  noise_type:
+  - unknown
+  objectives:
+  - 2
+  - 3
+  - 4
+  - 5
+  - 6
+  - 7
+  - 8
+  - 9
+  - 10
+  problems: null
+  references:
+  - authors: []
+    link:
+      type: null
+      url: https://doi.org/10.1016/j.swevo.2019.02.003
+    title: MaOP benchmark
+  source: null
+  tags: null
+  type: suite
+  variables:
+  - dim:
+      max: null
+      min: 1
+    type: continuous
+suite_mechbench:
+  allows_partial_evaluation: no
+  can_evaluate_objectives_independently: null
+  code_examples: null
+  constraints:
+  - equality: null
+    hard: yes
+    number:
+    - 1
+    - 2
+    type: unknown
+  description: Set of problems inspired by Structural Mechanics Design 
+    Optimization. Embeds physical simulations (plasticity only, no 
+    fracture/damage). Unstructured/non-isotropic multimodality.
+  dynamic_type: null
+  fidelity_levels: null
+  implementations:
+  - impl_mechbench
+  long_name: MECHBench
+  modality:
+  - multimodal
+  name: MECHBench
+  noise_type: null
+  objectives:
+  - 1
+  problems: null
+  references:
+  - authors: []
+    link:
+      type: null
+      url: https://arxiv.org/abs/2511.10821
+    title: MECHBench
+  source:
+  - real-world
+  tags: null
+  type: suite
+  variables:
+  - dim:
+      max: null
+      min: 1
+    type: continuous
+suite_mf2:
+  allows_partial_evaluation: null
+  can_evaluate_objectives_independently: null
+  code_examples: null
+  constraints: null
+  description: null
+  dynamic_type: null
+  fidelity_levels:
+  - 1
+  - 2
+  implementations:
+  - impl_mf2
+  long_name: null
+  modality: null
+  name: MF2
+  noise_type: null
+  objectives:
+  - 1
+  problems: null
+  references:
+  - authors: []
+    link:
+      type: null
+      url: https://doi.org/10.21105/joss.02049
+    title: 'mf2: a collection of multi-fidelity benchmark functions in Python'
+  source: null
+  tags: null
+  type: suite
+  variables:
+  - dim:
+      max: null
+      min: 1
+    type: continuous
+suite_minus_dtlz:
+  allows_partial_evaluation: null
+  can_evaluate_objectives_independently: null
+  code_examples: null
+  constraints: null
+  description: Variant of DTLZ that minimises the inverse of the base DTLZ 
+    functions
+  dynamic_type: null
+  fidelity_levels: null
+  implementations: null
+  long_name: null
+  modality: null
+  name: Minus DTLZ
+  noise_type: null
+  objectives:
+  - 2
+  - 3
+  - 4
+  - 5
+  - 6
+  - 7
+  - 8
+  - 9
+  - 10
+  problems: null
+  references:
+  - authors: []
+    link:
+      type: null
+      url: https://doi.org/10.1109/TEVC.2016.2587749
+    title: Minus DTLZ / Minus WFG
+  source: null
+  tags: null
+  type: suite
+  variables:
+  - dim:
+      max: null
+      min: 1
+    type: continuous
+suite_minus_wfg:
+  allows_partial_evaluation: null
+  can_evaluate_objectives_independently: null
+  code_examples: null
+  constraints: null
+  description: Variant of WFG that minimises the inverse of the base WFG 
+    functions
+  dynamic_type: null
+  fidelity_levels: null
+  implementations: null
+  long_name: null
+  modality: null
+  name: Minus WFG
+  noise_type: null
+  objectives:
+  - 2
+  - 3
+  - 4
+  - 5
+  - 6
+  - 7
+  - 8
+  - 9
+  - 10
+  problems: null
+  references:
+  - authors: []
+    link:
+      type: null
+      url: https://doi.org/10.1109/TEVC.2016.2587749
+    title: Minus DTLZ / Minus WFG
+  source: null
+  tags: null
+  type: suite
+  variables:
+  - dim:
+      max: null
+      min: 1
+    type: continuous
+suite_mmopp:
+  allows_partial_evaluation: null
+  can_evaluate_objectives_independently: null
+  code_examples: null
+  constraints:
+  - equality: null
+    hard: yes
+    number: null
+    type: unknown
+  description: null
+  dynamic_type: null
+  fidelity_levels: null
+  implementations:
+  - impl_mmopp
+  long_name: null
+  modality:
+  - multimodal
+  name: MMOPP
+  noise_type: null
+  objectives:
+  - 2
+  - 3
+  - 4
+  - 5
+  - 6
+  - 7
+  problems: null
+  references:
+  - authors: []
+    link:
+      type: null
+      url: 
+        http://www5.zzu.edu.cn/system/_content/download.jsp?urltype=news.DownloadAttachUrl&owner=1327567121&wbfileid=4764412
+    title: MMOPP technical report
+  source: null
+  tags: null
+  type: suite
+  variables:
+  - dim: 0
+    type: unknown
+suite_modact:
+  allows_partial_evaluation: null
+  can_evaluate_objectives_independently: null
+  code_examples: null
+  constraints:
+  - equality: null
+    hard: yes
+    number: null
+    type: unknown
+  description: Realistic Constrained Multi-Objective Optimization Benchmark 
+    Problems from Design.
+  dynamic_type: null
+  fidelity_levels: null
+  implementations:
+  - impl_pymoo
+  - impl_modact
+  long_name: multiobjective design of actuators
+  modality: null
+  name: MODAct
+  noise_type: null
+  objectives:
+  - 2
+  - 3
+  - 4
+  - 5
+  problems: null
+  references:
+  - authors: []
+    link:
+      type: null
+      url: https://doi.org/10.1109/TEVC.2020.3020046
+    title: MODAct
+  source:
+  - real-world
+  tags: null
+  type: suite
+  variables:
+  - dim: 20
+    type: continuous
+  - dim: 20
+    type: integer
+suite_morepo:
+  allows_partial_evaluation: null
+  can_evaluate_objectives_independently: null
+  code_examples: null
+  constraints:
+  - equality: null
+    hard: '?'
+    number: null
+    type: unknown
+  description: null
+  dynamic_type:
+  - unknown
+  fidelity_levels: null
+  implementations:
+  - impl_morepo
+  long_name: null
+  modality: null
+  name: MOrepo
+  noise_type:
+  - unknown
+  objectives:
+  - 2
+  problems: null
+  references: null
+  source: null
+  tags: null
+  type: suite
+  variables:
+  - dim: 0
+    type: unknown
+suite_pbo:
+  allows_partial_evaluation: null
+  can_evaluate_objectives_independently: null
+  code_examples: null
+  constraints: null
+  description: Suite of 25 binary optimization problems
+  dynamic_type: null
+  fidelity_levels: null
+  implementations:
+  - impl_iohexperimenter
+  long_name: null
+  modality: null
+  name: PBO
+  noise_type: null
+  objectives:
+  - 1
+  problems: null
+  references:
+  - authors: []
+    link:
+      type: null
+      url: https://dl.acm.org/doi/pdf/10.1145/3319619.3326810
+    title: PBO benchmarks
+  source:
+  - artificial
+  tags: null
+  type: suite
+  variables:
+  - dim:
+      max: null
+      min: 1
+    type: binary
+suite_porkchop:
+  allows_partial_evaluation: null
+  can_evaluate_objectives_independently: null
+  code_examples: null
+  constraints: null
+  description: null
+  dynamic_type: null
+  fidelity_levels: null
+  implementations:
+  - impl_transfer_rf_bbob_rw
+  long_name: null
+  modality:
+  - multimodal
+  name: PorkchopPlotInterplanetaryTrajectory
+  noise_type: null
+  objectives:
+  - 1
+  problems: null
+  references:
+  - authors: []
+    link:
+      type: null
+      url: https://doi.org/10.1109/CEC65147.2025.11042973
+    title: Porkchop plot interplanetary trajectory benchmark
+  source:
+  - real-world
+  tags: null
+  type: suite
+  variables:
+  - dim: 2
+    type: continuous
+suite_re:
+  allows_partial_evaluation: null
+  can_evaluate_objectives_independently: null
+  code_examples: null
+  constraints: null
+  description: null
+  dynamic_type: null
+  fidelity_levels: null
+  implementations:
+  - impl_reproblems
+  long_name: null
+  modality: null
+  name: RE
+  noise_type: null
+  objectives:
+  - 2
+  - 3
+  - 4
+  - 5
+  - 6
+  - 7
+  - 8
+  - 9
+  problems: null
+  references:
+  - authors:
+    - Ryoji Tanabe
+    - Hisao Ishibuchi
+    link:
+      type: null
+      url: https://doi.org/10.1016/j.asoc.2020.106078
+    title: Easy-to-evaluate real-world multi-objective optimization problems
+  source:
+  - real-world-like
+  tags: null
+  type: suite
+  variables:
+  - dim:
+      max: 7
+      min: 2
+    type: integer
+  - dim:
+      max: 7
+      min: 2
+    type: continuous
+suite_rwmvop:
+  allows_partial_evaluation: null
+  can_evaluate_objectives_independently: null
+  code_examples: null
+  constraints:
+  - equality: null
+    hard: yes
+    number: null
+    type: unknown
+  description: null
+  dynamic_type: null
+  fidelity_levels: null
+  implementations: null
+  long_name: null
+  modality: null
+  name: RWMVOP
+  noise_type: null
+  objectives:
+  - 1
+  problems: null
+  references:
+  - authors: []
+    link:
+      type: null
+      url: https://doi.org/10.1109/TEVC.2013.2281531
+    title: RWMVOP
+  source:
+  - real-world
+  tags: null
+  type: suite
+  variables:
+  - dim:
+      max: null
+      min: 1
+    type: categorical
+  - dim:
+      max: null
+      min: 1
+    type: integer
+  - dim:
+      max: null
+      min: 1
+    type: continuous
+suite_sbox_cost:
+  allows_partial_evaluation: null
+  can_evaluate_objectives_independently: null
+  code_examples: null
+  constraints: null
+  description: problems from BBOB but allows instances with the optimum close to
+    the boundary
+  dynamic_type: null
+  fidelity_levels: null
+  implementations:
+  - impl_iohexperimenter
+  long_name: null
+  modality:
+  - multimodal
+  name: SBOX-COST
+  noise_type: null
+  objectives:
+  - 1
+  problems: null
+  references:
+  - authors: []
+    link:
+      type: null
+      url: https://doi.org/10.48550/arXiv.2305.12221
+    title: SBOX-COST
+  source: null
+  tags: null
+  type: suite
+  variables:
+  - dim:
+      max: null
+      min: 1
+    type: continuous
+suite_sdp:
+  allows_partial_evaluation: null
+  can_evaluate_objectives_independently: null
+  code_examples: null
+  constraints: null
+  description: null
+  dynamic_type:
+  - dynamic
+  fidelity_levels: null
+  implementations: null
+  long_name: null
+  modality: null
+  name: SDP
+  noise_type:
+  - unknown
+  objectives:
+  - 2
+  - 3
+  - 4
+  - 5
+  - 6
+  - 7
+  - 8
+  - 9
+  - 10
+  problems: null
+  references:
+  - authors: []
+    link:
+      type: null
+      url: https://doi.org/10.1109/TCYB.2019.2896021
+    title: SDP dynamic multi-objective benchmark
+  source: null
+  tags: null
+  type: suite
+  variables:
+  - dim:
+      max: null
+      min: 1
+    type: continuous
+suite_submodular:
+  allows_partial_evaluation: null
+  can_evaluate_objectives_independently: null
+  code_examples: null
+  constraints: null
+  description: set of graph-based submodular optimization problems from 4 
+    problem types
+  dynamic_type: null
+  fidelity_levels: null
+  implementations:
+  - impl_iohexperimenter
+  long_name: null
+  modality: null
+  name: Submodular Optimization
+  noise_type: null
+  objectives:
+  - 1
+  problems: null
+  references:
+  - authors: []
+    link:
+      type: null
+      url: https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10254181
+    title: Submodular optimization benchmark
+  source:
+  - artificial
+  tags: null
+  type: suite
+  variables:
+  - dim:
+      max: null
+      min: 1
+    type: binary
+suite_tulipa_energy:
+  allows_partial_evaluation: null
+  can_evaluate_objectives_independently: null
+  code_examples: null
+  constraints:
+  - equality: null
+    hard: some
+    number: null
+    type: unknown
+  - equality: null
+    hard: yes
+    number: null
+    type: unknown
+  description: Determine the optimal investment and operation decisions for 
+    different assets in the energy system (production, consumption, conversion, 
+    storage, transport) while minimizing loss of load. Modelled as a potentially
+    very large linear program with multiple fidelity levels.
+  dynamic_type: null
+  fidelity_levels:
+  - 1
+  - 2
+  implementations:
+  - impl_tulipa
+  long_name: TulipaEnergyModel.jl
+  modality:
+  - unimodal
+  name: TulipaEnergy
+  noise_type:
+  - parameter
+  objectives:
+  - 1
+  problems: null
+  references:
+  - authors: []
+    link:
+      type: null
+      url: 
+        https://tulipaenergy.github.io/TulipaEnergyModel.jl/stable/40-scientific-foundation/45-scientific-references
+    title: TulipaEnergyModel.jl scientific references
+  source:
+  - real-world
+  tags: null
+  type: suite
+  variables:
+  - dim:
+      max: null
+      min: 1
+    type: continuous
+suite_vehicle_dynamics:
+  allows_partial_evaluation: null
+  can_evaluate_objectives_independently: null
+  code_examples: null
+  constraints: null
+  description: null
+  dynamic_type: null
+  fidelity_levels: null
+  implementations:
+  - impl_vehicle_dynamics
+  long_name: null
+  modality:
+  - multimodal
+  name: VehicleDynamics
+  noise_type: null
+  objectives:
+  - 1
+  problems: null
+  references:
+  - authors: []
+    link:
+      type: null
+      url: https://www.scitepress.org/Papers/2023/121580/121580.pdf
+    title: VehicleDynamics benchmark
+  source:
+  - real-world
+  tags: null
+  type: suite
+  variables:
+  - dim: 2
+    type: continuous
+suite_wfg:
+  allows_partial_evaluation: null
+  can_evaluate_objectives_independently: null
+  code_examples: null
+  constraints: null
+  description: null
+  dynamic_type: null
+  fidelity_levels: null
+  implementations:
+  - impl_pymoo
+  long_name: null
+  modality: null
+  name: WFG
+  noise_type: null
+  objectives:
+  - 2
+  - 3
+  - 4
+  - 5
+  - 6
+  - 7
+  - 8
+  - 9
+  - 10
+  problems: null
+  references:
+  - authors:
+    - Simon Huband
+    - Philip Hingston
+    - Luigi Barone
+    - Lyndon While
+    link:
+      type: null
+      url: https://doi.org/10.1109/TEVC.2005.861417
+    title: A review of multiobjective test problems and a scalable test problem 
+      toolkit
+  source: null
+  tags: null
+  type: suite
+  variables:
+  - dim:
+      max: null
+      min: 1
+    type: continuous
+suite_zdt:
+  allows_partial_evaluation: null
+  can_evaluate_objectives_independently: null
+  code_examples: null
+  constraints: null
+  description: null
+  dynamic_type: null
+  fidelity_levels: null
+  implementations:
+  - impl_pymoo
+  long_name: null
+  modality: null
+  name: ZDT
+  noise_type: null
+  objectives:
+  - 2
+  problems: null
+  references:
+  - authors:
+    - Eckart Zitzler
+    - Kalyanmoy Deb
+    - Lothar Thiele
+    link:
+      type: null
+      url: https://doi.org/10.1162/106365600568202
+    title: 'Comparison of multiobjective evolutionary algorithms: empirical results'
+  source: null
+  tags: null
+  type: suite
+  variables:
+  - dim:
+      max: null
+      min: 1
+    type: binary
+  - dim:
+      max: null
+      min: 1
+    type: continuous
+

From 32c8b8681f6511a93a654598cc0f568d2b6f7d7b Mon Sep 17 00:00:00 2001
From: Dvermetten <d.vermetten@gmail.com>
Date: Wed, 22 Apr 2026 14:17:31 +0200
Subject: [PATCH 7/9] Change links to main branch and add if__main__ things

---
 docs/javascript.html |   4 +-
 yaml_to_html.py      | 166 ++++++++++++++++++++++---------------------
 2 files changed, 86 insertions(+), 84 deletions(-)

diff --git a/docs/javascript.html b/docs/javascript.html
index 80dde81..d5f41ef 100644
--- a/docs/javascript.html
+++ b/docs/javascript.html
@@ -1,8 +1,8 @@
 
 
 <script>
-const SNIPPET_BASE_URL = 'https://raw.githubusercontent.com/OpenOptimizationOrg/OPL/code_snippets/code_snippets';
-const SNIPPET_TREE_BASE_URL = 'https://github.com/OpenOptimizationOrg/OPL/tree/code_snippets/code_snippets';
+const SNIPPET_BASE_URL = 'https://raw.githubusercontent.com/OpenOptimizationOrg/OPL/main/code_snippets';
+const SNIPPET_TREE_BASE_URL = 'https://github.com/OpenOptimizationOrg/OPL/tree/main/code_snippets';
 const snippetCache = new Map();
 let markdownParserPromise = null;
 
diff --git a/yaml_to_html.py b/yaml_to_html.py
index f106263..639f00b 100755
--- a/yaml_to_html.py
+++ b/yaml_to_html.py
@@ -56,86 +56,88 @@ def add_problem_id_row_attributes(table_html, problem_ids):
 html_footer = f"{html_dir}footer.html"
 html_index = f"{html_dir}index.html"
 html_table_template = f"{html_dir}table_template.html"
-
-# Load data
-with open(yaml_file) as yaml_input:
-    raw_data = pd.json_normalize(yaml.safe_load(yaml_input))
-
-if "problem_id" in raw_data.columns:
-    problem_ids = raw_data["problem_id"].fillna("").map(str)
-else:
-    problem_ids = raw_data["name"].fillna("").map(str).map(to_problem_id)
-
-problem_ids = problem_ids.where(problem_ids.str.len() > 0, raw_data["name"].fillna("").map(str).map(to_problem_id))
-data = raw_data.copy()
-
-# Choose desired columns
-all_columns = False
 default_columns = ["name",
-                   "textual description",
-                   "suite/generator/single",
-                   "objectives",
-                   "dimensionality",
-                   "variable type",
-                   "constraints",
-                   "dynamic",
-                   "noise",
-                   "multi-fidelity",
-                   "source (real-world/artificial)",
-                   "reference",
-                   "implementation"]
-
-if all_columns is False:
-    columns = default_columns
-    data = data[columns]
-
-data = data.map(linkify_cell)
-
-# Generate plain table
-table = data.to_html(render_links=False,
-                     escape=False,  # Don't escape HTML in cells (to allow links)
-                     index=False,
-                     table_id="problems",
-                     classes=["display compact", "display", "styled-table"],  # Set display style
-                     border=0,
-                     na_rep="")  # Leave NaN cells empty
-
-table = add_problem_id_row_attributes(table, problem_ids.tolist())
-
-# Add footer to facilitate individual column search
-idx = table.index('</table>')
-final_table = table[:idx] + "<tfoot><tr>" + " ".join(["<th>"+ i +"</th>" for i in data.columns])+"</tr> </tfoot>" + table[idx:]
-
-default_hidden_columns = {"textual description", "reference", "implementation"}
-
-column_toggles = "".join(
-    [
-        (
-            f'<label class="column-chip">'
-            f'<input class="col-toggle" type="checkbox" data-column="{i}"'
-            f'{" checked" if col not in default_hidden_columns else ""}>'
-            f'<span>{escape(col)}</span>'
-            f'</label>'
-        )
-        for i, col in enumerate(data.columns)
-    ]
-)
-
-with open(html_table_template, encoding="utf-8") as template_file:
-    table_template = template_file.read()
-
-table_markup = (
-    table_template
-    .replace("__COLUMN_TOGGLES__", column_toggles)
-    .replace("__TABLE__", final_table)
-)
-
-# Write table to file
-with open(html_table, "w", encoding="utf-8") as table_file:
-    table_file.write(table_markup)
-
-# Merge table and scripts into HTML page
-with open(html_index, "wb") as output_file:
-    for part_path in [html_header, html_table, html_scripts, html_footer]:
-        with open(part_path, "rb") as part_file:
-            shutil.copyfileobj(part_file, output_file)
+                "textual description",
+                "suite/generator/single",
+                "objectives",
+                "dimensionality",
+                "variable type",
+                "constraints",
+                "dynamic",
+                "noise",
+                "multi-fidelity",
+                "source (real-world/artificial)",
+                "reference",
+                "implementation"]
+
+if __name__ == "__main__":
+
+    # Load data
+    with open(yaml_file) as yaml_input:
+        raw_data = pd.json_normalize(yaml.safe_load(yaml_input))
+
+    if "problem_id" in raw_data.columns:
+        problem_ids = raw_data["problem_id"].fillna("").map(str)
+    else:
+        problem_ids = raw_data["name"].fillna("").map(str).map(to_problem_id)
+
+    problem_ids = problem_ids.where(problem_ids.str.len() > 0, raw_data["name"].fillna("").map(str).map(to_problem_id))
+    data = raw_data.copy()
+
+    # Choose desired columns
+    all_columns = False
+
+    if all_columns is False:
+        columns = default_columns
+        data = data[columns]
+
+    data = data.map(linkify_cell)
+
+    # Generate plain table
+    table = data.to_html(render_links=False,
+                        escape=False,  # Don't escape HTML in cells (to allow links)
+                        index=False,
+                        table_id="problems",
+                        classes=["display compact", "display", "styled-table"],  # Set display style
+                        border=0,
+                        na_rep="")  # Leave NaN cells empty
+
+    table = add_problem_id_row_attributes(table, problem_ids.tolist())
+
+    # Add footer to facilitate individual column search
+    idx = table.index('</table>')
+    final_table = table[:idx] + "<tfoot><tr>" + " ".join(["<th>"+ i +"</th>" for i in data.columns])+"</tr> </tfoot>" + table[idx:]
+
+    default_hidden_columns = {"textual description", "reference", "implementation"}
+
+    column_toggles = "".join(
+        [
+            (
+                f'<label class="column-chip">'
+                f'<input class="col-toggle" type="checkbox" data-column="{i}"'
+                f'{" checked" if col not in default_hidden_columns else ""}>'
+                f'<span>{escape(col)}</span>'
+                f'</label>'
+            )
+            for i, col in enumerate(data.columns)
+        ]
+    )
+
+    with open(html_table_template, encoding="utf-8") as template_file:
+        table_template = template_file.read()
+
+    table_markup = (
+        table_template
+        .replace("__COLUMN_TOGGLES__", column_toggles)
+        .replace("__TABLE__", final_table)
+    )
+
+    # Write table to file
+    with open(html_table, "w", encoding="utf-8") as table_file:
+        table_file.write(table_markup)
+
+    # Merge table and scripts into HTML page
+    with open(html_index, "wb") as output_file:
+        for part_path in [html_header, html_table, html_scripts, html_footer]:
+            with open(part_path, "rb") as part_file:
+                shutil.copyfileobj(part_file, output_file)

From d107213fc0a0720a5821d7712fcf9c1d42fb17ac Mon Sep 17 00:00:00 2001
From: Dvermetten <d.vermetten@gmail.com>
Date: Wed, 22 Apr 2026 16:54:36 +0200
Subject: [PATCH 8/9] Initial version of new yaml to html conversion

---
 docs/index.html          | 4737 ++++++++++++++++++++++++++++----------
 docs/javascript.html     |  196 +-
 docs/problems.html       | 4537 ++++++++++++++++++++++++++----------
 docs/table_styles.css    |   28 +
 docs/table_template.html |   18 +-
 yaml_to_html.py          |  812 ++++++-
 6 files changed, 7752 insertions(+), 2576 deletions(-)

diff --git a/docs/index.html b/docs/index.html
index 78f6a70..ce97309 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -24,1233 +24,3332 @@ <h2>OPL &ndash; Optimisation problem library</h2>
 <p>
 Submit problems and corrections on <a target="_blank" href="https://github.com/OpenOptimizationOrg/OPL/">GitHub</a> with pull requests / issues, through the <a target="_blank" href="https://docs.google.com/forms/d/e/1FAIpQLSehQp24AuFAH2j9jizDhq8K_BYgNGMKXWTMu6s-2RwEJrK59Q/viewform">Google form</a>, or by email: <a href="mailto:koen.van.der.blom@cwi.nl" itemprop="email">koen.van.der.blom@cwi.nl</a>
 </p>
-<section class="table-shell">
-    <div class="table-toolbar">
-        <div class="toolbar-title">Visible columns</div>
-        <div class="toolbar-actions">
-            <button type="button" class="toolbar-btn" id="show-all-columns">Show all</button>
-            <button type="button" class="toolbar-btn" id="hide-all-columns">Hide all</button>
-        </div>
-        <div class="column-controls">
-            <label class="column-chip"><input class="col-toggle" type="checkbox" data-column="0" checked><span>name</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="1"><span>textual description</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="2" checked><span>suite/generator/single</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="3" checked><span>objectives</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="4" checked><span>dimensionality</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="5" checked><span>variable type</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="6" checked><span>constraints</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="7" checked><span>dynamic</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="8" checked><span>noise</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="9" checked><span>multi-fidelity</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="10" checked><span>source (real-world/artificial)</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="11"><span>reference</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="12"><span>implementation</span></label>
-        </div>
-    </div>
-    <div class="table-wrap">
-        <table class="dataframe display compact display styled-table" id="problems">
-  <thead>
-    <tr style="text-align: right;">
-      <th>name</th>
-      <th>textual description</th>
-      <th>suite/generator/single</th>
-      <th>objectives</th>
-      <th>dimensionality</th>
-      <th>variable type</th>
-      <th>constraints</th>
-      <th>dynamic</th>
-      <th>noise</th>
-      <th>multi-fidelity</th>
-      <th>source (real-world/artificial)</th>
-      <th>reference</th>
-      <th>implementation</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr data-problem-id="bbob">
-      <td>BBOB</td>
-      <td></td>
-      <td>suite</td>
-      <td>1</td>
-      <td>scalable</td>
-      <td>continuous</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td></td>
-      <td><a href="https://doi.org/10.1080/10556788.2020.1808977">https://doi.org/10.1080/10556788.2020.1808977</a></td>
-      <td><a href="https://github.com/numbbo/coco">https://github.com/numbbo/coco</a></td>
-    </tr>
-    <tr data-problem-id="bbob_biobj">
-      <td>BBOB-biobj</td>
-      <td></td>
-      <td>suite</td>
-      <td>2</td>
-      <td>2-40</td>
-      <td>continuous</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td></td>
-      <td><a href="https://doi.org/10.48550/arXiv.1604.00359">https://doi.org/10.48550/arXiv.1604.00359</a></td>
-      <td><a href="https://github.com/numbbo/coco">https://github.com/numbbo/coco</a></td>
-    </tr>
-    <tr data-problem-id="bbob_noisy">
-      <td>BBOB-noisy</td>
-      <td></td>
-      <td>suite</td>
-      <td>1</td>
-      <td>scalable</td>
-      <td>continuous</td>
-      <td>no</td>
-      <td>no</td>
-      <td>yes</td>
-      <td>no</td>
-      <td></td>
-      <td><a href="https://hal.inria.fr/inria-00369466">https://hal.inria.fr/inria-00369466</a></td>
-      <td><a href="https://web.archive.org/web/20210416065610/https://coco.gforge.inria.fr/doku.php?id=downloads">https://web.archive.org/web/20210416065610/https://coco.gforge.inria.fr/doku.php?id=downloads</a></td>
-    </tr>
-    <tr data-problem-id="bbob_largescale">
-      <td>BBOB-largescale</td>
-      <td></td>
-      <td>suite</td>
-      <td>1</td>
-      <td>20-640</td>
-      <td>continuous</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td></td>
-      <td><a href="https://doi.org/10.48550/arXiv.1903.06396">https://doi.org/10.48550/arXiv.1903.06396</a></td>
-      <td><a href="https://github.com/numbbo/coco">https://github.com/numbbo/coco</a></td>
-    </tr>
-    <tr data-problem-id="bbob_mixint">
-      <td>BBOB-mixint</td>
-      <td></td>
-      <td>suite</td>
-      <td>1</td>
-      <td>5-160</td>
-      <td>integer;continuous;mixed</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td></td>
-      <td><a href="https://doi.org/10.1145/3321707.3321868">https://doi.org/10.1145/3321707.3321868</a></td>
-      <td><a href="https://github.com/numbbo/coco">https://github.com/numbbo/coco</a></td>
-    </tr>
-    <tr data-problem-id="bbob_biobj_mixint">
-      <td>BBOB-biobj-mixint</td>
-      <td></td>
-      <td>suite</td>
-      <td>2</td>
-      <td>5-160</td>
-      <td>integer;continuous;mixed</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td></td>
-      <td><a href="https://doi.org/10.1145/3321707.3321868">https://doi.org/10.1145/3321707.3321868</a></td>
-      <td><a href="https://github.com/numbbo/coco">https://github.com/numbbo/coco</a></td>
-    </tr>
-    <tr data-problem-id="bbob_constrained">
-      <td>BBOB-constrained</td>
-      <td></td>
-      <td>suite</td>
-      <td>1</td>
-      <td>2-40</td>
-      <td>continuous</td>
-      <td>yes</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td></td>
-      <td><a href="http://numbbo.github.io/coco-doc/bbob-constrained/">http://numbbo.github.io/coco-doc/bbob-constrained/</a></td>
-      <td><a href="https://github.com/numbbo/coco">https://github.com/numbbo/coco</a></td>
-    </tr>
-    <tr data-problem-id="morepo">
-      <td>MOrepo</td>
-      <td></td>
-      <td>suite</td>
-      <td>2</td>
-      <td>?</td>
-      <td>combinatorial</td>
-      <td>?</td>
-      <td>?</td>
-      <td>?</td>
-      <td>no</td>
-      <td></td>
-      <td></td>
-      <td><a href="https://github.com/MCDMSociety/MOrepo">https://github.com/MCDMSociety/MOrepo</a></td>
-    </tr>
-    <tr data-problem-id="zdt">
-      <td>ZDT</td>
-      <td></td>
-      <td>suite</td>
-      <td>2</td>
-      <td>scalable</td>
-      <td>continuous;binary</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td></td>
-      <td><a href="https://doi.org/10.1162/106365600568202">https://doi.org/10.1162/106365600568202</a></td>
-      <td><a href="https://github.com/anyoptimization/pymoo">https://github.com/anyoptimization/pymoo</a></td>
-    </tr>
-    <tr data-problem-id="dtlz">
-      <td>DTLZ</td>
-      <td></td>
-      <td>suite</td>
-      <td>2+</td>
-      <td>scalable</td>
-      <td>continuous</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td></td>
-      <td><a href="https://doi.org/10.1109/CEC.2002.1007032">https://doi.org/10.1109/CEC.2002.1007032</a></td>
-      <td><a href="https://pymoo.org/problems/many/dtlz.html">https://pymoo.org/problems/many/dtlz.html</a></td>
-    </tr>
-    <tr data-problem-id="wfg">
-      <td>WFG</td>
-      <td></td>
-      <td>suite</td>
-      <td>2+</td>
-      <td>scalable</td>
-      <td>continuous</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td></td>
-      <td><a href="https://doi.org/10.1109/TEVC.2005.861417">https://doi.org/10.1109/TEVC.2005.861417</a></td>
-      <td><a href="https://pymoo.org/problems/many/wfg.html">https://pymoo.org/problems/many/wfg.html</a></td>
-    </tr>
-    <tr data-problem-id="cdmp">
-      <td>CDMP</td>
-      <td></td>
-      <td>suite</td>
-      <td>2+</td>
-      <td>scalable</td>
-      <td>continuous</td>
-      <td>yes</td>
-      <td>?</td>
-      <td>?</td>
-      <td>no</td>
-      <td></td>
-      <td><a href="https://doi.org/10.1145/3321707.3321878">https://doi.org/10.1145/3321707.3321878</a></td>
-      <td>?</td>
-    </tr>
-    <tr data-problem-id="sdp">
-      <td>SDP</td>
-      <td></td>
-      <td>suite</td>
-      <td>2+</td>
-      <td>scalable</td>
-      <td>continuous</td>
-      <td>no</td>
-      <td>yes</td>
-      <td>?</td>
-      <td>no</td>
-      <td></td>
-      <td><a href="https://doi.org/10.1109/TCYB.2019.2896021">https://doi.org/10.1109/TCYB.2019.2896021</a></td>
-      <td>?</td>
-    </tr>
-    <tr data-problem-id="maop">
-      <td>MaOP</td>
-      <td></td>
-      <td>suite</td>
-      <td>2+</td>
-      <td>scalable</td>
-      <td>continuous</td>
-      <td>no</td>
-      <td>no</td>
-      <td>?</td>
-      <td>no</td>
-      <td></td>
-      <td><a href="https://doi.org/10.1016/j.swevo.2019.02.003">https://doi.org/10.1016/j.swevo.2019.02.003</a></td>
-      <td>?</td>
-    </tr>
-    <tr data-problem-id="bp">
-      <td>BP</td>
-      <td></td>
-      <td>suite</td>
-      <td>2+</td>
-      <td>scalable</td>
-      <td>continuous</td>
-      <td>no</td>
-      <td>no</td>
-      <td>?</td>
-      <td>no</td>
-      <td></td>
-      <td><a href="https://doi.org/10.1109/CEC.2019.8790277">https://doi.org/10.1109/CEC.2019.8790277</a></td>
-      <td>?</td>
-    </tr>
-    <tr data-problem-id="gpd">
-      <td>GPD</td>
-      <td></td>
-      <td>generator</td>
-      <td>2+</td>
-      <td>scalable</td>
-      <td>continuous</td>
-      <td>optional</td>
-      <td>no</td>
-      <td>optional</td>
-      <td>no</td>
-      <td></td>
-      <td><a href="https://doi.org/10.1016/j.asoc.2020.106139">https://doi.org/10.1016/j.asoc.2020.106139</a></td>
-      <td>?</td>
-    </tr>
-    <tr data-problem-id="etmof">
-      <td>ETMOF</td>
-      <td></td>
-      <td>suite</td>
-      <td>2-50</td>
-      <td>25-10000</td>
-      <td>continuous</td>
-      <td>no</td>
-      <td>yes</td>
-      <td>no</td>
-      <td>no</td>
-      <td></td>
-      <td><a href="https://doi.org/10.48550/arXiv.2110.08033">https://doi.org/10.48550/arXiv.2110.08033</a></td>
-      <td><a href="https://github.com/songbai-liu/etmo">https://github.com/songbai-liu/etmo</a></td>
-    </tr>
-    <tr data-problem-id="mmopp">
-      <td>MMOPP</td>
-      <td></td>
-      <td>suite</td>
-      <td>2-7</td>
-      <td>?</td>
-      <td>?</td>
-      <td>yes</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td></td>
-      <td><a href="http://www5.zzu.edu.cn/system/_content/download.jsp?urltype=news.DownloadAttachUrl&owner=1327567121&wbfileid=4764412">http://www5.zzu.edu.cn/system/_content/download.jsp?urltype=news.DownloadAttachUrl&owner=1327567121&wbfileid=4764412</a></td>
-      <td><a href="http://www5.zzu.edu.cn/ecilab/info/1036/1251.htm">http://www5.zzu.edu.cn/ecilab/info/1036/1251.htm</a></td>
-    </tr>
-    <tr data-problem-id="cfd">
-      <td>CFD</td>
-      <td>expensive evaluations 30s-15m</td>
-      <td>suite</td>
-      <td>1-2</td>
-      <td>scalable</td>
-      <td>?</td>
-      <td>yes</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>real world</td>
-      <td><a href="https://doi.org/10.1007/978-3-319-99259-4_24">https://doi.org/10.1007/978-3-319-99259-4_24</a></td>
-      <td><a href="https://bitbucket.org/arahat/cfd-test-problem-suite">https://bitbucket.org/arahat/cfd-test-problem-suite</a></td>
-    </tr>
-    <tr data-problem-id="gbea">
-      <td>GBEA</td>
-      <td>expensive evaluations 5s-35s, RW-GAN-Mario and TopTrumps are part of GBEA</td>
-      <td>suite</td>
-      <td>1-2</td>
-      <td>scalable</td>
-      <td>continuous</td>
-      <td>no</td>
-      <td>no</td>
-      <td>yes</td>
-      <td>no</td>
-      <td>real world</td>
-      <td><a href="https://doi.org/10.1145/3321707.3321805">https://doi.org/10.1145/3321707.3321805</a></td>
-      <td><a href="https://github.com/ttusar/coco-gbea">https://github.com/ttusar/coco-gbea</a></td>
-    </tr>
-    <tr data-problem-id="car_structure">
-      <td>Car structure</td>
-      <td>54 constraints</td>
-      <td>suite</td>
-      <td>2</td>
-      <td>144-222</td>
-      <td>discrete</td>
-      <td>yes</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>real world</td>
-      <td><a href="https://doi.org/10.1145/3205651.3205702">https://doi.org/10.1145/3205651.3205702</a></td>
-      <td><a href="http://ladse.eng.isas.jaxa.jp/benchmark/">http://ladse.eng.isas.jaxa.jp/benchmark/</a></td>
-    </tr>
-    <tr data-problem-id="emo2017">
-      <td>EMO2017</td>
-      <td></td>
-      <td>suite</td>
-      <td>2</td>
-      <td>4-24</td>
-      <td>continuous</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>real world</td>
-      <td><a href="https://www.ini.rub.de/PEOPLE/glasmtbl/projects/bbcomp/">https://www.ini.rub.de/PEOPLE/glasmtbl/projects/bbcomp/</a></td>
-      <td><a href="https://www.ini.rub.de/PEOPLE/glasmtbl/projects/bbcomp/downloads/realworld-problems-bbcomp-EMO-2017.zip">https://www.ini.rub.de/PEOPLE/glasmtbl/projects/bbcomp/downloads/realworld-problems-bbcomp-EMO-2017.zip</a></td>
-    </tr>
-    <tr data-problem-id="jsec2019">
-      <td>JSEC2019</td>
-      <td>expensive evaluations 3s; 22 constraints</td>
-      <td>single</td>
-      <td>1-5</td>
-      <td>32</td>
-      <td>continuous</td>
-      <td>yes</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>real world</td>
-      <td><a href="http://www.jpnsec.org/files/competition2019/EC-Symposium-2019-Competition-English.html">http://www.jpnsec.org/files/competition2019/EC-Symposium-2019-Competition-English.html</a></td>
-      <td><a href="http://www.jpnsec.org/files/competition2019/EC-Symposium-2019-Competition-English.html">http://www.jpnsec.org/files/competition2019/EC-Symposium-2019-Competition-English.html</a></td>
-    </tr>
-    <tr data-problem-id="re">
-      <td>RE</td>
-      <td></td>
-      <td>suite</td>
-      <td>2-9</td>
-      <td>2-7</td>
-      <td>continuous;integer;mixed</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>real world like</td>
-      <td><a href="https://doi.org/10.1016/j.asoc.2020.106078">https://doi.org/10.1016/j.asoc.2020.106078</a></td>
-      <td><a href="https://github.com/ryojitanabe/reproblems">https://github.com/ryojitanabe/reproblems</a></td>
-    </tr>
-    <tr data-problem-id="cre">
-      <td>CRE</td>
-      <td></td>
-      <td>suite</td>
-      <td>2-5</td>
-      <td>3-7</td>
-      <td>continuous;integer;mixed</td>
-      <td>yes</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>real world like</td>
-      <td><a href="https://doi.org/10.1016/j.asoc.2020.106078">https://doi.org/10.1016/j.asoc.2020.106078</a></td>
-      <td><a href="https://github.com/ryojitanabe/reproblems">https://github.com/ryojitanabe/reproblems</a></td>
-    </tr>
-    <tr data-problem-id="radar_waveform">
-      <td>Radar waveform</td>
-      <td></td>
-      <td>single</td>
-      <td>9</td>
-      <td>4-12</td>
-      <td>integer</td>
-      <td>yes</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>real world</td>
-      <td><a href="https://doi.org/10.1007/978-3-540-70928-2_53">https://doi.org/10.1007/978-3-540-70928-2_53</a></td>
-      <td><a href="http://code.evanhughes.org/">http://code.evanhughes.org/</a></td>
-    </tr>
-    <tr data-problem-id="mf2">
-      <td>MF2</td>
-      <td></td>
-      <td>suite</td>
-      <td>1</td>
-      <td>1-n</td>
-      <td>continuous</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>yes</td>
-      <td></td>
-      <td><a href="https://doi.org/10.21105/joss.02049">https://doi.org/10.21105/joss.02049</a></td>
-      <td><a href="https://github.com/sjvrijn/mf2">https://github.com/sjvrijn/mf2</a></td>
-    </tr>
-    <tr data-problem-id="amvop">
-      <td>AMVOP</td>
-      <td></td>
-      <td>suite</td>
-      <td>1</td>
-      <td>scalable</td>
-      <td>mixed continuous+ordinal+categorical+both</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td></td>
-      <td><a href="https://doi.org/10.1109/TEVC.2013.2281531">https://doi.org/10.1109/TEVC.2013.2281531</a></td>
-      <td>?</td>
-    </tr>
-    <tr data-problem-id="rwmvop">
-      <td>RWMVOP</td>
-      <td></td>
-      <td>suite</td>
-      <td>1</td>
-      <td>scalable</td>
-      <td>continuous;mixed continuous+ordinal+categorical+both</td>
-      <td>yes</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>real world</td>
-      <td><a href="https://doi.org/10.1109/TEVC.2013.2281531">https://doi.org/10.1109/TEVC.2013.2281531</a></td>
-      <td>?</td>
-    </tr>
-    <tr data-problem-id="sbox_cost">
-      <td>SBOX-COST</td>
-      <td>problems from BBOB but allows instances with the optimum close to the boundary</td>
-      <td>suite</td>
-      <td>1</td>
-      <td>scalable</td>
-      <td>continuous</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td></td>
-      <td><a href="https://doi.org/10.48550/arXiv.2305.12221">https://doi.org/10.48550/arXiv.2305.12221</a></td>
-      <td><a href="https://github.com/IOHprofiler/IOHexperimenter/">https://github.com/IOHprofiler/IOHexperimenter/</a></td>
-    </tr>
-    <tr data-problem-id="mnk_landscapes">
-      <td>ρMNK-Landscapes</td>
-      <td>tunable variable and objective dimensions; tunable multimodality and correlation between objectives</td>
-      <td>generator</td>
-      <td>scalable</td>
-      <td>scalable</td>
-      <td>binary</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td></td>
-      <td><a href="https://doi.org/10.1016/j.ejor.2012.12.019">https://doi.org/10.1016/j.ejor.2012.12.019</a></td>
-      <td><a href="https://gitlab.com/aliefooghe/mocobench/">https://gitlab.com/aliefooghe/mocobench/</a></td>
-    </tr>
-    <tr data-problem-id="mubqp">
-      <td>mUBQP</td>
-      <td>tunable variable and objective dimensions; tunable density and correlation between objectives</td>
-      <td>generator</td>
-      <td>scalable</td>
-      <td>scalable</td>
-      <td>binary</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td></td>
-      <td><a href="https://doi.org/10.1016/j.asoc.2013.11.008">https://doi.org/10.1016/j.asoc.2013.11.008</a></td>
-      <td><a href="https://gitlab.com/aliefooghe/mocobench/">https://gitlab.com/aliefooghe/mocobench/</a></td>
-    </tr>
-    <tr data-problem-id="mtsp">
-      <td>ρmTSP</td>
-      <td>tunable variable and objective dimensions; tunable instance type (euclidian/random); tunable correlation between objectives</td>
-      <td>generator</td>
-      <td>scalable</td>
-      <td>scalable</td>
-      <td>permutations</td>
-      <td>no (apart from being permutations)</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td></td>
-      <td><a href="https://doi.org/10.1007/978-3-319-45823-6_40">https://doi.org/10.1007/978-3-319-45823-6_40</a></td>
-      <td><a href="https://gitlab.com/aliefooghe/mocobench/">https://gitlab.com/aliefooghe/mocobench/</a></td>
-    </tr>
-    <tr data-problem-id="cec2015_dmoo">
-      <td>CEC2015-DMOO</td>
-      <td></td>
-      <td>suite</td>
-      <td>2-3</td>
-      <td>?</td>
-      <td>continuous</td>
-      <td>?</td>
-      <td>yes</td>
-      <td>no</td>
-      <td>no</td>
-      <td></td>
-      <td>Benchmark Functions for CEC 2015 Special Session and Competition on Dynamic Multi-objective Optimization</td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="ealain">
-      <td>Ealain</td>
-      <td>Real-world-like, easily extensible to increase complexity</td>
-      <td>generator</td>
-      <td>1+</td>
-      <td>scalable</td>
-      <td>continuous,binary,integer</td>
-      <td>optional</td>
-      <td>optional</td>
-      <td>no</td>
-      <td>optional</td>
-      <td>Real-world-like</td>
-      <td><a href="https://doi.org/10.1145/3638530.3654299">https://doi.org/10.1145/3638530.3654299</a></td>
-      <td><a href="https://github.com/qrenau/Ealain">https://github.com/qrenau/Ealain</a></td>
-    </tr>
-    <tr data-problem-id="ma_bbob">
-      <td>MA-BBOB</td>
-      <td>Generator that creates affine combinations of BBOB functions</td>
-      <td>generator</td>
-      <td>1</td>
-      <td>scalable</td>
-      <td>continuous</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>artificial</td>
-      <td><a href="https://doi.org/10.1145/3673908">https://doi.org/10.1145/3673908</a></td>
-      <td><a href="https://github.com/IOHprofiler/IOHexperimenter/blob/master/example/Competitions/MA-BBOB/Example_MABBOB.ipynb">https://github.com/IOHprofiler/IOHexperimenter/blob/master/example/Competitions/MA-BBOB/Example_MABBOB.ipynb</a></td>
-    </tr>
-    <tr data-problem-id="mpm2">
-      <td>MPM2</td>
-      <td>nonlinear nonseparable nonsymmetric; scalable in terms of time to evaluate the objective function</td>
-      <td>generator</td>
-      <td>1</td>
-      <td>scalable</td>
-      <td>continuous</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td></td>
-      <td><a href="https://ls11-www.cs.tu-dortmund.de/_media/techreports/tr15-01.pdf">https://ls11-www.cs.tu-dortmund.de/_media/techreports/tr15-01.pdf</a></td>
-      <td><a href="https://github.com/jakobbossek/smoof/blob/master/inst/mpm2.py">https://github.com/jakobbossek/smoof/blob/master/inst/mpm2.py</a></td>
-    </tr>
-    <tr data-problem-id="convex_dtlz2">
-      <td>Convex DTLZ2</td>
-      <td>Variant of DTLZ2 with a convex Pareto front (instead of concave)</td>
-      <td>single</td>
-      <td>2+</td>
-      <td>scalable</td>
-      <td>continuous</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td></td>
-      <td><a href="https://doi.org/10.1109/TEVC.2013.2281535">https://doi.org/10.1109/TEVC.2013.2281535</a></td>
-      <td>?</td>
-    </tr>
-    <tr data-problem-id="inverted_dtlz1">
-      <td>Inverted DTLZ1</td>
-      <td>Variant of DTLZ1 with an inverted Pareto front</td>
-      <td>single</td>
-      <td>2+</td>
-      <td>scalable</td>
-      <td>continuous</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td></td>
-      <td><a href="https://doi.org/10.1109/TEVC.2013.2281534">https://doi.org/10.1109/TEVC.2013.2281534</a></td>
-      <td>?</td>
-    </tr>
-    <tr data-problem-id="minus_dtlz">
-      <td>Minus DTLZ</td>
-      <td>Variant of DTLZ that minimises the inverse of the base DTLZ functions</td>
-      <td>suite</td>
-      <td>2+</td>
-      <td>scalable</td>
-      <td>continuous</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td></td>
-      <td><a href="https://doi.org/10.1109/TEVC.2016.2587749">https://doi.org/10.1109/TEVC.2016.2587749</a></td>
-      <td>?</td>
-    </tr>
-    <tr data-problem-id="minus_wfg">
-      <td>Minus WFG</td>
-      <td>Variant of WFG that minimises the inverse of the base WFG functions</td>
-      <td>suite</td>
-      <td>2+</td>
-      <td>scalable</td>
-      <td>continuous</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td></td>
-      <td><a href="https://doi.org/10.1109/TEVC.2016.2587749">https://doi.org/10.1109/TEVC.2016.2587749</a></td>
-      <td>?</td>
-    </tr>
-    <tr data-problem-id="l1_zdt">
-      <td>L1-ZDT</td>
-      <td>Variant of ZDT with linkages between variables within one of two groups but not between variables in a different group; Linear recombination operators can potentially take advantage of the problem structure</td>
-      <td>suite</td>
-      <td>2</td>
-      <td>scalable</td>
-      <td>continuous;binary</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td></td>
-      <td><a href="https://doi.org/10.1145/1143997.1144179">https://doi.org/10.1145/1143997.1144179</a></td>
-      <td>?</td>
-    </tr>
-    <tr data-problem-id="l2_zdt">
-      <td>L2-ZDT</td>
-      <td>Variant of ZDT with linkages between all variables; Linear recombination operators can potentially take advantage of the problem structure</td>
-      <td>suite</td>
-      <td>2</td>
-      <td>scalable</td>
-      <td>continuous;binary</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td></td>
-      <td><a href="https://doi.org/10.1145/1143997.1144179">https://doi.org/10.1145/1143997.1144179</a></td>
-      <td>?</td>
-    </tr>
-    <tr data-problem-id="l3_zdt">
-      <td>L3-ZDT</td>
-      <td>Variant of L2-ZDT using a mapping to prevent linear recombination operators from potentially taking advantage of the problem structure</td>
-      <td>suite</td>
-      <td>2</td>
-      <td>scalable</td>
-      <td>continuous;binary</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td></td>
-      <td><a href="https://doi.org/10.1145/1143997.1144179">https://doi.org/10.1145/1143997.1144179</a></td>
-      <td>?</td>
-    </tr>
-    <tr data-problem-id="l2_dtlz">
-      <td>L2-DTLZ</td>
-      <td>Variant of DTLZ2 and DTLZ3 with linkages between all variables; Linear recombination operators can potentially take advantage of the problem structure</td>
-      <td>suite</td>
-      <td>2+</td>
-      <td>scalable</td>
-      <td>continuous</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td></td>
-      <td><a href="https://doi.org/10.1145/1143997.1144179">https://doi.org/10.1145/1143997.1144179</a></td>
-      <td>?</td>
-    </tr>
-    <tr data-problem-id="l3_dtlz">
-      <td>L3-DTLZ</td>
-      <td>Variant of L2-DTLZ using a mapping to prevent linear recombination operators from potentially taking advantage of the problem structure</td>
-      <td>suite</td>
-      <td>2+</td>
-      <td>scalable</td>
-      <td>continuous</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td></td>
-      <td><a href="https://doi.org/10.1145/1143997.1144179">https://doi.org/10.1145/1143997.1144179</a></td>
-      <td>?</td>
-    </tr>
-    <tr data-problem-id="cec2018_dt_cec2018_competition_on_dynamic_multiobjective_optimisation">
-      <td>CEC2018 DT - CEC2018 Competition on Dynamic Multiobjective Optimisation</td>
-      <td>14 problems. Time-dependent: Pareto front/Pareto set geometry; irregular Pareto front shapes; variable-linkage; number of disconnected Pareto front segments; etc.</td>
-      <td>suite</td>
-      <td>2 or 3</td>
-      <td>scalable?</td>
-      <td>?</td>
-      <td>no</td>
-      <td>yes</td>
-      <td>no</td>
-      <td>no</td>
-      <td>artificial</td>
-      <td><a href="https://www.academia.edu/download/94499025/TR-CEC2018-DMOP-Competition.pdf">https://www.academia.edu/download/94499025/TR-CEC2018-DMOP-Competition.pdf</a></td>
-      <td><a href="https://pymoo.org/problems/dynamic/df.html">https://pymoo.org/problems/dynamic/df.html</a></td>
-    </tr>
-    <tr data-problem-id="modact_multiobjective_design_of_actuators">
-      <td>MODAct - multiobjective design of actuators</td>
-      <td>Realistic Constrained Multi-Objective Optimization Benchmark Problems from Design. Need the <a href="https://github.com/epfl-lamd/modact">https://github.com/epfl-lamd/modact</a> package installed; evaluation times around 20ms</td>
-      <td>suite</td>
-      <td>2 3 4 or 5</td>
-      <td>20</td>
-      <td>mixed; integer and continuous</td>
-      <td>yes</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>real-world</td>
-      <td><a href="https://doi.org/10.1109/TEVC.2020.3020046">https://doi.org/10.1109/TEVC.2020.3020046</a></td>
-      <td><a href="https://pymoo.org/problems/constrained/modact.html">https://pymoo.org/problems/constrained/modact.html</a></td>
-    </tr>
-    <tr data-problem-id="iohclustering">
-      <td>IOHClustering</td>
-      <td>Set of benchmark problems from clustering: optimization task is selecting cluster centers for a given set of data, with the number of clusters defining problem dimensionality. Includes both a suite and a generator. Based on ML clustering datasets</td>
-      <td>suite; generator</td>
-      <td>1</td>
-      <td>scalable</td>
-      <td>continuous</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>artificial, but based on real data</td>
-      <td><a href="https://arxiv.org/pdf/2505.09233">https://arxiv.org/pdf/2505.09233</a></td>
-      <td><a href="https://github.com/IOHprofiler/IOHClustering">https://github.com/IOHprofiler/IOHClustering</a></td>
-    </tr>
-    <tr data-problem-id="gnbg_ii">
-      <td>GNBG-II</td>
-      <td>Generalized Numerical Benchmark Generator (version 2). Also in IOH <a href="https://github.com/IOHprofiler/IOHGNBG">https://github.com/IOHprofiler/IOHGNBG</a></td>
-      <td>suite; generator</td>
-      <td>1</td>
-      <td>scalable</td>
-      <td>continuous</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>artificial</td>
-      <td><a href="https://dl.acm.org/doi/pdf/10.1145/3712255.3734271">https://dl.acm.org/doi/pdf/10.1145/3712255.3734271</a></td>
-      <td><a href="https://github.com/rohitsalgotra/GNBG-II">https://github.com/rohitsalgotra/GNBG-II</a></td>
-    </tr>
-    <tr data-problem-id="gnbg">
-      <td>GNBG</td>
-      <td>Generalized Numerical Benchmark Generator</td>
-      <td>suite; generator</td>
-      <td>1</td>
-      <td>scalable</td>
-      <td>continuous</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>artificial</td>
-      <td><a href="https://arxiv.org/abs/2312.07083">https://arxiv.org/abs/2312.07083</a></td>
-      <td><a href="https://github.com/Danial-Yazdani/GNBG-Generator">https://github.com/Danial-Yazdani/GNBG-Generator</a></td>
-    </tr>
-    <tr data-problem-id="dynamicbinval">
-      <td>DynamicBinVal</td>
-      <td>Four versions of the dynamic binary value problem</td>
-      <td>suite</td>
-      <td>1</td>
-      <td>scalable</td>
-      <td>binary</td>
-      <td>no</td>
-      <td>yes</td>
-      <td>no</td>
-      <td>no</td>
-      <td>artificial</td>
-      <td><a href="https://arxiv.org/pdf/2404.15837">https://arxiv.org/pdf/2404.15837</a></td>
-      <td><a href="https://github.com/IOHprofiler/IOHexperimenter">https://github.com/IOHprofiler/IOHexperimenter</a></td>
-    </tr>
-    <tr data-problem-id="pbo">
-      <td>PBO</td>
-      <td>Suite of 25 binary optimization problems</td>
-      <td>suite</td>
-      <td>1</td>
-      <td>scalable</td>
-      <td>binary</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>artificial</td>
-      <td><a href="https://dl.acm.org/doi/pdf/10.1145/3319619.3326810">https://dl.acm.org/doi/pdf/10.1145/3319619.3326810</a></td>
-      <td><a href="https://github.com/IOHprofiler/IOHexperimenter">https://github.com/IOHprofiler/IOHexperimenter</a></td>
-    </tr>
-    <tr data-problem-id="w_model">
-      <td>W-model</td>
-      <td>Tunable generator for binary optimization based on several difficulty features</td>
-      <td>generator</td>
-      <td>1</td>
-      <td>scalable</td>
-      <td>binary</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>artificial</td>
-      <td><a href="https://dl.acm.org/doi/abs/10.1145/3205651.3208240?casa_token=S4U_Pi9f6MwAAAAA:U9ztNTPwmupT8K3GamWZfBL7-8fqjxPtr_kprv51vdwA-REsp0EyOFGa99BtbANb0XbqyrVg795hIw">https://dl.acm.org/doi/abs/10.1145/3205651.3208240?casa_token=S4U_Pi9f6MwAAAAA:U9ztNTPwmupT8K3GamWZfBL7-8fqjxPtr_kprv51vdwA-REsp0EyOFGa99BtbANb0XbqyrVg795hIw</a></td>
-      <td><a href="https://github.com/thomasWeise/BBDOB_W_Model">https://github.com/thomasWeise/BBDOB_W_Model</a></td>
-    </tr>
-    <tr data-problem-id="submodular_optimitzation">
-      <td>Submodular Optimitzation</td>
-      <td>set of graph-based submodular optimization problems from 4 problem types</td>
-      <td>suite</td>
-      <td>1</td>
-      <td>scalable</td>
-      <td>binary</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>artificial</td>
-      <td><a href="https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10254181">https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10254181</a></td>
-      <td><a href="https://github.com/IOHprofiler/IOHexperimenter">https://github.com/IOHprofiler/IOHexperimenter</a></td>
-    </tr>
-    <tr data-problem-id="cec2013">
-      <td>CEC2013</td>
-      <td>suite used for cec2013 competition. Also in IOH <a href="https://github.com/IOHprofiler/IOHexperimenter">https://github.com/IOHprofiler/IOHexperimenter</a></td>
-      <td>suite</td>
-      <td>1</td>
-      <td>scalable</td>
-      <td>continuous</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>artificial</td>
-      <td><a href="https://peerj.com/articles/cs-2671/CEC2013.pdf">https://peerj.com/articles/cs-2671/CEC2013.pdf</a></td>
-      <td><a href="https://github.com/P-N-Suganthan/CEC2013">https://github.com/P-N-Suganthan/CEC2013</a></td>
-    </tr>
-    <tr data-problem-id="cec2022">
-      <td>CEC2022</td>
-      <td>suite used for cec2022 competition. Also in IOH <a href="https://github.com/IOHprofiler/IOHexperimenter">https://github.com/IOHprofiler/IOHexperimenter</a></td>
-      <td>suite</td>
-      <td>1</td>
-      <td>scalable</td>
-      <td>continuous</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>artificial</td>
-      <td><a href="https://github.com/P-N-Suganthan/2022-SO-BO/blob/main/CEC2022%20TR.pdf">https://github.com/P-N-Suganthan/2022-SO-BO/blob/main/CEC2022%20TR.pdf</a></td>
-      <td><a href="https://github.com/P-N-Suganthan/2022-SO-BO">https://github.com/P-N-Suganthan/2022-SO-BO</a></td>
-    </tr>
-    <tr data-problem-id="onemaxsphere_zeromaxsphere">
-      <td>Onemax+Sphere / Zeromax+Sphere</td>
-      <td></td>
-      <td>single</td>
-      <td>2</td>
-      <td>scalable</td>
-      <td>binary and continuous;mixed;</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>artificial</td>
-      <td><a href="https://doi.org/10.1145/3449726.3459521">https://doi.org/10.1145/3449726.3459521</a></td>
-      <td>None</td>
-    </tr>
-    <tr data-problem-id="onemaxsphere_deceptivetraprotatedellipsoid">
-      <td>Onemax+Sphere / DeceptiveTrap+RotatedEllipsoid</td>
-      <td></td>
-      <td>single</td>
-      <td>2</td>
-      <td>scalable</td>
-      <td>binary and continuous;mixed;</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>artificial</td>
-      <td><a href="https://doi.org/10.1145/3449726.3459521">https://doi.org/10.1145/3449726.3459521</a></td>
-      <td>None</td>
-    </tr>
-    <tr data-problem-id="inversedeceptivetraprotatedellipsoid_deceptivetraprotatedellipsoid">
-      <td>InverseDeceptiveTrap+RotatedEllipsoid / DeceptiveTrap+RotatedEllipsoid</td>
-      <td></td>
-      <td>single</td>
-      <td>2</td>
-      <td>scalable</td>
-      <td>binary and continuous;mixed;</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>artificial</td>
-      <td><a href="https://doi.org/10.1145/3449726.3459521">https://doi.org/10.1145/3449726.3459521</a></td>
-      <td>None</td>
-    </tr>
-    <tr data-problem-id="porkchopplotinterplanetarytrajectory">
-      <td>PorkchopPlotInterplanetaryTrajectory</td>
-      <td></td>
-      <td>suite</td>
-      <td>1</td>
-      <td>2</td>
-      <td>continuous</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>real-world</td>
-      <td><a href="https://doi.org/10.1109/CEC65147.2025.11042973">https://doi.org/10.1109/CEC65147.2025.11042973</a></td>
-      <td><a href="https://github.com/ShuaiqunPan/Transfer_Random_forests_BBOB_Real_world">https://github.com/ShuaiqunPan/Transfer_Random_forests_BBOB_Real_world</a></td>
-    </tr>
-    <tr data-problem-id="kinematicsrobotarm">
-      <td>KinematicsRobotArm</td>
-      <td></td>
-      <td>suite</td>
-      <td>1</td>
-      <td>21</td>
-      <td>continuous</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>real-world</td>
-      <td><a href="https://doi.org/10.1023/A:1013258808932">https://doi.org/10.1023/A:1013258808932</a></td>
-      <td><a href="https://github.com/ShuaiqunPan/Transfer_Random_forests_BBOB_Real_world">https://github.com/ShuaiqunPan/Transfer_Random_forests_BBOB_Real_world</a></td>
-    </tr>
-    <tr data-problem-id="vehicledynamics">
-      <td>VehicleDynamics</td>
-      <td></td>
-      <td>suite</td>
-      <td>1</td>
-      <td>2</td>
-      <td>continuous</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>real-world</td>
-      <td><a href="https://www.scitepress.org/Papers/2023/121580/121580.pdf">https://www.scitepress.org/Papers/2023/121580/121580.pdf</a></td>
-      <td><a href="https://zenodo.org/records/8307853">https://zenodo.org/records/8307853</a></td>
-    </tr>
-    <tr data-problem-id="mechbench">
-      <td>MECHBench</td>
-      <td>This is a set of problems with inspiration from Structural Mechanics Design Optimization. The suite comprises three physical models, from which the user may define different kind of problems which impact the final design output.</td>
-      <td>Problem Suite</td>
-      <td>1</td>
-      <td>scalable'</td>
-      <td>Continuous</td>
-      <td>yes</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>Real-World Application</td>
-      <td><a href="https://arxiv.org/abs/2511.10821">https://arxiv.org/abs/2511.10821</a></td>
-      <td><a href="https://github.com/BayesOptApp/MECHBench">https://github.com/BayesOptApp/MECHBench</a></td>
-    </tr>
-    <tr data-problem-id="expobench">
-      <td>EXPObench</td>
-      <td>Wind farm layout optimization, gas filter design, pipe shape optimization, hyperparameter tuning, and hospital simulation</td>
-      <td>Problem Suite</td>
-      <td>1</td>
-      <td>10 to 135</td>
-      <td>Continuous, Integer, Categorical, Conditional</td>
-      <td>yes</td>
-      <td>no</td>
-      <td>yes</td>
-      <td>no</td>
-      <td>Real-World Application</td>
-      <td><a href="https://doi.org/10.1016/j.asoc.2023.110744">https://doi.org/10.1016/j.asoc.2023.110744</a></td>
-      <td><a href="https://github.com/AlgTUDelft/ExpensiveOptimBenchmark">https://github.com/AlgTUDelft/ExpensiveOptimBenchmark</a></td>
-    </tr>
-    <tr data-problem-id="gasoline_direct_injection_engine_design">
-      <td>Gasoline direct injection engine design</td>
-      <td>A multi-objective optimization problem seeking to minimize fuel consumption and NOx emissions over a two-minute dynamic duty cycle, subject to five constraints (turbine inlet temperature, number of knock occurrences, peak cylinder pressure, peak cylinder pressure rise, total work). Seven decision variables are defined: four define the hardware choices of cylinder compression ratio, turbo machinery and EGR cooler sizing; three relate to control variables that parameterise the engine control logic.</td>
-      <td>Single Problem</td>
-      <td>2</td>
-      <td>7</td>
-      <td>Continuous, Ordinal</td>
-      <td>yes</td>
-      <td>no</td>
-      <td>no</td>
-      <td>yes</td>
-      <td>Real-World Application</td>
-      <td></td>
-      <td><a href="https://doi.org/10.1016/j.ejor.2022.08.032">https://doi.org/10.1016/j.ejor.2022.08.032</a></td>
-    </tr>
-    <tr data-problem-id="beacon">
-      <td>BEACON</td>
-      <td>Generator for bi-objective benchmark problems with explicitly controlled correlations in continuous spaces.</td>
-      <td>Generator</td>
-      <td>2</td>
-      <td>scalable</td>
-      <td>Continuous</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>Artificially Generated</td>
-      <td><a href="https://dl.acm.org/doi/10.1145/3712255.3734303">https://dl.acm.org/doi/10.1145/3712255.3734303</a></td>
-      <td><a href="https://github.com/Stebbet/BEACON/">https://github.com/Stebbet/BEACON/</a></td>
-    </tr>
-    <tr data-problem-id="tulipaenergy">
-      <td>TulipaEnergy</td>
-      <td>Determine the optimal investment and operation decisions  for different types of assets in the energy system (production, consumption, conversion, storage, and transport), while minimizing loss of load.</td>
-      <td>Problem Suite</td>
-      <td>1</td>
-      <td>scalable</td>
-      <td>Continuous</td>
-      <td>yes</td>
-      <td>no</td>
-      <td>yes</td>
-      <td>yes</td>
-      <td>Real-World Application</td>
-      <td>See <a href="https://tulipaenergy.github.io/TulipaEnergyModel.jl/stable/40-scientific-foundation/45-scientific-references">https://tulipaenergy.github.io/TulipaEnergyModel.jl/stable/40-scientific-foundation/45-scientific-references</a></td>
-      <td><a href="https://tulipaenergy.github.io/TulipaEnergyModel.jl/stable/">https://tulipaenergy.github.io/TulipaEnergyModel.jl/stable/</a></td>
-    </tr>
-    <tr data-problem-id="ato">
-      <td>ATO</td>
-      <td>Parameters of the Modules of the Automatic Train Operation should be optimized. The parameters are continuous with different ranges. There are two objectives (minimizing energy consumption, minimizing driving duration.</td>
-      <td>Single Problem</td>
-      <td>2</td>
-      <td>10</td>
-      <td>Continuous</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>Real-World Application</td>
-      <td></td>
-      <td>-</td>
-    </tr>
-    <tr data-problem-id="brachytherapy_treatment_planning">
-      <td>Brachytherapy treatment planning</td>
-      <td>Treatment planning for internal radiation therapy</td>
-      <td>Problem Suite</td>
-      <td>2-3</td>
-      <td>100-500</td>
-      <td>Continuous</td>
-      <td>yes</td>
-      <td>no</td>
-      <td>no</td>
-      <td>yes</td>
-      <td>Real-World Application</td>
-      <td><a href="https://www.sciencedirect.com/science/article/pii/S1538472123016781">https://www.sciencedirect.com/science/article/pii/S1538472123016781</a></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="fleetopt">
-      <td>FleetOpt</td>
-      <td>Healthcare organisation in the UK provided data about their current fleet of vehicles to conduct non-emergency heathcare trips in the Argyll and Bute region of Scotland, UK. They also provided historical data about the trips the vehicles took and about the bases which the vehicles return to. The aim is to reduce the existing fleet of vehicles while still ensuring all trips can be covered. Moving a vehicle from one base to another to help cover trips is OK as long as the original base can still cover its trips. Link to paper with more details: <a href="https://dl.acm.org/doi/abs/10.1145/3638530.3664137">https://dl.acm.org/doi/abs/10.1145/3638530.3664137</a></td>
-      <td>Single Problem</td>
-      <td>1</td>
-      <td>Upper level: 54; lower level: 13208</td>
-      <td>Integer</td>
-      <td>yes</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>Real-World Application</td>
-      <td><a href="https://dl.acm.org/doi/abs/10.1145/3638530.3664137">https://dl.acm.org/doi/abs/10.1145/3638530.3664137</a></td>
-      <td>Not public: was done for real client with their private data</td>
-    </tr>
-    <tr data-problem-id="building_spatial_design">
-      <td>Building spatial design</td>
-      <td>Optimise the spatial layout of a building to: minimise energy consumption for climate control, and minimise the strain on the structure</td>
-      <td>Single Problem</td>
-      <td>2</td>
-      <td>scalable depending on problem size (e.g. 90 for)</td>
-      <td>Continuous, Boolean</td>
-      <td>yes</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>Real-World Application</td>
-      <td><a href="https://hdl.handle.net/1887/81789">https://hdl.handle.net/1887/81789</a></td>
-      <td><a href="https://github.com/TUe-excellent-buildings/BSO-toolbox">https://github.com/TUe-excellent-buildings/BSO-toolbox</a></td>
-    </tr>
-    <tr data-problem-id="electric_motor_design_optimization">
-      <td>Electric Motor Design Optimization</td>
-      <td>The goal is to find a design of a synchronous electric motor for power steering systems that minimizes costs and satisfies all constraints.</td>
-      <td>Single Problem</td>
-      <td>1</td>
-      <td>13</td>
-      <td>Continuous, Integer</td>
-      <td>yes</td>
-      <td>no</td>
-      <td>yes</td>
-      <td>no</td>
-      <td>Real-World Application</td>
-      <td><a href="https://dis.ijs.si/tea/Publications/Tusar23Multistep.pdf">https://dis.ijs.si/tea/Publications/Tusar23Multistep.pdf</a> (paper in Slovene)</td>
-      <td>Implementation not freely available</td>
-    </tr>
-    <tr data-problem-id="bono_bench">
-      <td>BONO-Bench</td>
-      <td>Bi-objective problem generator and suite with scalable continuous decision space. Features complex problem properties (different types of multimodality and challenges in decision and objective space) as well as Pareto front approximations with error guarantees for the hypervolume and exact R2 indicators.</td>
-      <td>Generator</td>
-      <td>2</td>
-      <td>scalable</td>
-      <td>Continuous</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>Artificially Generated</td>
-      <td></td>
-      <td><a href="https://github.com/schaepermeier/bonobench">https://github.com/schaepermeier/bonobench</a></td>
-    </tr>
-    <tr data-problem-id="randoptgen">
-      <td>RandOptGen</td>
-      <td>RandOptGen: A Unified Random Problem Generator for Single-and Multi-Objective Optimization Problems with Mixed-Variable Input Spaces</td>
-      <td>Generator</td>
-      <td>scalable</td>
-      <td>scalable</td>
-      <td>Continuous, Integer, Boolean</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>Artificially Generated</td>
-      <td></td>
-      <td><a href="https://github.com/MALEO-research-group/RandOptGen">https://github.com/MALEO-research-group/RandOptGen</a></td>
-    </tr>
-    <tr data-problem-id="cuter">
-      <td>CUTEr</td>
-      <td>A constrained and unconstrained testing environment</td>
-      <td>Problem Suite</td>
-      <td>1</td>
-      <td>scalable</td>
-      <td>Continuous, Integer, Boolean</td>
-      <td>yes</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>Artificially Generated</td>
-      <td><a href="https://dl.acm.org/doi/10.1145/962437.962439">https://dl.acm.org/doi/10.1145/962437.962439</a></td>
-      <td>Not Found</td>
-    </tr>
-    <tr data-problem-id="cutest">
-      <td>CUTEst</td>
-      <td>The Constrained and Unconstrained Testing Environment with safe threads (CUTEst) for optimization software</td>
-      <td>Problem Suite</td>
-      <td>1</td>
-      <td>scalable</td>
-      <td>Continuous, Integer, Boolean</td>
-      <td>yes</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>Artificially Generated</td>
-      <td><a href="https://link.springer.com/article/10.1007/s10589-014-9687-3">https://link.springer.com/article/10.1007/s10589-014-9687-3</a></td>
-      <td><a href="https://github.com/jfowkes/pycutest">https://github.com/jfowkes/pycutest</a></td>
-    </tr>
-    <tr data-problem-id="puboi">
-      <td>PUBOi</td>
-      <td>A benchmark in which variable importance is tunable, based on the Walsh function</td>
-      <td>Generator</td>
-      <td>1</td>
-      <td>scalable</td>
-      <td>Boolean</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>Artificially Generated</td>
-      <td><a href="https://link.springer.com/chapter/10.1007/978-3-031-04148-8_12">https://link.springer.com/chapter/10.1007/978-3-031-04148-8_12</a></td>
-      <td><a href="https://gitlab.com/verel/pubo-importance-benchmark">https://gitlab.com/verel/pubo-importance-benchmark</a></td>
-    </tr>
-  </tbody>
-<tfoot><tr><th>name</th> <th>textual description</th> <th>suite/generator/single</th> <th>objectives</th> <th>dimensionality</th> <th>variable type</th> <th>constraints</th> <th>dynamic</th> <th>noise</th> <th>multi-fidelity</th> <th>source (real-world/artificial)</th> <th>reference</th> <th>implementation</th></tr> </tfoot></table>
-    </div>
-</section>
-
-<section class="details-shell" id="problem-details" aria-live="polite">
-    <h3 class="details-title">Problem details</h3>
-    <p class="details-hint" id="problem-details-hint">Click a table row to inspect full details.</p>
-    <dl class="details-grid" id="problem-details-content"></dl>
-    <div class="snippet-shell" id="problem-snippet-shell">
-        <h4 class="snippet-title">README.md</h4>
-        <p class="snippet-hint" id="problem-readme-hint">README content for this snippet folder will appear here.</p>
-        <div class="snippet-readme markdown-body" id="problem-readme-content"></div>
-        <h4 class="snippet-title">Snippet: <code id="problem-snippet-path">call_{problem_id}.py</code></h4>
-        <p class="snippet-meta"><a id="problem-snippet-folder-link" href="#" target="_blank" rel="noopener noreferrer">Open snippet folder</a></p>
-        <p class="snippet-hint" id="problem-snippet-hint">Select a problem to check for an available code snippet.</p>
-        <pre class="snippet-code"><code id="problem-snippet-content"></code></pre>
-    </div>
-</section>
+<section class="table-shell">
+    <div class="table-toolbar">
+        <details class="column-toolbar-disclosure">
+            <summary class="toolbar-title">Visible Columns</summary>
+            <div class="toolbar-actions">
+                <button type="button" class="toolbar-btn" id="show-all-columns">Show all</button>
+                <button type="button" class="toolbar-btn" id="hide-all-columns">Hide all</button>
+            </div>
+            <div class="column-controls">
+                <label class="column-chip"><input class="col-toggle" type="checkbox" data-column="0"><span>ID</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="1" checked><span>Name</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="2" checked><span>Type</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="3" checked><span>Variable Types</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="4" checked><span>Total Variables</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="5" checked><span>Objectives</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="6" checked><span>Properties</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="7" checked><span>Constraint Types</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="8" checked><span>Total Constraints</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="9" checked><span>Dynamics</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="10" checked><span>Noise</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="11" checked><span>Partial Evaluations</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="12" checked><span>Independent Objectives</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="13" checked><span>Fidelity Levels</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="14"><span>Full Name</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="15"><span>Description</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="16"><span>Tags</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="17"><span>References</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="18"><span>Implementations</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="19"><span>Modality</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="20"><span>Examples</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="21"><span>Source</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="22"><span>Binary Vars</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="23"><span>Categorical Vars</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="24"><span>Continuous Vars</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="25"><span>Integer Vars</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="26"><span>Implementation Names</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="27"><span>Implementation Languages</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="28"><span>Implementation Evaluation Times</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="29"><span>Implementation Links</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="30"><span>Implementation Descriptions</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="31"><span>Implementation Requirements</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="32"><span>Hard Box Constraints</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="33"><span>Soft Box Constraints</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="34"><span>Hard Linear Constraints</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="35"><span>Soft Linear Constraints</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="36"><span>Hard Function Constraints</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="37"><span>Soft Function Constraints</span></label>
+            </div>
+        </details>
+    </div>
+    <div class="table-wrap">
+        <table class="dataframe display compact display styled-table" id="problems">
+  <thead>
+    <tr style="text-align: right;">
+      <th>ID</th>
+      <th>Name</th>
+      <th>Type</th>
+      <th>Variable Types</th>
+      <th>Total Variables</th>
+      <th>Objectives</th>
+      <th>Properties</th>
+      <th>Constraint Types</th>
+      <th>Total Constraints</th>
+      <th>Dynamics</th>
+      <th>Noise</th>
+      <th>Partial Evaluations</th>
+      <th>Independent Objectives</th>
+      <th>Fidelity Levels</th>
+      <th>Full Name</th>
+      <th>Description</th>
+      <th>Tags</th>
+      <th>References</th>
+      <th>Implementations</th>
+      <th>Modality</th>
+      <th>Examples</th>
+      <th>Source</th>
+      <th>Binary Vars</th>
+      <th>Categorical Vars</th>
+      <th>Continuous Vars</th>
+      <th>Integer Vars</th>
+      <th>Implementation Names</th>
+      <th>Implementation Languages</th>
+      <th>Implementation Evaluation Times</th>
+      <th>Implementation Links</th>
+      <th>Implementation Descriptions</th>
+      <th>Implementation Requirements</th>
+      <th>Hard Box Constraints</th>
+      <th>Soft Box Constraints</th>
+      <th>Hard Linear Constraints</th>
+      <th>Soft Linear Constraints</th>
+      <th>Hard Function Constraints</th>
+      <th>Soft Function Constraints</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr data-problem-id="fn_ato">
+      <td>fn_ato</td>
+      <td>ATO</td>
+      <td>Problem</td>
+      <td>continuous</td>
+      <td>10</td>
+      <td>2</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>no</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Parameters of the Modules of the Automatic Train Operation are optimized; two objectives: minimizing energy consumption and minimizing driving duration.</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>unimodal</td>
+      <td></td>
+      <td>real-world</td>
+      <td></td>
+      <td></td>
+      <td>10</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="fn_building_spatial">
+      <td>fn_building_spatial</td>
+      <td>Building spatial design</td>
+      <td>Problem</td>
+      <td>binary | continuous</td>
+      <td>>=2</td>
+      <td>2</td>
+      <td></td>
+      <td>unknown | box</td>
+      <td>>=2</td>
+      <td></td>
+      <td></td>
+      <td>no</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Optimise the spatial layout of a building to minimise energy consumption for climate control and minimise the strain on the structure. Many hard constraints; mixed-variable (continuous+binary); expensive evaluations.</td>
+      <td></td>
+      <td>Building spatial design, <a href="https://hdl.handle.net/1887/81789">https://hdl.handle.net/1887/81789</a></td>
+      <td>impl_bso_toolbox</td>
+      <td></td>
+      <td></td>
+      <td>real-world</td>
+      <td>>=1</td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>BSO-toolbox</td>
+      <td>C++</td>
+      <td>~1s (smallest) to ~40s (larger)</td>
+      <td><a href="https://github.com/TUe-excellent-buildings/BSO-toolbox">https://github.com/TUe-excellent-buildings/BSO-toolbox</a></td>
+      <td>Building Spatial Design toolbox (TU/e)</td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="fn_convex_dtlz2">
+      <td>fn_convex_dtlz2</td>
+      <td>Convex DTLZ2</td>
+      <td>Problem</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>[10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Variant of DTLZ2 with a convex Pareto front (instead of concave)</td>
+      <td></td>
+      <td>Convex DTLZ2, <a href="https://doi.org/10.1109/TEVC.2013.2281535">https://doi.org/10.1109/TEVC.2013.2281535</a></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="fn_emdo">
+      <td>fn_emdo</td>
+      <td>Electric Motor Design Optimization</td>
+      <td>Problem</td>
+      <td>integer | continuous</td>
+      <td>26</td>
+      <td>1</td>
+      <td>noisy</td>
+      <td>box | unknown</td>
+      <td>>=14</td>
+      <td></td>
+      <td>noisy</td>
+      <td>no</td>
+      <td></td>
+      <td></td>
+      <td>Electric Motor Design Optimization</td>
+      <td># Goal\nFind a design of a synchronous electric motor for power steering systems that minimizes costs and satisfies all constraints.\n\n# Motivation\nChallenging to find good solutions in a limited time.\n\n# Key Challenges\n* Time-consuming solution evaluation\n* Highly-constrained problem\n* Constraints are multimodal\n\nThis is not an available problem, but could be interesting to show to researchers which difficulties appear in real-world problems.</td>
+      <td></td>
+      <td>A Multi-Step Evaluation Process in Electric Motor Design, Tea Tušar; Peter Korošec; Bogdan Filipič, <a href="https://dis.ijs.si/tea/Publications/Tusar23Multistep.pdf">https://dis.ijs.si/tea/Publications/Tusar23Multistep.pdf</a></td>
+      <td>impl_emdo</td>
+      <td>multimodal</td>
+      <td></td>
+      <td>real-world</td>
+      <td></td>
+      <td></td>
+      <td>13</td>
+      <td>13</td>
+      <td>Electric Motor Design Optimization</td>
+      <td>Python</td>
+      <td>8 minutes</td>
+      <td></td>
+      <td>Not publicly available</td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="fn_fleetopt">
+      <td>fn_fleetopt</td>
+      <td>FleetOpt</td>
+      <td>Problem</td>
+      <td>integer</td>
+      <td>{54, 13208}</td>
+      <td>1</td>
+      <td></td>
+      <td>unknown</td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td>yes</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>UK healthcare organisation fleet optimisation: reduce the fleet of non-emergency healthcare trip vehicles while still ensuring all trips can be covered. Bilevel: upper level 54 vars, lower level 13208 vars.</td>
+      <td></td>
+      <td>FleetOpt, <a href="https://dl.acm.org/doi/abs/10.1145/3638530.3664137">https://dl.acm.org/doi/abs/10.1145/3638530.3664137</a></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>real-world</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>{54, 13208}</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="fn_gasoline">
+      <td>fn_gasoline</td>
+      <td>Gasoline direct injection engine design</td>
+      <td>Problem</td>
+      <td>integer | continuous</td>
+      <td>14</td>
+      <td>2</td>
+      <td>multi-fidelity</td>
+      <td>unknown</td>
+      <td>5</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>[1, 2]</td>
+      <td></td>
+      <td>Multi-objective optimization to minimize fuel consumption and NOx emissions over a two-minute dynamic duty cycle, subject to five constraints (turbine inlet temperature, knock occurrences, peak cylinder pressure, peak cylinder pressure rise, total work). Seven decision variables cover hardware choices and engine control parameters.</td>
+      <td></td>
+      <td>Gasoline direct injection engine design, <a href="https://doi.org/10.1016/j.ejor.2022.08.032">https://doi.org/10.1016/j.ejor.2022.08.032</a></td>
+      <td>impl_gasoline</td>
+      <td></td>
+      <td></td>
+      <td>real-world</td>
+      <td></td>
+      <td></td>
+      <td>7</td>
+      <td>7</td>
+      <td>Gasoline direct injection engine design</td>
+      <td>Matlab Simulink / Wave RT</td>
+      <td></td>
+      <td><a href="https://doi.org/10.1016/j.ejor.2022.08.032">https://doi.org/10.1016/j.ejor.2022.08.032</a></td>
+      <td>Proprietary Matlab Simulink + Wave RT co-simulation</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="fn_invdeceptive_deceptive_rotell">
+      <td>fn_invdeceptive_deceptive_rotell</td>
+      <td>InverseDeceptiveTrap+RotatedEllipsoid / DeceptiveTrap+RotatedEllipsoid</td>
+      <td>Problem</td>
+      <td>binary | continuous</td>
+      <td>>=2</td>
+      <td>2</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Mixed-variable multi-objective test problems, <a href="https://doi.org/10.1145/3449726.3459521">https://doi.org/10.1145/3449726.3459521</a></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>artificial</td>
+      <td>>=1</td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="fn_inverted_dtlz1">
+      <td>fn_inverted_dtlz1</td>
+      <td>Inverted DTLZ1</td>
+      <td>Problem</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>[10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Variant of DTLZ1 with an inverted Pareto front</td>
+      <td></td>
+      <td>Inverted DTLZ1, <a href="https://doi.org/10.1109/TEVC.2013.2281534">https://doi.org/10.1109/TEVC.2013.2281534</a></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="fn_jsec2019">
+      <td>fn_jsec2019</td>
+      <td>JSEC2019</td>
+      <td>Problem</td>
+      <td>continuous</td>
+      <td>32</td>
+      <td>[1, 2, 3, 4, 5]</td>
+      <td></td>
+      <td>unknown</td>
+      <td>22</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>expensive evaluations 3s; 22 constraints</td>
+      <td></td>
+      <td>JPNSEC EC-Symposium 2019 competition, <a href="http://www.jpnsec.org/files/competition2019/EC-Symposium-2019-Competition-English.html">http://www.jpnsec.org/files/competition2019/EC-Symposium-2019-Competition-English.html</a></td>
+      <td>impl_jsec2019</td>
+      <td></td>
+      <td></td>
+      <td>real-world</td>
+      <td></td>
+      <td></td>
+      <td>32</td>
+      <td></td>
+      <td>JSEC 2019 competition</td>
+      <td></td>
+      <td>3s</td>
+      <td><a href="http://www.jpnsec.org/files/competition2019/EC-Symposium-2019-Competition-English.html">http://www.jpnsec.org/files/competition2019/EC-Symposium-2019-Competition-English.html</a></td>
+      <td>JPNSEC EC-Symposium 2019 competition problem</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="fn_onemax_sphere_deceptive_rotell">
+      <td>fn_onemax_sphere_deceptive_rotell</td>
+      <td>Onemax+Sphere / DeceptiveTrap+RotatedEllipsoid</td>
+      <td>Problem</td>
+      <td>binary | continuous</td>
+      <td>>=2</td>
+      <td>2</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Mixed-variable multi-objective test problems, <a href="https://doi.org/10.1145/3449726.3459521">https://doi.org/10.1145/3449726.3459521</a></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>artificial</td>
+      <td>>=1</td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="fn_onemax_sphere_zeromax_sphere">
+      <td>fn_onemax_sphere_zeromax_sphere</td>
+      <td>Onemax+Sphere / Zeromax+Sphere</td>
+      <td>Problem</td>
+      <td>binary | continuous</td>
+      <td>>=2</td>
+      <td>2</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Onemax+Sphere / Zeromax+Sphere, <a href="https://doi.org/10.1145/3449726.3459521">https://doi.org/10.1145/3449726.3459521</a></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>artificial</td>
+      <td>>=1</td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="fn_radar_waveform">
+      <td>fn_radar_waveform</td>
+      <td>Radar waveform</td>
+      <td>Problem</td>
+      <td>integer</td>
+      <td>4-12</td>
+      <td>9</td>
+      <td></td>
+      <td>unknown</td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Radar waveform design, <a href="https://doi.org/10.1007/978-3-540-70928-2_53">https://doi.org/10.1007/978-3-540-70928-2_53</a></td>
+      <td>impl_radar_waveform</td>
+      <td></td>
+      <td></td>
+      <td>real-world</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>4-12</td>
+      <td>Evan Hughes radar waveform code</td>
+      <td></td>
+      <td></td>
+      <td><a href="http://code.evanhughes.org/">http://code.evanhughes.org/</a></td>
+      <td>Radar waveform design reference implementation</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="gen_beacon">
+      <td>gen_beacon</td>
+      <td>BEACON</td>
+      <td>Generator</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>2</td>
+      <td></td>
+      <td>box</td>
+      <td>0</td>
+      <td></td>
+      <td></td>
+      <td>no</td>
+      <td></td>
+      <td></td>
+      <td>Continuous Bi-objective Benchmark problems with Explicit Adjustable COrrelatioN control</td>
+      <td>Generator for bi-objective benchmark problems with explicitly controlled correlations in continuous spaces. Multimodal with random structure.</td>
+      <td></td>
+      <td>BEACON, <a href="https://dl.acm.org/doi/10.1145/3712255.3734303">https://dl.acm.org/doi/10.1145/3712255.3734303</a></td>
+      <td>impl_beacon</td>
+      <td>multimodal</td>
+      <td></td>
+      <td>artificial</td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>BEACON</td>
+      <td>Python</td>
+      <td>negligible</td>
+      <td><a href="https://github.com/Stebbet/BEACON/">https://github.com/Stebbet/BEACON/</a></td>
+      <td>Continuous Bi-objective Benchmark with Explicit Adjustable COrrelatioN control</td>
+      <td></td>
+      <td>0</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="gen_bono_bench">
+      <td>gen_bono_bench</td>
+      <td>BONO-Bench</td>
+      <td>Generator</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>2</td>
+      <td></td>
+      <td>box</td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td>no</td>
+      <td></td>
+      <td></td>
+      <td>Bi-objective Numerical Optimization Benchmark</td>
+      <td>Bi-objective problem generator and suite with scalable continuous decision space. Features complex problem properties and Pareto front approximations with error guarantees for the hypervolume and exact R2 indicators.</td>
+      <td></td>
+      <td></td>
+      <td>impl_bonobench</td>
+      <td>multimodal</td>
+      <td></td>
+      <td>artificial</td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>BONO-Bench</td>
+      <td>Python</td>
+      <td></td>
+      <td><a href="https://github.com/schaepermeier/bonobench">https://github.com/schaepermeier/bonobench</a></td>
+      <td>Bi-objective Numerical Optimization Benchmark (BONO-Bench)</td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="gen_ealain">
+      <td>gen_ealain</td>
+      <td>Ealain</td>
+      <td>Generator</td>
+      <td>binary | continuous | integer</td>
+      <td>>=3</td>
+      <td>[1, 10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
+      <td>dynamic | multi-fidelity</td>
+      <td>unknown</td>
+      <td>>=1</td>
+      <td>optional</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>[1, 2]</td>
+      <td></td>
+      <td>Real-world-like, easily extensible to increase complexity</td>
+      <td></td>
+      <td>Ealain, <a href="https://doi.org/10.1145/3638530.3654299">https://doi.org/10.1145/3638530.3654299</a></td>
+      <td>impl_ealain</td>
+      <td></td>
+      <td></td>
+      <td>real-world-like</td>
+      <td>>=1</td>
+      <td></td>
+      <td>>=1</td>
+      <td>>=1</td>
+      <td>Ealain</td>
+      <td></td>
+      <td></td>
+      <td><a href="https://github.com/qrenau/Ealain">https://github.com/qrenau/Ealain</a></td>
+      <td>Real-world-like extensible benchmark problem generator</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="gen_gnbg">
+      <td>gen_gnbg</td>
+      <td>GNBG</td>
+      <td>Generator</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Generator counterpart of GNBG.</td>
+      <td></td>
+      <td>GNBG, <a href="https://arxiv.org/abs/2312.07083">https://arxiv.org/abs/2312.07083</a></td>
+      <td>impl_gnbg</td>
+      <td></td>
+      <td></td>
+      <td>artificial</td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>GNBG Generator</td>
+      <td></td>
+      <td></td>
+      <td><a href="https://github.com/Danial-Yazdani/GNBG-Generator">https://github.com/Danial-Yazdani/GNBG-Generator</a></td>
+      <td>Generalized Numerical Benchmark Generator</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="gen_gnbg_ii">
+      <td>gen_gnbg_ii</td>
+      <td>GNBG-II</td>
+      <td>Generator</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Generator counterpart of GNBG-II.</td>
+      <td></td>
+      <td>GNBG-II, <a href="https://dl.acm.org/doi/pdf/10.1145/3712255.3734271">https://dl.acm.org/doi/pdf/10.1145/3712255.3734271</a></td>
+      <td>["impl_gnbg_ii", "impl_iohgnbg"]</td>
+      <td></td>
+      <td></td>
+      <td>artificial</td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>GNBG-II | IOHGNBG</td>
+      <td></td>
+      <td></td>
+      <td><a href="https://github.com/rohitsalgotra/GNBG-II">https://github.com/rohitsalgotra/GNBG-II</a> | <a href="https://github.com/IOHprofiler/IOHGNBG">https://github.com/IOHprofiler/IOHGNBG</a></td>
+      <td>Generalized Numerical Benchmark Generator version 2 | IOHprofiler version of GNBG</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="gen_gpd">
+      <td>gen_gpd</td>
+      <td>GPD</td>
+      <td>Generator</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>[10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
+      <td>noisy</td>
+      <td>unknown</td>
+      <td>>=1</td>
+      <td></td>
+      <td>optional</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>GPD generator, <a href="https://doi.org/10.1016/j.asoc.2020.106139">https://doi.org/10.1016/j.asoc.2020.106139</a></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="gen_iohclustering">
+      <td>gen_iohclustering</td>
+      <td>IOHClustering</td>
+      <td>Generator</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Generator counterpart of the IOHClustering suite.</td>
+      <td></td>
+      <td>IOHClustering, <a href="https://arxiv.org/pdf/2505.09233">https://arxiv.org/pdf/2505.09233</a></td>
+      <td>impl_iohclustering</td>
+      <td>multimodal</td>
+      <td></td>
+      <td>artificial-from-real-data</td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>IOHClustering</td>
+      <td></td>
+      <td></td>
+      <td><a href="https://github.com/IOHprofiler/IOHClustering">https://github.com/IOHprofiler/IOHClustering</a></td>
+      <td>Clustering-based optimization benchmark built on ML datasets</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="gen_ma_bbob">
+      <td>gen_ma_bbob</td>
+      <td>MA-BBOB</td>
+      <td>Generator</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Generator that creates affine combinations of BBOB functions</td>
+      <td></td>
+      <td>MA-BBOB, <a href="https://doi.org/10.1145/3673908">https://doi.org/10.1145/3673908</a></td>
+      <td>["impl_iohexperimenter", "impl_ma_bbob"]</td>
+      <td>multimodal</td>
+      <td></td>
+      <td>artificial</td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>IOHexperimenter | MA-BBOB (IOHexperimenter)</td>
+      <td>C++/Python</td>
+      <td></td>
+      <td><a href="https://github.com/IOHprofiler/IOHexperimenter">https://github.com/IOHprofiler/IOHexperimenter</a> | <a href="https://github.com/IOHprofiler/IOHexperimenter/blob/master/example/Competitions/MA-BBOB/Example_MABBOB.ipynb">https://github.com/IOHprofiler/IOHexperimenter/blob/master/example/Competitions/MA-BBOB/Example_MABBOB.ipynb</a></td>
+      <td>IOHprofiler experimenter framework | Example notebook for MA-BBOB in IOHexperimenter</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="gen_mpm2">
+      <td>gen_mpm2</td>
+      <td>MPM2</td>
+      <td>Generator</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>nonlinear nonseparable nonsymmetric; scalable in terms of time to evaluate the objective function</td>
+      <td></td>
+      <td>MPM2 technical report TR15-01, <a href="https://ls11-www.cs.tu-dortmund.de/_media/techreports/tr15-01.pdf">https://ls11-www.cs.tu-dortmund.de/_media/techreports/tr15-01.pdf</a></td>
+      <td>impl_mpm2</td>
+      <td>multimodal</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>MPM2 (smoof)</td>
+      <td>Python</td>
+      <td></td>
+      <td><a href="https://github.com/jakobbossek/smoof/blob/master/inst/mpm2.py">https://github.com/jakobbossek/smoof/blob/master/inst/mpm2.py</a></td>
+      <td>Python implementation of MPM2 distributed with smoof</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="gen_mubqp">
+      <td>gen_mubqp</td>
+      <td>mUBQP</td>
+      <td>Generator</td>
+      <td>binary</td>
+      <td>>=1</td>
+      <td>[1, 10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>tunable variable and objective dimensions; tunable density and correlation between objectives</td>
+      <td></td>
+      <td>mUBQP benchmark, <a href="https://doi.org/10.1016/j.asoc.2013.11.008">https://doi.org/10.1016/j.asoc.2013.11.008</a></td>
+      <td>impl_mocobench</td>
+      <td>["multimodal", "quadratic"]</td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>mocobench</td>
+      <td>C++</td>
+      <td></td>
+      <td><a href="https://gitlab.com/aliefooghe/mocobench/">https://gitlab.com/aliefooghe/mocobench/</a></td>
+      <td>Multi-objective combinatorial optimization benchmark</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="gen_puboi">
+      <td>gen_puboi</td>
+      <td>PUBOi</td>
+      <td>Generator</td>
+      <td>binary</td>
+      <td>>=1</td>
+      <td>1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>no</td>
+      <td></td>
+      <td></td>
+      <td>Polynomial Unconstrained Binary Optimization with tunable importance</td>
+      <td>A benchmark in which variable importance is tunable, based on the Walsh function.</td>
+      <td></td>
+      <td>PUBOi, <a href="https://link.springer.com/chapter/10.1007/978-3-031-04148-8_12">https://link.springer.com/chapter/10.1007/978-3-031-04148-8_12</a></td>
+      <td>impl_puboi</td>
+      <td></td>
+      <td></td>
+      <td>artificial</td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>PUBO Importance Benchmark</td>
+      <td>Python / C++</td>
+      <td></td>
+      <td><a href="https://gitlab.com/verel/pubo-importance-benchmark">https://gitlab.com/verel/pubo-importance-benchmark</a></td>
+      <td>A benchmark in which variable importance is tunable, based on the Walsh function</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="gen_randoptgen">
+      <td>gen_randoptgen</td>
+      <td>RandOptGen</td>
+      <td>Generator</td>
+      <td>binary | continuous | integer</td>
+      <td>>=3</td>
+      <td>[1, 10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>no</td>
+      <td></td>
+      <td></td>
+      <td>RandOptGen</td>
+      <td>A Unified Random Problem Generator for Single- and Multi-Objective Optimization Problems with Mixed-Variable Input Spaces.</td>
+      <td></td>
+      <td></td>
+      <td>impl_randoptgen</td>
+      <td>multimodal</td>
+      <td></td>
+      <td>artificial</td>
+      <td>>=1</td>
+      <td></td>
+      <td>>=1</td>
+      <td>>=1</td>
+      <td>RandOptGen</td>
+      <td>Python</td>
+      <td>milliseconds</td>
+      <td><a href="https://github.com/MALEO-research-group/RandOptGen">https://github.com/MALEO-research-group/RandOptGen</a> | <a href="https://doi.org/10.1145/3712256.3726478">https://doi.org/10.1145/3712256.3726478</a></td>
+      <td>Unified Random Problem Generator for Single- and Multi-Objective Optimization with Mixed-Variable Input Spaces</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="gen_rho_mnk_landscapes">
+      <td>gen_rho_mnk_landscapes</td>
+      <td>ρMNK-Landscapes</td>
+      <td>Generator</td>
+      <td>binary</td>
+      <td>>=1</td>
+      <td>[1, 10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>tunable variable and objective dimensions; tunable multimodality and correlation between objectives</td>
+      <td></td>
+      <td>On the design of multi-objective evolutionary algorithms based on NK-landscapes, <a href="https://doi.org/10.1016/j.ejor.2012.12.019">https://doi.org/10.1016/j.ejor.2012.12.019</a></td>
+      <td>impl_mocobench</td>
+      <td>multimodal</td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>mocobench</td>
+      <td>C++</td>
+      <td></td>
+      <td><a href="https://gitlab.com/aliefooghe/mocobench/">https://gitlab.com/aliefooghe/mocobench/</a></td>
+      <td>Multi-objective combinatorial optimization benchmark</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="gen_rho_mtsp">
+      <td>gen_rho_mtsp</td>
+      <td>ρmTSP</td>
+      <td>Generator</td>
+      <td>unknown</td>
+      <td>>=1</td>
+      <td>[1, 10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>tunable variable and objective dimensions; tunable instance type (euclidean/random); tunable correlation between objectives</td>
+      <td></td>
+      <td>On the impact of multi-objective scalability for the ρmTSP, <a href="https://doi.org/10.1007/978-3-319-45823-6_40">https://doi.org/10.1007/978-3-319-45823-6_40</a></td>
+      <td>impl_mocobench</td>
+      <td>["multimodal", "quadratic"]</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>mocobench</td>
+      <td>C++</td>
+      <td></td>
+      <td><a href="https://gitlab.com/aliefooghe/mocobench/">https://gitlab.com/aliefooghe/mocobench/</a></td>
+      <td>Multi-objective combinatorial optimization benchmark</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="gen_wmodel">
+      <td>gen_wmodel</td>
+      <td>W-model</td>
+      <td>Generator</td>
+      <td>binary</td>
+      <td>>=1</td>
+      <td>1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Tunable generator for binary optimization based on several difficulty features</td>
+      <td></td>
+      <td>W-model, <a href="https://dl.acm.org/doi/abs/10.1145/3205651.3208240">https://dl.acm.org/doi/abs/10.1145/3205651.3208240</a></td>
+      <td>impl_wmodel</td>
+      <td></td>
+      <td></td>
+      <td>artificial</td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>BBDOB W-Model</td>
+      <td></td>
+      <td></td>
+      <td><a href="https://github.com/thomasWeise/BBDOB_W_Model">https://github.com/thomasWeise/BBDOB_W_Model</a></td>
+      <td>Tunable generator for binary optimization</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_amvop">
+      <td>suite_amvop</td>
+      <td>AMVOP</td>
+      <td>Suite</td>
+      <td>integer | continuous | categorical</td>
+      <td>>=3</td>
+      <td>1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>AMVOP, <a href="https://doi.org/10.1109/TEVC.2013.2281531">https://doi.org/10.1109/TEVC.2013.2281531</a></td>
+      <td></td>
+      <td>multimodal</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td>>=1</td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_bbob">
+      <td>suite_bbob</td>
+      <td>BBOB</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>COCO: a platform for comparing continuous optimizers in a black-box setting, <a href="https://doi.org/10.1080/10556788.2020.1808977">https://doi.org/10.1080/10556788.2020.1808977</a></td>
+      <td>impl_coco</td>
+      <td>multimodal</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>COCO framework</td>
+      <td>C/Python</td>
+      <td></td>
+      <td><a href="https://github.com/numbbo/coco">https://github.com/numbbo/coco</a></td>
+      <td>Comparing Continuous Optimizers: black-box optimization benchmarking platform</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_bbob_biobj">
+      <td>suite_bbob_biobj</td>
+      <td>BBOB-biobj</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>2-40</td>
+      <td>2</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>BBOB bi-objective test suite, <a href="https://doi.org/10.48550/arXiv.1604.00359">https://doi.org/10.48550/arXiv.1604.00359</a></td>
+      <td>impl_coco</td>
+      <td>multimodal</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>2-40</td>
+      <td></td>
+      <td>COCO framework</td>
+      <td>C/Python</td>
+      <td></td>
+      <td><a href="https://github.com/numbbo/coco">https://github.com/numbbo/coco</a></td>
+      <td>Comparing Continuous Optimizers: black-box optimization benchmarking platform</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_bbob_biobj_mixint">
+      <td>suite_bbob_biobj_mixint</td>
+      <td>BBOB-biobj-mixint</td>
+      <td>Suite</td>
+      <td>continuous | integer</td>
+      <td>10-320</td>
+      <td>2</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>BBOB bi-objective mixed-integer test suite, <a href="https://doi.org/10.1145/3321707.3321868">https://doi.org/10.1145/3321707.3321868</a></td>
+      <td>impl_coco</td>
+      <td>multimodal</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>5-160</td>
+      <td>5-160</td>
+      <td>COCO framework</td>
+      <td>C/Python</td>
+      <td></td>
+      <td><a href="https://github.com/numbbo/coco">https://github.com/numbbo/coco</a></td>
+      <td>Comparing Continuous Optimizers: black-box optimization benchmarking platform</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_bbob_constrained">
+      <td>suite_bbob_constrained</td>
+      <td>BBOB-constrained</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>2-40</td>
+      <td>1</td>
+      <td></td>
+      <td>unknown</td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>bbob-constrained documentation, <a href="http://numbbo.github.io/coco-doc/bbob-constrained/">http://numbbo.github.io/coco-doc/bbob-constrained/</a></td>
+      <td>impl_coco</td>
+      <td>multimodal</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>2-40</td>
+      <td></td>
+      <td>COCO framework</td>
+      <td>C/Python</td>
+      <td></td>
+      <td><a href="https://github.com/numbbo/coco">https://github.com/numbbo/coco</a></td>
+      <td>Comparing Continuous Optimizers: black-box optimization benchmarking platform</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_bbob_largescale">
+      <td>suite_bbob_largescale</td>
+      <td>BBOB-largescale</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>20-640</td>
+      <td>1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>BBOB large-scale test suite, <a href="https://doi.org/10.48550/arXiv.1903.06396">https://doi.org/10.48550/arXiv.1903.06396</a></td>
+      <td>impl_coco</td>
+      <td>multimodal</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>20-640</td>
+      <td></td>
+      <td>COCO framework</td>
+      <td>C/Python</td>
+      <td></td>
+      <td><a href="https://github.com/numbbo/coco">https://github.com/numbbo/coco</a></td>
+      <td>Comparing Continuous Optimizers: black-box optimization benchmarking platform</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_bbob_mixint">
+      <td>suite_bbob_mixint</td>
+      <td>BBOB-mixint</td>
+      <td>Suite</td>
+      <td>continuous | integer</td>
+      <td>10-320</td>
+      <td>1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>BBOB mixed-integer test suite, <a href="https://doi.org/10.1145/3321707.3321868">https://doi.org/10.1145/3321707.3321868</a></td>
+      <td>impl_coco</td>
+      <td>multimodal</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>5-160</td>
+      <td>5-160</td>
+      <td>COCO framework</td>
+      <td>C/Python</td>
+      <td></td>
+      <td><a href="https://github.com/numbbo/coco">https://github.com/numbbo/coco</a></td>
+      <td>Comparing Continuous Optimizers: black-box optimization benchmarking platform</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_bbob_noisy">
+      <td>suite_bbob_noisy</td>
+      <td>BBOB-noisy</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>1</td>
+      <td>noisy</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>noisy</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Real-parameter black-box optimization benchmarking: noisy functions definitions, <a href="https://hal.inria.fr/inria-00369466">https://hal.inria.fr/inria-00369466</a></td>
+      <td>impl_coco_legacy</td>
+      <td>multimodal</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>COCO legacy (bbob-noisy)</td>
+      <td>C/Python</td>
+      <td></td>
+      <td><a href="https://web.archive.org/web/20210416065610/https://coco.gforge.inria.fr/doku.php?id=downloads">https://web.archive.org/web/20210416065610/https://coco.gforge.inria.fr/doku.php?id=downloads</a></td>
+      <td>Archived COCO download page that hosted the bbob-noisy suite</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_bp">
+      <td>suite_bp</td>
+      <td>BP</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>[10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
+      <td>noisy</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>unknown</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>BP benchmark, <a href="https://doi.org/10.1109/CEC.2019.8790277">https://doi.org/10.1109/CEC.2019.8790277</a></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_brachytherapy">
+      <td>suite_brachytherapy</td>
+      <td>Brachytherapy treatment planning</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>100-500</td>
+      <td>[2, 3]</td>
+      <td>multi-fidelity</td>
+      <td>unknown</td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td>yes</td>
+      <td></td>
+      <td>[1, 2]</td>
+      <td>Brachytherapy treatment planning</td>
+      <td>Treatment planning for internal radiation therapy. Multi-objective with aggregated objectives; no public source code.</td>
+      <td></td>
+      <td>Brachytherapy treatment planning, <a href="https://www.sciencedirect.com/science/article/pii/S1538472123016781">https://www.sciencedirect.com/science/article/pii/S1538472123016781</a></td>
+      <td></td>
+      <td>multimodal</td>
+      <td></td>
+      <td>real-world</td>
+      <td></td>
+      <td></td>
+      <td>100-500</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_car_structure">
+      <td>suite_car_structure</td>
+      <td>Car structure</td>
+      <td>Suite</td>
+      <td>integer</td>
+      <td>144-222</td>
+      <td>2</td>
+      <td></td>
+      <td>unknown</td>
+      <td>54</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>54 constraints</td>
+      <td></td>
+      <td>Car structure design benchmark, <a href="https://doi.org/10.1145/3205651.3205702">https://doi.org/10.1145/3205651.3205702</a></td>
+      <td>impl_car_structure</td>
+      <td></td>
+      <td></td>
+      <td>real-world</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>144-222</td>
+      <td>Car-structure benchmark</td>
+      <td></td>
+      <td></td>
+      <td><a href="http://ladse.eng.isas.jaxa.jp/benchmark/">http://ladse.eng.isas.jaxa.jp/benchmark/</a></td>
+      <td>JAXA LADSE benchmark problems</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_cdmp">
+      <td>suite_cdmp</td>
+      <td>CDMP</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>[10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
+      <td>dynamic | noisy</td>
+      <td>unknown</td>
+      <td>>=1</td>
+      <td>unknown</td>
+      <td>unknown</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>CDMP benchmark, <a href="https://doi.org/10.1145/3321707.3321878">https://doi.org/10.1145/3321707.3321878</a></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_cec2013">
+      <td>suite_cec2013</td>
+      <td>CEC2013</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>suite used for cec2013 competition. Also in IOH.</td>
+      <td></td>
+      <td>CEC2013 definitions, <a href="https://peerj.com/articles/cs-2671/CEC2013.pdf">https://peerj.com/articles/cs-2671/CEC2013.pdf</a></td>
+      <td>["impl_cec2013", "impl_iohexperimenter"]</td>
+      <td></td>
+      <td></td>
+      <td>artificial</td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>IOHexperimenter | CEC2013 reference code</td>
+      <td>C++/Python</td>
+      <td></td>
+      <td><a href="https://github.com/IOHprofiler/IOHexperimenter">https://github.com/IOHprofiler/IOHexperimenter</a> | <a href="https://github.com/P-N-Suganthan/CEC2013">https://github.com/P-N-Suganthan/CEC2013</a></td>
+      <td>IOHprofiler experimenter framework | Suganthan's reference implementation</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_cec2015_dmoo">
+      <td>suite_cec2015_dmoo</td>
+      <td>CEC2015-DMOO</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>0</td>
+      <td>[2, 3]</td>
+      <td>dynamic</td>
+      <td>unknown</td>
+      <td>>=1</td>
+      <td>dynamic</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Benchmark Functions for CEC 2015 Special Session and Competition on Dynamic Multi-objective Optimization</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>0</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_cec2018_dt">
+      <td>suite_cec2018_dt</td>
+      <td>CEC2018 DT</td>
+      <td>Suite</td>
+      <td>unknown</td>
+      <td>>=1</td>
+      <td>[2, 3]</td>
+      <td>dynamic</td>
+      <td></td>
+      <td></td>
+      <td>dynamic</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>CEC2018 Competition on Dynamic Multiobjective Optimisation</td>
+      <td>14 problems. Time-dependent: Pareto front/Pareto set geometry; irregular Pareto front shapes; variable-linkage; number of disconnected Pareto front segments; etc.</td>
+      <td></td>
+      <td>CEC2018 DMOP Competition TR, <a href="https://www.academia.edu/download/94499025/TR-CEC2018-DMOP-Competition.pdf">https://www.academia.edu/download/94499025/TR-CEC2018-DMOP-Competition.pdf</a></td>
+      <td>impl_pymoo</td>
+      <td></td>
+      <td></td>
+      <td>artificial</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>pymoo</td>
+      <td>Python</td>
+      <td></td>
+      <td><a href="https://github.com/anyoptimization/pymoo">https://github.com/anyoptimization/pymoo</a></td>
+      <td>Multi-objective optimization in Python</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_cec2022">
+      <td>suite_cec2022</td>
+      <td>CEC2022</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>suite used for cec2022 competition. Also in IOH.</td>
+      <td></td>
+      <td>CEC2022 TR, <a href="https://github.com/P-N-Suganthan/2022-SO-BO/blob/main/CEC2022%20TR.pdf">https://github.com/P-N-Suganthan/2022-SO-BO/blob/main/CEC2022%20TR.pdf</a></td>
+      <td>["impl_cec2022", "impl_iohexperimenter"]</td>
+      <td></td>
+      <td></td>
+      <td>artificial</td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>IOHexperimenter | CEC2022 reference code</td>
+      <td>C++/Python</td>
+      <td></td>
+      <td><a href="https://github.com/IOHprofiler/IOHexperimenter">https://github.com/IOHprofiler/IOHexperimenter</a> | <a href="https://github.com/P-N-Suganthan/2022-SO-BO">https://github.com/P-N-Suganthan/2022-SO-BO</a></td>
+      <td>IOHprofiler experimenter framework | Suganthan's reference implementation</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_cfd">
+      <td>suite_cfd</td>
+      <td>CFD</td>
+      <td>Suite</td>
+      <td>unknown</td>
+      <td>>=1</td>
+      <td>[1, 2]</td>
+      <td></td>
+      <td>unknown</td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>expensive evaluations 30s-15m</td>
+      <td></td>
+      <td>CFD test problem suite, <a href="https://doi.org/10.1007/978-3-319-99259-4_24">https://doi.org/10.1007/978-3-319-99259-4_24</a></td>
+      <td>impl_cfd</td>
+      <td></td>
+      <td></td>
+      <td>real-world</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>CFD test problem suite</td>
+      <td></td>
+      <td>30s-15m</td>
+      <td><a href="https://bitbucket.org/arahat/cfd-test-problem-suite">https://bitbucket.org/arahat/cfd-test-problem-suite</a></td>
+      <td>Expensive real-world CFD-based test problems</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_cre">
+      <td>suite_cre</td>
+      <td>CRE</td>
+      <td>Suite</td>
+      <td>integer | continuous</td>
+      <td>6-14</td>
+      <td>[2, 3, 4, 5]</td>
+      <td></td>
+      <td>unknown</td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Easy-to-evaluate real-world multi-objective optimization problems, Ryoji Tanabe; Hisao Ishibuchi, <a href="https://doi.org/10.1016/j.asoc.2020.106078">https://doi.org/10.1016/j.asoc.2020.106078</a></td>
+      <td>impl_reproblems</td>
+      <td></td>
+      <td></td>
+      <td>real-world-like</td>
+      <td></td>
+      <td></td>
+      <td>3-7</td>
+      <td>3-7</td>
+      <td>reproblems</td>
+      <td>Python</td>
+      <td></td>
+      <td><a href="https://github.com/ryojitanabe/reproblems">https://github.com/ryojitanabe/reproblems</a></td>
+      <td>Real-world inspired multi-objective optimization problem suite</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_cuter">
+      <td>suite_cuter</td>
+      <td>CUTEr</td>
+      <td>Suite</td>
+      <td>binary | continuous | integer</td>
+      <td>>=3</td>
+      <td>1</td>
+      <td></td>
+      <td>unknown</td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td>no</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>A constrained and unconstrained testing environment.</td>
+      <td></td>
+      <td>CUTEr, <a href="https://dl.acm.org/doi/10.1145/962437.962439">https://dl.acm.org/doi/10.1145/962437.962439</a></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>artificial</td>
+      <td>>=1</td>
+      <td></td>
+      <td>>=1</td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_cutest">
+      <td>suite_cutest</td>
+      <td>CUTEst</td>
+      <td>Suite</td>
+      <td>binary | continuous | integer</td>
+      <td>>=3</td>
+      <td>1</td>
+      <td></td>
+      <td>unknown | box</td>
+      <td>>=2</td>
+      <td></td>
+      <td></td>
+      <td>no</td>
+      <td></td>
+      <td></td>
+      <td>Constrained and Unconstrained Testing Environment with safe threads</td>
+      <td>CUTEst for optimization software</td>
+      <td></td>
+      <td>CUTEst, <a href="https://link.springer.com/article/10.1007/s10589-014-9687-3">https://link.springer.com/article/10.1007/s10589-014-9687-3</a></td>
+      <td>impl_pycutest</td>
+      <td>multimodal</td>
+      <td></td>
+      <td>artificial</td>
+      <td>>=1</td>
+      <td></td>
+      <td>>=1</td>
+      <td>>=1</td>
+      <td>pycutest</td>
+      <td>Python / C++ / Fortran</td>
+      <td></td>
+      <td><a href="https://github.com/jfowkes/pycutest">https://github.com/jfowkes/pycutest</a></td>
+      <td>Python interface to CUTEst</td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_dtlz">
+      <td>suite_dtlz</td>
+      <td>DTLZ</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>[10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Scalable multi-objective optimization test problems, Kalyanmoy Deb; Lothar Thiele; Marco Laumanns; Eckart Zitzler, <a href="https://doi.org/10.1109/CEC.2002.1007032">https://doi.org/10.1109/CEC.2002.1007032</a></td>
+      <td>impl_pymoo</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>pymoo</td>
+      <td>Python</td>
+      <td></td>
+      <td><a href="https://github.com/anyoptimization/pymoo">https://github.com/anyoptimization/pymoo</a></td>
+      <td>Multi-objective optimization in Python</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_dynamicbinval">
+      <td>suite_dynamicbinval</td>
+      <td>DynamicBinVal</td>
+      <td>Suite</td>
+      <td>binary</td>
+      <td>>=1</td>
+      <td>1</td>
+      <td>dynamic</td>
+      <td></td>
+      <td></td>
+      <td>dynamic</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Four versions of the dynamic binary value problem</td>
+      <td></td>
+      <td>DynamicBinVal, <a href="https://arxiv.org/pdf/2404.15837">https://arxiv.org/pdf/2404.15837</a></td>
+      <td>impl_iohexperimenter</td>
+      <td></td>
+      <td></td>
+      <td>artificial</td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>IOHexperimenter</td>
+      <td>C++/Python</td>
+      <td></td>
+      <td><a href="https://github.com/IOHprofiler/IOHexperimenter">https://github.com/IOHprofiler/IOHexperimenter</a></td>
+      <td>IOHprofiler experimenter framework</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_emo2017">
+      <td>suite_emo2017</td>
+      <td>EMO2017</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>4-24</td>
+      <td>2</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>BBComp EMO 2017, <a href="https://www.ini.rub.de/PEOPLE/glasmtbl/projects/bbcomp/">https://www.ini.rub.de/PEOPLE/glasmtbl/projects/bbcomp/</a></td>
+      <td>impl_emo2017</td>
+      <td></td>
+      <td></td>
+      <td>real-world</td>
+      <td></td>
+      <td></td>
+      <td>4-24</td>
+      <td></td>
+      <td>EMO 2017 real-world problems</td>
+      <td></td>
+      <td></td>
+      <td><a href="https://www.ini.rub.de/PEOPLE/glasmtbl/projects/bbcomp/downloads/realworld-problems-bbcomp-EMO-2017.zip">https://www.ini.rub.de/PEOPLE/glasmtbl/projects/bbcomp/downloads/realworld-problems-bbcomp-EMO-2017.zip</a></td>
+      <td>BBComp EMO-2017 real-world problem archive</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_etmof">
+      <td>suite_etmof</td>
+      <td>ETMOF</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>25-10000</td>
+      <td>[10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 2, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 3, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 4, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 5, 50, 6, 7, 8, 9]</td>
+      <td>dynamic</td>
+      <td></td>
+      <td></td>
+      <td>dynamic</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Evolutionary many-task optimization framework, <a href="https://doi.org/10.48550/arXiv.2110.08033">https://doi.org/10.48550/arXiv.2110.08033</a></td>
+      <td>impl_etmof</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>25-10000</td>
+      <td></td>
+      <td>ETMOF</td>
+      <td></td>
+      <td></td>
+      <td><a href="https://github.com/songbai-liu/etmo">https://github.com/songbai-liu/etmo</a></td>
+      <td>Evolutionary many-task optimization framework</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_expobench">
+      <td>suite_expobench</td>
+      <td>EXPObench</td>
+      <td>Suite</td>
+      <td>categorical | continuous | integer</td>
+      <td>30-405</td>
+      <td>1</td>
+      <td>noisy</td>
+      <td>unknown | box</td>
+      <td>>=2</td>
+      <td></td>
+      <td>["observational", "real-life"]</td>
+      <td>no</td>
+      <td></td>
+      <td></td>
+      <td>EXPensive Optimization benchmark library</td>
+      <td>Wind farm layout optimization, gas filter design, pipe shape optimization, hyperparameter tuning, and hospital simulation</td>
+      <td></td>
+      <td>EXPObench, <a href="https://doi.org/10.1016/j.asoc.2023.110744">https://doi.org/10.1016/j.asoc.2023.110744</a></td>
+      <td>impl_expobench</td>
+      <td></td>
+      <td></td>
+      <td>real-world</td>
+      <td></td>
+      <td>10-135</td>
+      <td>10-135</td>
+      <td>10-135</td>
+      <td>EXPObench</td>
+      <td>Python</td>
+      <td>2 to 80 seconds</td>
+      <td><a href="https://github.com/AlgTUDelft/ExpensiveOptimBenchmark">https://github.com/AlgTUDelft/ExpensiveOptimBenchmark</a></td>
+      <td>EXPensive Optimization benchmark library (wind farm layout, gas filter design, pipe shape, hyperparameter tuning, hospital simulation)</td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_gbea">
+      <td>suite_gbea</td>
+      <td>GBEA</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>[1, 2]</td>
+      <td>noisy</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>noisy</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>expensive evaluations 5s-35s, RW-GAN-Mario and TopTrumps are part of GBEA</td>
+      <td></td>
+      <td>Game benchmark for evolutionary algorithms, <a href="https://doi.org/10.1145/3321707.3321805">https://doi.org/10.1145/3321707.3321805</a></td>
+      <td>impl_gbea</td>
+      <td>multimodal</td>
+      <td></td>
+      <td>real-world</td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>coco-gbea</td>
+      <td></td>
+      <td>5s-35s</td>
+      <td><a href="https://github.com/ttusar/coco-gbea">https://github.com/ttusar/coco-gbea</a></td>
+      <td>Game-Benchmark for Evolutionary Algorithms (COCO fork)</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_gnbg">
+      <td>suite_gnbg</td>
+      <td>GNBG</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Generalized Numerical Benchmark Generator</td>
+      <td></td>
+      <td>GNBG, <a href="https://arxiv.org/abs/2312.07083">https://arxiv.org/abs/2312.07083</a></td>
+      <td>impl_gnbg</td>
+      <td></td>
+      <td></td>
+      <td>artificial</td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>GNBG Generator</td>
+      <td></td>
+      <td></td>
+      <td><a href="https://github.com/Danial-Yazdani/GNBG-Generator">https://github.com/Danial-Yazdani/GNBG-Generator</a></td>
+      <td>Generalized Numerical Benchmark Generator</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_gnbg_ii">
+      <td>suite_gnbg_ii</td>
+      <td>GNBG-II</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Generalized Numerical Benchmark Generator (version 2). Also available in IOH.</td>
+      <td></td>
+      <td>GNBG-II, <a href="https://dl.acm.org/doi/pdf/10.1145/3712255.3734271">https://dl.acm.org/doi/pdf/10.1145/3712255.3734271</a></td>
+      <td>["impl_gnbg_ii", "impl_iohgnbg"]</td>
+      <td></td>
+      <td></td>
+      <td>artificial</td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>GNBG-II | IOHGNBG</td>
+      <td></td>
+      <td></td>
+      <td><a href="https://github.com/rohitsalgotra/GNBG-II">https://github.com/rohitsalgotra/GNBG-II</a> | <a href="https://github.com/IOHprofiler/IOHGNBG">https://github.com/IOHprofiler/IOHGNBG</a></td>
+      <td>Generalized Numerical Benchmark Generator version 2 | IOHprofiler version of GNBG</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_iohclustering">
+      <td>suite_iohclustering</td>
+      <td>IOHClustering</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Set of benchmark problems from clustering: optimization task is selecting cluster centers for a given set of data.</td>
+      <td></td>
+      <td>IOHClustering, <a href="https://arxiv.org/pdf/2505.09233">https://arxiv.org/pdf/2505.09233</a></td>
+      <td>impl_iohclustering</td>
+      <td>multimodal</td>
+      <td></td>
+      <td>artificial-from-real-data</td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>IOHClustering</td>
+      <td></td>
+      <td></td>
+      <td><a href="https://github.com/IOHprofiler/IOHClustering">https://github.com/IOHprofiler/IOHClustering</a></td>
+      <td>Clustering-based optimization benchmark built on ML datasets</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_kinematics_robotarm">
+      <td>suite_kinematics_robotarm</td>
+      <td>KinematicsRobotArm</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>21</td>
+      <td>1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Kinematics of a robot arm, <a href="https://doi.org/10.1023/A:1013258808932">https://doi.org/10.1023/A:1013258808932</a></td>
+      <td>impl_transfer_rf_bbob_rw</td>
+      <td>unimodal</td>
+      <td></td>
+      <td>real-world</td>
+      <td></td>
+      <td></td>
+      <td>21</td>
+      <td></td>
+      <td>Transfer Random Forests BBOB Real-world</td>
+      <td></td>
+      <td></td>
+      <td><a href="https://github.com/ShuaiqunPan/Transfer_Random_forests_BBOB_Real_world">https://github.com/ShuaiqunPan/Transfer_Random_forests_BBOB_Real_world</a></td>
+      <td>Real-world BBOB-like problem implementations (Porkchop, KinematicsRobotArm)</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_l1_zdt">
+      <td>suite_l1_zdt</td>
+      <td>L1-ZDT</td>
+      <td>Suite</td>
+      <td>binary | continuous</td>
+      <td>>=2</td>
+      <td>2</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Variant of ZDT with linkages between variables within groups</td>
+      <td></td>
+      <td>Linkage ZDT/DTLZ variants, <a href="https://doi.org/10.1145/1143997.1144179">https://doi.org/10.1145/1143997.1144179</a></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_l2_dtlz">
+      <td>suite_l2_dtlz</td>
+      <td>L2-DTLZ</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>[10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Variant of DTLZ2/DTLZ3 with linkages between all variables</td>
+      <td></td>
+      <td>Linkage ZDT/DTLZ variants, <a href="https://doi.org/10.1145/1143997.1144179">https://doi.org/10.1145/1143997.1144179</a></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_l2_zdt">
+      <td>suite_l2_zdt</td>
+      <td>L2-ZDT</td>
+      <td>Suite</td>
+      <td>binary | continuous</td>
+      <td>>=2</td>
+      <td>2</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Variant of ZDT with linkages between all variables</td>
+      <td></td>
+      <td>Linkage ZDT/DTLZ variants, <a href="https://doi.org/10.1145/1143997.1144179">https://doi.org/10.1145/1143997.1144179</a></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_l3_dtlz">
+      <td>suite_l3_dtlz</td>
+      <td>L3-DTLZ</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>[10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Variant of L2-DTLZ with anti-linkage mapping</td>
+      <td></td>
+      <td>Linkage ZDT/DTLZ variants, <a href="https://doi.org/10.1145/1143997.1144179">https://doi.org/10.1145/1143997.1144179</a></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_l3_zdt">
+      <td>suite_l3_zdt</td>
+      <td>L3-ZDT</td>
+      <td>Suite</td>
+      <td>binary | continuous</td>
+      <td>>=2</td>
+      <td>2</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Variant of L2-ZDT with anti-linkage mapping</td>
+      <td></td>
+      <td>Linkage ZDT/DTLZ variants, <a href="https://doi.org/10.1145/1143997.1144179">https://doi.org/10.1145/1143997.1144179</a></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_maop">
+      <td>suite_maop</td>
+      <td>MaOP</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>[10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
+      <td>noisy</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>unknown</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>MaOP benchmark, <a href="https://doi.org/10.1016/j.swevo.2019.02.003">https://doi.org/10.1016/j.swevo.2019.02.003</a></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_mechbench">
+      <td>suite_mechbench</td>
+      <td>MECHBench</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>1</td>
+      <td></td>
+      <td>unknown</td>
+      <td>{1, 2}</td>
+      <td></td>
+      <td></td>
+      <td>no</td>
+      <td></td>
+      <td></td>
+      <td>MECHBench</td>
+      <td>Set of problems inspired by Structural Mechanics Design Optimization. Embeds physical simulations (plasticity only, no fracture/damage). Unstructured/non-isotropic multimodality.</td>
+      <td></td>
+      <td>MECHBench, <a href="https://arxiv.org/abs/2511.10821">https://arxiv.org/abs/2511.10821</a></td>
+      <td>impl_mechbench</td>
+      <td>multimodal</td>
+      <td></td>
+      <td>real-world</td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>MECHBench</td>
+      <td>Python</td>
+      <td>1-7 minutes</td>
+      <td><a href="https://github.com/BayesOptApp/MECHBench">https://github.com/BayesOptApp/MECHBench</a></td>
+      <td>Structural mechanics design optimization benchmark</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_mf2">
+      <td>suite_mf2</td>
+      <td>MF2</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>1</td>
+      <td>multi-fidelity</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>[1, 2]</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>mf2: a collection of multi-fidelity benchmark functions in Python, <a href="https://doi.org/10.21105/joss.02049">https://doi.org/10.21105/joss.02049</a></td>
+      <td>impl_mf2</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>mf2</td>
+      <td>Python</td>
+      <td></td>
+      <td><a href="https://github.com/sjvrijn/mf2">https://github.com/sjvrijn/mf2</a></td>
+      <td>Multi-fidelity test function collection</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_minus_dtlz">
+      <td>suite_minus_dtlz</td>
+      <td>Minus DTLZ</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>[10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Variant of DTLZ that minimises the inverse of the base DTLZ functions</td>
+      <td></td>
+      <td>Minus DTLZ / Minus WFG, <a href="https://doi.org/10.1109/TEVC.2016.2587749">https://doi.org/10.1109/TEVC.2016.2587749</a></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_minus_wfg">
+      <td>suite_minus_wfg</td>
+      <td>Minus WFG</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>[10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Variant of WFG that minimises the inverse of the base WFG functions</td>
+      <td></td>
+      <td>Minus DTLZ / Minus WFG, <a href="https://doi.org/10.1109/TEVC.2016.2587749">https://doi.org/10.1109/TEVC.2016.2587749</a></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_mmopp">
+      <td>suite_mmopp</td>
+      <td>MMOPP</td>
+      <td>Suite</td>
+      <td>unknown</td>
+      <td>0</td>
+      <td>[2, 3, 4, 5, 6, 7]</td>
+      <td></td>
+      <td>unknown</td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>MMOPP technical report, <a href="http://www5.zzu.edu.cn/system/_content/download.jsp?urltype=news.DownloadAttachUrl&owner=1327567121&wbfileid=4764412">http://www5.zzu.edu.cn/system/_content/download.jsp?urltype=news.DownloadAttachUrl&owner=1327567121&wbfileid=4764412</a></td>
+      <td>impl_mmopp</td>
+      <td>multimodal</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>MMOPP</td>
+      <td></td>
+      <td></td>
+      <td><a href="http://www5.zzu.edu.cn/ecilab/info/1036/1251.htm">http://www5.zzu.edu.cn/ecilab/info/1036/1251.htm</a></td>
+      <td>ECI lab distribution page for MMOPP</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_modact">
+      <td>suite_modact</td>
+      <td>MODAct</td>
+      <td>Suite</td>
+      <td>continuous | integer</td>
+      <td>40</td>
+      <td>[2, 3, 4, 5]</td>
+      <td></td>
+      <td>unknown</td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>multiobjective design of actuators</td>
+      <td>Realistic Constrained Multi-Objective Optimization Benchmark Problems from Design.</td>
+      <td></td>
+      <td>MODAct, <a href="https://doi.org/10.1109/TEVC.2020.3020046">https://doi.org/10.1109/TEVC.2020.3020046</a></td>
+      <td>["impl_modact", "impl_pymoo"]</td>
+      <td></td>
+      <td></td>
+      <td>real-world</td>
+      <td></td>
+      <td></td>
+      <td>20</td>
+      <td>20</td>
+      <td>modact | pymoo</td>
+      <td>Python</td>
+      <td>20ms</td>
+      <td><a href="https://github.com/epfl-lamd/modact">https://github.com/epfl-lamd/modact</a> | <a href="https://github.com/anyoptimization/pymoo">https://github.com/anyoptimization/pymoo</a></td>
+      <td>EPFL-LAMD modact package | Multi-objective optimization in Python</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_morepo">
+      <td>suite_morepo</td>
+      <td>MOrepo</td>
+      <td>Suite</td>
+      <td>unknown</td>
+      <td>0</td>
+      <td>2</td>
+      <td>dynamic | noisy</td>
+      <td>unknown</td>
+      <td>>=1</td>
+      <td>unknown</td>
+      <td>unknown</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>impl_morepo</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>MOrepo</td>
+      <td></td>
+      <td></td>
+      <td><a href="https://github.com/MCDMSociety/MOrepo">https://github.com/MCDMSociety/MOrepo</a></td>
+      <td>Multi-objective optimisation problem repository</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_pbo">
+      <td>suite_pbo</td>
+      <td>PBO</td>
+      <td>Suite</td>
+      <td>binary</td>
+      <td>>=1</td>
+      <td>1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Suite of 25 binary optimization problems</td>
+      <td></td>
+      <td>PBO benchmarks, <a href="https://dl.acm.org/doi/pdf/10.1145/3319619.3326810">https://dl.acm.org/doi/pdf/10.1145/3319619.3326810</a></td>
+      <td>impl_iohexperimenter</td>
+      <td></td>
+      <td></td>
+      <td>artificial</td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>IOHexperimenter</td>
+      <td>C++/Python</td>
+      <td></td>
+      <td><a href="https://github.com/IOHprofiler/IOHexperimenter">https://github.com/IOHprofiler/IOHexperimenter</a></td>
+      <td>IOHprofiler experimenter framework</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_porkchop">
+      <td>suite_porkchop</td>
+      <td>PorkchopPlotInterplanetaryTrajectory</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>2</td>
+      <td>1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Porkchop plot interplanetary trajectory benchmark, <a href="https://doi.org/10.1109/CEC65147.2025.11042973">https://doi.org/10.1109/CEC65147.2025.11042973</a></td>
+      <td>impl_transfer_rf_bbob_rw</td>
+      <td>multimodal</td>
+      <td></td>
+      <td>real-world</td>
+      <td></td>
+      <td></td>
+      <td>2</td>
+      <td></td>
+      <td>Transfer Random Forests BBOB Real-world</td>
+      <td></td>
+      <td></td>
+      <td><a href="https://github.com/ShuaiqunPan/Transfer_Random_forests_BBOB_Real_world">https://github.com/ShuaiqunPan/Transfer_Random_forests_BBOB_Real_world</a></td>
+      <td>Real-world BBOB-like problem implementations (Porkchop, KinematicsRobotArm)</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_re">
+      <td>suite_re</td>
+      <td>RE</td>
+      <td>Suite</td>
+      <td>continuous | integer</td>
+      <td>4-14</td>
+      <td>[2, 3, 4, 5, 6, 7, 8, 9]</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Easy-to-evaluate real-world multi-objective optimization problems, Ryoji Tanabe; Hisao Ishibuchi, <a href="https://doi.org/10.1016/j.asoc.2020.106078">https://doi.org/10.1016/j.asoc.2020.106078</a></td>
+      <td>impl_reproblems</td>
+      <td></td>
+      <td></td>
+      <td>real-world-like</td>
+      <td></td>
+      <td></td>
+      <td>2-7</td>
+      <td>2-7</td>
+      <td>reproblems</td>
+      <td>Python</td>
+      <td></td>
+      <td><a href="https://github.com/ryojitanabe/reproblems">https://github.com/ryojitanabe/reproblems</a></td>
+      <td>Real-world inspired multi-objective optimization problem suite</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_rwmvop">
+      <td>suite_rwmvop</td>
+      <td>RWMVOP</td>
+      <td>Suite</td>
+      <td>integer | continuous | categorical</td>
+      <td>>=3</td>
+      <td>1</td>
+      <td></td>
+      <td>unknown</td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>RWMVOP, <a href="https://doi.org/10.1109/TEVC.2013.2281531">https://doi.org/10.1109/TEVC.2013.2281531</a></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>real-world</td>
+      <td></td>
+      <td>>=1</td>
+      <td>>=1</td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_sbox_cost">
+      <td>suite_sbox_cost</td>
+      <td>SBOX-COST</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>problems from BBOB but allows instances with the optimum close to the boundary</td>
+      <td></td>
+      <td>SBOX-COST, <a href="https://doi.org/10.48550/arXiv.2305.12221">https://doi.org/10.48550/arXiv.2305.12221</a></td>
+      <td>impl_iohexperimenter</td>
+      <td>multimodal</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>IOHexperimenter</td>
+      <td>C++/Python</td>
+      <td></td>
+      <td><a href="https://github.com/IOHprofiler/IOHexperimenter">https://github.com/IOHprofiler/IOHexperimenter</a></td>
+      <td>IOHprofiler experimenter framework</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_sdp">
+      <td>suite_sdp</td>
+      <td>SDP</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>[10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
+      <td>dynamic | noisy</td>
+      <td></td>
+      <td></td>
+      <td>dynamic</td>
+      <td>unknown</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>SDP dynamic multi-objective benchmark, <a href="https://doi.org/10.1109/TCYB.2019.2896021">https://doi.org/10.1109/TCYB.2019.2896021</a></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_submodular">
+      <td>suite_submodular</td>
+      <td>Submodular Optimization</td>
+      <td>Suite</td>
+      <td>binary</td>
+      <td>>=1</td>
+      <td>1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>set of graph-based submodular optimization problems from 4 problem types</td>
+      <td></td>
+      <td>Submodular optimization benchmark, <a href="https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10254181">https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10254181</a></td>
+      <td>impl_iohexperimenter</td>
+      <td></td>
+      <td></td>
+      <td>artificial</td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>IOHexperimenter</td>
+      <td>C++/Python</td>
+      <td></td>
+      <td><a href="https://github.com/IOHprofiler/IOHexperimenter">https://github.com/IOHprofiler/IOHexperimenter</a></td>
+      <td>IOHprofiler experimenter framework</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_tulipa_energy">
+      <td>suite_tulipa_energy</td>
+      <td>TulipaEnergy</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>1</td>
+      <td>noisy | multi-fidelity</td>
+      <td>unknown</td>
+      <td>>=2</td>
+      <td></td>
+      <td>parameter</td>
+      <td></td>
+      <td></td>
+      <td>[1, 2]</td>
+      <td>TulipaEnergyModel.jl</td>
+      <td>Determine the optimal investment and operation decisions for different assets in the energy system (production, consumption, conversion, storage, transport) while minimizing loss of load. Modelled as a potentially very large linear program with multiple fidelity levels.</td>
+      <td></td>
+      <td>TulipaEnergyModel.jl scientific references, <a href="https://tulipaenergy.github.io/TulipaEnergyModel.jl/stable/40-scientific-foundation/45-scientific-references">https://tulipaenergy.github.io/TulipaEnergyModel.jl/stable/40-scientific-foundation/45-scientific-references</a></td>
+      <td>impl_tulipa</td>
+      <td>unimodal</td>
+      <td></td>
+      <td>real-world</td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>TulipaEnergyModel.jl</td>
+      <td>Julia / JuMP</td>
+      <td>minutes to hours</td>
+      <td><a href="https://tulipaenergy.github.io/TulipaEnergyModel.jl/stable/">https://tulipaenergy.github.io/TulipaEnergyModel.jl/stable/</a> | <a href="https://github.com/TulipaEnergy/Tulipa-OBZ-CaseStudy">https://github.com/TulipaEnergy/Tulipa-OBZ-CaseStudy</a></td>
+      <td>Large linear program for optimal investment and operation of energy systems</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_vehicle_dynamics">
+      <td>suite_vehicle_dynamics</td>
+      <td>VehicleDynamics</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>2</td>
+      <td>1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>VehicleDynamics benchmark, <a href="https://www.scitepress.org/Papers/2023/121580/121580.pdf">https://www.scitepress.org/Papers/2023/121580/121580.pdf</a></td>
+      <td>impl_vehicle_dynamics</td>
+      <td>multimodal</td>
+      <td></td>
+      <td>real-world</td>
+      <td></td>
+      <td></td>
+      <td>2</td>
+      <td></td>
+      <td>VehicleDynamics (Zenodo)</td>
+      <td></td>
+      <td></td>
+      <td><a href="https://zenodo.org/records/8307853">https://zenodo.org/records/8307853</a></td>
+      <td>Zenodo archive for the vehicle dynamics benchmark</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_wfg">
+      <td>suite_wfg</td>
+      <td>WFG</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>[10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>A review of multiobjective test problems and a scalable test problem toolkit, Simon Huband; Philip Hingston; Luigi Barone; Lyndon While, <a href="https://doi.org/10.1109/TEVC.2005.861417">https://doi.org/10.1109/TEVC.2005.861417</a></td>
+      <td>impl_pymoo</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>pymoo</td>
+      <td>Python</td>
+      <td></td>
+      <td><a href="https://github.com/anyoptimization/pymoo">https://github.com/anyoptimization/pymoo</a></td>
+      <td>Multi-objective optimization in Python</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_zdt">
+      <td>suite_zdt</td>
+      <td>ZDT</td>
+      <td>Suite</td>
+      <td>binary | continuous</td>
+      <td>>=2</td>
+      <td>2</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Comparison of multiobjective evolutionary algorithms: empirical results, Eckart Zitzler; Kalyanmoy Deb; Lothar Thiele, <a href="https://doi.org/10.1162/106365600568202">https://doi.org/10.1162/106365600568202</a></td>
+      <td>impl_pymoo</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>pymoo</td>
+      <td>Python</td>
+      <td></td>
+      <td><a href="https://github.com/anyoptimization/pymoo">https://github.com/anyoptimization/pymoo</a></td>
+      <td>Multi-objective optimization in Python</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+  </tbody>
+<tfoot><tr><th>ID</th> <th>Name</th> <th>Type</th> <th>Variable Types</th> <th>Total Variables</th> <th>Objectives</th> <th>Properties</th> <th>Constraint Types</th> <th>Total Constraints</th> <th>Dynamics</th> <th>Noise</th> <th>Partial Evaluations</th> <th>Independent Objectives</th> <th>Fidelity Levels</th> <th>Full Name</th> <th>Description</th> <th>Tags</th> <th>References</th> <th>Implementations</th> <th>Modality</th> <th>Examples</th> <th>Source</th> <th>Binary Vars</th> <th>Categorical Vars</th> <th>Continuous Vars</th> <th>Integer Vars</th> <th>Implementation Names</th> <th>Implementation Languages</th> <th>Implementation Evaluation Times</th> <th>Implementation Links</th> <th>Implementation Descriptions</th> <th>Implementation Requirements</th> <th>Hard Box Constraints</th> <th>Soft Box Constraints</th> <th>Hard Linear Constraints</th> <th>Soft Linear Constraints</th> <th>Hard Function Constraints</th> <th>Soft Function Constraints</th></tr></tfoot></table>
+    </div>
+</section>
+
+<section class="details-shell" id="problem-details" aria-live="polite">
+    <h3 class="details-title">Problem details</h3>
+    <p class="details-hint" id="problem-details-hint">Click a table row to inspect full details.</p>
+    <dl class="details-grid" id="problem-details-content"></dl>
+    <div class="snippet-shell" id="problem-snippet-shell">
+        <h4 class="snippet-title">README.md</h4>
+        <p class="snippet-hint" id="problem-readme-hint">README content for this snippet folder will appear here.</p>
+        <div class="snippet-readme markdown-body" id="problem-readme-content"></div>
+        <h4 class="snippet-title">Snippet: <code id="problem-snippet-path">call_{problem_id}.py</code></h4>
+        <p class="snippet-meta"><a id="problem-snippet-folder-link" href="#" target="_blank" rel="noopener noreferrer">Open snippet folder</a></p>
+        <p class="snippet-hint" id="problem-snippet-hint">Select a problem to check for an available code snippet.</p>
+        <pre class="snippet-code"><code id="problem-snippet-content"></code></pre>
+    </div>
+</section>
 
 
 <script>
-const SNIPPET_BASE_URL = 'https://raw.githubusercontent.com/OpenOptimizationOrg/OPL/code_snippets/code_snippets';
-const SNIPPET_TREE_BASE_URL = 'https://github.com/OpenOptimizationOrg/OPL/tree/code_snippets/code_snippets';
+const SNIPPET_BASE_URL = 'https://raw.githubusercontent.com/OpenOptimizationOrg/OPL/main/code_snippets';
+const SNIPPET_TREE_BASE_URL = 'https://github.com/OpenOptimizationOrg/OPL/tree/main/code_snippets';
 const snippetCache = new Map();
 let markdownParserPromise = null;
+const rangeFilters = new Map();
+const RANGE_FILTER_COLUMN_NAMES = new Set(['total variables', 'total constraints', 'objectives']);
 
 const table = new DataTable('#problems', {
     layout: {
@@ -1272,6 +3371,23 @@ <h4 class="snippet-title">Snippet: <code id="problem-snippet-path">call_{problem
                 column.footer().replaceChildren(input);
 
                 input.addEventListener('keyup', () => {
+                    const titleLower = title.trim().toLowerCase();
+                    if (RANGE_FILTER_COLUMN_NAMES.has(titleLower)) {
+                        const value = input.value.trim();
+                        if (value) {
+                            rangeFilters.set(column.index(), value);
+                        } else {
+                            rangeFilters.delete(column.index());
+                        }
+
+                        if (column.search() !== '') {
+                            column.search('');
+                        }
+
+                        table.draw();
+                        return;
+                    }
+
                     if (column.search() !== input.value) {
                         column.search(input.value).draw();
                     }
@@ -1300,6 +3416,14 @@ <h4 class="snippet-title">Snippet: <code id="problem-snippet-path">call_{problem
 const readmeHint = document.getElementById('problem-readme-hint');
 const readmeContent = document.getElementById('problem-readme-content');
 
+const getHeaderLabels = () => table
+    .columns()
+    .header()
+    .toArray()
+    .map((header) => header.textContent.trim());
+
+const getColumnIndex = (columnName) => getHeaderLabels().findIndex((label) => label === columnName);
+
 const stripHtml = (value) => {
     const wrapper = document.createElement('div');
     wrapper.innerHTML = value || '';
@@ -1312,6 +3436,128 @@ <h4 class="snippet-title">Snippet: <code id="problem-snippet-path">call_{problem
     return wrapper.innerHTML;
 };
 
+const parseNumber = (value) => {
+    const parsed = Number(value);
+    return Number.isFinite(parsed) ? parsed : null;
+};
+
+const extractNumericToken = (value) => {
+    const text = (value || '').trim();
+    if (!text) {
+        return null;
+    }
+
+    const exact = parseNumber(text);
+    if (exact !== null) {
+        return exact;
+    }
+
+    const match = text.match(/[-+]?\d+(?:\.\d+)?/);
+    if (!match) {
+        return null;
+    }
+
+    return parseNumber(match[0]);
+};
+
+const rangeContainsValue = (cellText, target) => {
+    const text = (cellText || '').trim();
+    if (!text) {
+        return false;
+    }
+
+    const compact = text.replace(/\s+/g, '');
+
+    const single = compact.match(/^[-+]?\d+(?:\.\d+)?$/);
+    if (single) {
+        return parseNumber(single[0]) === target;
+    }
+
+    const ge = compact.match(/^>=([-+]?\d+(?:\.\d+)?)$/);
+    if (ge) {
+        const bound = parseNumber(ge[1]);
+        return bound !== null && target >= bound;
+    }
+
+    const le = compact.match(/^<=([-+]?\d+(?:\.\d+)?)$/);
+    if (le) {
+        const bound = parseNumber(le[1]);
+        return bound !== null && target <= bound;
+    }
+
+    const gt = compact.match(/^>([-+]?\d+(?:\.\d+)?)$/);
+    if (gt) {
+        const bound = parseNumber(gt[1]);
+        return bound !== null && target > bound;
+    }
+
+    const lt = compact.match(/^<([-+]?\d+(?:\.\d+)?)$/);
+    if (lt) {
+        const bound = parseNumber(lt[1]);
+        return bound !== null && target < bound;
+    }
+
+    const interval = compact.match(/^([-+]?\d+(?:\.\d+)?)-([-+]?\d+(?:\.\d+)?)$/);
+    if (interval) {
+        const lower = parseNumber(interval[1]);
+        const upper = parseNumber(interval[2]);
+        return lower !== null && upper !== null && target >= lower && target <= upper;
+    }
+
+    if (compact.startsWith('{') && compact.endsWith('}')) {
+        const values = compact.slice(1, -1).split(',').map(parseNumber).filter((value) => value !== null);
+        return values.includes(target);
+    }
+
+    if (compact.startsWith('[') && compact.endsWith(']')) {
+        const values = compact.slice(1, -1).split(',').map(parseNumber).filter((value) => value !== null);
+        return values.includes(target);
+    }
+
+    if (compact.includes('|') || compact.includes(';')) {
+        const parts = compact.split(/\||;/).map((part) => part.trim()).filter(Boolean);
+        return parts.some((part) => rangeContainsValue(part, target));
+    }
+
+    return false;
+};
+
+const matchesRangeAwareFilter = (cellText, rawFilter) => {
+    const filter = (rawFilter || '').trim();
+    if (!filter) {
+        return true;
+    }
+
+    const numericFilter = extractNumericToken(filter);
+    if (numericFilter !== null) {
+        return rangeContainsValue(cellText, numericFilter);
+    }
+
+    return (cellText || '').toLowerCase().includes(filter.toLowerCase());
+};
+
+if (!window.__oplRangeFilterRegistered) {
+    window.__oplRangeFilterRegistered = true;
+    DataTable.ext.search.push((settings, data) => {
+        if (settings.nTable?.id !== 'problems') {
+            return true;
+        }
+
+        for (const [columnIndex, rawFilter] of rangeFilters.entries()) {
+            if (!rawFilter) {
+                continue;
+            }
+
+            const cellText = stripHtml(data[columnIndex] || '');
+            if (!matchesRangeAwareFilter(cellText, rawFilter)) {
+                return false;
+            }
+        }
+
+        return true;
+    });
+}
+
 const ensureMarkdownParser = () => {
     if (window.marked?.parse) {
         return Promise.resolve(window.marked.parse);
@@ -1366,6 +3612,11 @@ <h4 class="snippet-title">Snippet: <code id="problem-snippet-path">call_{problem
         .replace(/^_+|_+$/g, '');
 };
 
+const toSnippetProblemId = (value) => {
+    const normalized = toProblemId(value);
+    return normalized.replace(/^(fn|gen|suite)_/, '');
+};
+
 const setSnippetState = ({
     path = '',
     hint = '',
@@ -1408,7 +3659,8 @@ <h4 class="snippet-title">Snippet: <code id="problem-snippet-path">call_{problem
 };
 
 const loadSnippet = async (problemId) => {
-    if (!problemId) {
+    const snippetProblemId = toSnippetProblemId(problemId);
+    if (!snippetProblemId) {
         setSnippetState({
             hint: 'No problem id available for this row.',
             code: '',
@@ -1418,13 +3670,13 @@ <h4 class="snippet-title">Snippet: <code id="problem-snippet-path">call_{problem
         return;
     }
 
-    const fileName = `call_${problemId}.py`;
-    const rawUrl = `${SNIPPET_BASE_URL}/${encodeURIComponent(problemId)}/${encodeURIComponent(fileName)}`;
-    const readmeRawUrl = `${SNIPPET_BASE_URL}/${encodeURIComponent(problemId)}/README.md`;
-    const folderUrl = `${SNIPPET_TREE_BASE_URL}/${encodeURIComponent(problemId)}`;
+    const fileName = `call_${snippetProblemId}.py`;
+    const rawUrl = `${SNIPPET_BASE_URL}/${encodeURIComponent(snippetProblemId)}/${encodeURIComponent(fileName)}`;
+    const readmeRawUrl = `${SNIPPET_BASE_URL}/${encodeURIComponent(snippetProblemId)}/README.md`;
+    const folderUrl = `${SNIPPET_TREE_BASE_URL}/${encodeURIComponent(snippetProblemId)}`;
 
     setSnippetState({
-        path: `${problemId}/${fileName}`,
+        path: `${snippetProblemId}/${fileName}`,
         hint: 'Loading snippet from repository...',
         code: '',
         folderHref: folderUrl,
@@ -1432,10 +3684,10 @@ <h4 class="snippet-title">Snippet: <code id="problem-snippet-path">call_{problem
         readmeHtml: ''
     });
 
-    if (snippetCache.has(problemId)) {
-        const cached = snippetCache.get(problemId);
+    if (snippetCache.has(snippetProblemId)) {
+        const cached = snippetCache.get(snippetProblemId);
         setSnippetState({
-            path: `${problemId}/${fileName}`,
+            path: `${snippetProblemId}/${fileName}`,
             hint: cached.codeHint,
             code: cached.code,
             folderHref: folderUrl,
@@ -1475,7 +3727,7 @@ <h4 class="snippet-title">Snippet: <code id="problem-snippet-path">call_{problem
             readmeHintText = 'README.md loaded from snippet folder.';
         }
 
-        snippetCache.set(problemId, {
+        snippetCache.set(snippetProblemId, {
             code,
             codeHint,
             readmeHint: readmeHintText,
@@ -1483,7 +3735,7 @@ <h4 class="snippet-title">Snippet: <code id="problem-snippet-path">call_{problem
         });
 
         setSnippetState({
-            path: `${problemId}/${fileName}`,
+            path: `${snippetProblemId}/${fileName}`,
             hint: codeHint,
             code,
             folderHref: folderUrl,
@@ -1492,7 +3744,7 @@ <h4 class="snippet-title">Snippet: <code id="problem-snippet-path">call_{problem
         });
     } catch (error) {
         setSnippetState({
-            path: `${problemId}/${fileName}`,
+            path: `${snippetProblemId}/${fileName}`,
             hint: 'Network error while fetching snippet.',
             code: '',
             folderHref: folderUrl,
@@ -1508,11 +3760,7 @@ <h4 class="snippet-title">Snippet: <code id="problem-snippet-path">call_{problem
         return;
     }
 
-    const headers = table
-        .columns()
-        .header()
-        .toArray()
-        .map((header) => header.textContent.trim());
+    const headers = getHeaderLabels();
 
     const detailsHtml = headers
         .map((label, index) => {
@@ -1521,7 +3769,9 @@ <h4 class="snippet-title">Snippet: <code id="problem-snippet-path">call_{problem
         })
         .join('');
 
-    const name = stripHtml(rowData[0]);
+    const nameIndex = getColumnIndex('name');
+    const fallbackIndex = getColumnIndex('id');
+    const name = stripHtml(rowData[nameIndex >= 0 ? nameIndex : fallbackIndex >= 0 ? fallbackIndex : 0]);
     if (detailsHint) {
         detailsHint.textContent = name ? `Selected: ${name}` : 'Selected row';
     }
@@ -1588,8 +3838,11 @@ <h4 class="snippet-title">Snippet: <code id="problem-snippet-path">call_{problem
         targetRow.classList.add('is-active');
         renderRowDetails(rowApi);
 
-        const name = stripHtml(rowApi.data()[0]);
-        const problemId = targetRow.getAttribute('data-problem-id') || toProblemId(name);
+        const headers = getHeaderLabels();
+        const nameIndex = headers.findIndex((label) => label === 'name');
+        const problemIdIndex = headers.findIndex((label) => label === 'id');
+        const name = stripHtml(rowApi.data()[nameIndex >= 0 ? nameIndex : 0]);
+        const problemId = targetRow.getAttribute('data-problem-id') || stripHtml(rowApi.data()[problemIdIndex >= 0 ? problemIdIndex : 0]) || toProblemId(name);
         loadSnippet(problemId);
     });
 }
diff --git a/docs/javascript.html b/docs/javascript.html
index d5f41ef..e3a771e 100644
--- a/docs/javascript.html
+++ b/docs/javascript.html
@@ -5,6 +5,8 @@
 const SNIPPET_TREE_BASE_URL = 'https://github.com/OpenOptimizationOrg/OPL/tree/main/code_snippets';
 const snippetCache = new Map();
 let markdownParserPromise = null;
+const rangeFilters = new Map();
+const RANGE_FILTER_COLUMN_NAMES = new Set(['total variables', 'total constraints', 'objectives']);
 
 const table = new DataTable('#problems', {
     layout: {
@@ -26,6 +28,23 @@
                 column.footer().replaceChildren(input);
 
                 input.addEventListener('keyup', () => {
+                    const titleLower = title.trim().toLowerCase();
+                    if (RANGE_FILTER_COLUMN_NAMES.has(titleLower)) {
+                        const value = input.value.trim();
+                        if (value) {
+                            rangeFilters.set(column.index(), value);
+                        } else {
+                            rangeFilters.delete(column.index());
+                        }
+
+                        if (column.search() !== '') {
+                            column.search('');
+                        }
+
+                        table.draw();
+                        return;
+                    }
+
                     if (column.search() !== input.value) {
                         column.search(input.value).draw();
                     }
@@ -54,6 +73,14 @@
 const readmeHint = document.getElementById('problem-readme-hint');
 const readmeContent = document.getElementById('problem-readme-content');
 
+const getHeaderLabels = () => table
+    .columns()
+    .header()
+    .toArray()
+    .map((header) => header.textContent.trim());
+
+const getColumnIndex = (columnName) => getHeaderLabels().findIndex((label) => label === columnName);
+
 const stripHtml = (value) => {
     const wrapper = document.createElement('div');
     wrapper.innerHTML = value || '';
@@ -66,6 +93,128 @@
     return wrapper.innerHTML;
 };
 
+const parseNumber = (value) => {
+    const parsed = Number(value);
+    return Number.isFinite(parsed) ? parsed : null;
+};
+
+const extractNumericToken = (value) => {
+    const text = (value || '').trim();
+    if (!text) {
+        return null;
+    }
+
+    const exact = parseNumber(text);
+    if (exact !== null) {
+        return exact;
+    }
+
+    const match = text.match(/[-+]?\d+(?:\.\d+)?/);
+    if (!match) {
+        return null;
+    }
+
+    return parseNumber(match[0]);
+};
+
+const rangeContainsValue = (cellText, target) => {
+    const text = (cellText || '').trim();
+    if (!text) {
+        return false;
+    }
+
+    const compact = text.replace(/\s+/g, '');
+
+    const single = compact.match(/^[-+]?\d+(?:\.\d+)?$/);
+    if (single) {
+        return parseNumber(single[0]) === target;
+    }
+
+    const ge = compact.match(/^>=([-+]?\d+(?:\.\d+)?)$/);
+    if (ge) {
+        const bound = parseNumber(ge[1]);
+        return bound !== null && target >= bound;
+    }
+
+    const le = compact.match(/^<=([-+]?\d+(?:\.\d+)?)$/);
+    if (le) {
+        const bound = parseNumber(le[1]);
+        return bound !== null && target <= bound;
+    }
+
+    const gt = compact.match(/^>([-+]?\d+(?:\.\d+)?)$/);
+    if (gt) {
+        const bound = parseNumber(gt[1]);
+        return bound !== null && target > bound;
+    }
+
+    const lt = compact.match(/^<([-+]?\d+(?:\.\d+)?)$/);
+    if (lt) {
+        const bound = parseNumber(lt[1]);
+        return bound !== null && target < bound;
+    }
+
+    const interval = compact.match(/^([-+]?\d+(?:\.\d+)?)-([-+]?\d+(?:\.\d+)?)$/);
+    if (interval) {
+        const lower = parseNumber(interval[1]);
+        const upper = parseNumber(interval[2]);
+        return lower !== null && upper !== null && target >= lower && target <= upper;
+    }
+
+    if (compact.startsWith('{') && compact.endsWith('}')) {
+        const values = compact.slice(1, -1).split(',').map(parseNumber).filter((value) => value !== null);
+        return values.includes(target);
+    }
+
+    if (compact.startsWith('[') && compact.endsWith(']')) {
+        const values = compact.slice(1, -1).split(',').map(parseNumber).filter((value) => value !== null);
+        return values.includes(target);
+    }
+
+    if (compact.includes('|') || compact.includes(';')) {
+        const parts = compact.split(/\||;/).map((part) => part.trim()).filter(Boolean);
+        return parts.some((part) => rangeContainsValue(part, target));
+    }
+
+    return false;
+};
+
+const matchesRangeAwareFilter = (cellText, rawFilter) => {
+    const filter = (rawFilter || '').trim();
+    if (!filter) {
+        return true;
+    }
+
+    const numericFilter = extractNumericToken(filter);
+    if (numericFilter !== null) {
+        return rangeContainsValue(cellText, numericFilter);
+    }
+
+    return (cellText || '').toLowerCase().includes(filter.toLowerCase());
+};
+
+if (!window.__oplRangeFilterRegistered) {
+    window.__oplRangeFilterRegistered = true;
+    DataTable.ext.search.push((settings, data) => {
+        if (settings.nTable?.id !== 'problems') {
+            return true;
+        }
+
+        for (const [columnIndex, rawFilter] of rangeFilters.entries()) {
+            if (!rawFilter) {
+                continue;
+            }
+
+            const cellText = stripHtml(data[columnIndex] || '');
+            if (!matchesRangeAwareFilter(cellText, rawFilter)) {
+                return false;
+            }
+        }
+
+        return true;
+    });
+}
+
 const ensureMarkdownParser = () => {
     if (window.marked?.parse) {
         return Promise.resolve(window.marked.parse);
@@ -120,6 +269,11 @@
         .replace(/^_+|_+$/g, '');
 };
 
+const toSnippetProblemId = (value) => {
+    const normalized = toProblemId(value);
+    return normalized.replace(/^(fn|gen|suite)_/, '');
+};
+
 const setSnippetState = ({
     path = '',
     hint = '',
@@ -162,7 +316,8 @@
 };
 
 const loadSnippet = async (problemId) => {
-    if (!problemId) {
+    const snippetProblemId = toSnippetProblemId(problemId);
+    if (!snippetProblemId) {
         setSnippetState({
             hint: 'No problem id available for this row.',
             code: '',
@@ -172,13 +327,13 @@
         return;
     }
 
-    const fileName = `call_${problemId}.py`;
-    const rawUrl = `${SNIPPET_BASE_URL}/${encodeURIComponent(problemId)}/${encodeURIComponent(fileName)}`;
-    const readmeRawUrl = `${SNIPPET_BASE_URL}/${encodeURIComponent(problemId)}/README.md`;
-    const folderUrl = `${SNIPPET_TREE_BASE_URL}/${encodeURIComponent(problemId)}`;
+    const fileName = `call_${snippetProblemId}.py`;
+    const rawUrl = `${SNIPPET_BASE_URL}/${encodeURIComponent(snippetProblemId)}/${encodeURIComponent(fileName)}`;
+    const readmeRawUrl = `${SNIPPET_BASE_URL}/${encodeURIComponent(snippetProblemId)}/README.md`;
+    const folderUrl = `${SNIPPET_TREE_BASE_URL}/${encodeURIComponent(snippetProblemId)}`;
 
     setSnippetState({
-        path: `${problemId}/${fileName}`,
+        path: `${snippetProblemId}/${fileName}`,
         hint: 'Loading snippet from repository...',
         code: '',
         folderHref: folderUrl,
@@ -186,10 +341,10 @@
         readmeHtml: ''
     });
 
-    if (snippetCache.has(problemId)) {
-        const cached = snippetCache.get(problemId);
+    if (snippetCache.has(snippetProblemId)) {
+        const cached = snippetCache.get(snippetProblemId);
         setSnippetState({
-            path: `${problemId}/${fileName}`,
+            path: `${snippetProblemId}/${fileName}`,
             hint: cached.codeHint,
             code: cached.code,
             folderHref: folderUrl,
@@ -229,7 +384,7 @@
             readmeHintText = 'README.md loaded from snippet folder.';
         }
 
-        snippetCache.set(problemId, {
+        snippetCache.set(snippetProblemId, {
             code,
             codeHint,
             readmeHint: readmeHintText,
@@ -237,7 +392,7 @@
         });
 
         setSnippetState({
-            path: `${problemId}/${fileName}`,
+            path: `${snippetProblemId}/${fileName}`,
             hint: codeHint,
             code,
             folderHref: folderUrl,
@@ -246,7 +401,7 @@
         });
     } catch (error) {
         setSnippetState({
-            path: `${problemId}/${fileName}`,
+            path: `${snippetProblemId}/${fileName}`,
             hint: 'Network error while fetching snippet.',
             code: '',
             folderHref: folderUrl,
@@ -262,11 +417,7 @@
         return;
     }
 
-    const headers = table
-        .columns()
-        .header()
-        .toArray()
-        .map((header) => header.textContent.trim());
+    const headers = getHeaderLabels();
 
     const detailsHtml = headers
         .map((label, index) => {
@@ -275,7 +426,9 @@
         })
         .join('');
 
-    const name = stripHtml(rowData[0]);
+    const nameIndex = getColumnIndex('name');
+    const fallbackIndex = getColumnIndex('id');
+    const name = stripHtml(rowData[nameIndex >= 0 ? nameIndex : fallbackIndex >= 0 ? fallbackIndex : 0]);
     if (detailsHint) {
         detailsHint.textContent = name ? `Selected: ${name}` : 'Selected row';
     }
@@ -342,8 +495,11 @@
         targetRow.classList.add('is-active');
         renderRowDetails(rowApi);
 
-        const name = stripHtml(rowApi.data()[0]);
-        const problemId = targetRow.getAttribute('data-problem-id') || toProblemId(name);
+        const headers = getHeaderLabels();
+        const nameIndex = headers.findIndex((label) => label === 'name');
+        const problemIdIndex = headers.findIndex((label) => label === 'id');
+        const name = stripHtml(rowApi.data()[nameIndex >= 0 ? nameIndex : 0]);
+        const problemId = targetRow.getAttribute('data-problem-id') || stripHtml(rowApi.data()[problemIdIndex >= 0 ? problemIdIndex : 0]) || toProblemId(name);
         loadSnippet(problemId);
     });
 }
diff --git a/docs/problems.html b/docs/problems.html
index 9869979..7f933ac 100644
--- a/docs/problems.html
+++ b/docs/problems.html
@@ -1,1220 +1,3317 @@
-<section class="table-shell">
-    <div class="table-toolbar">
-        <div class="toolbar-title">Visible columns</div>
-        <div class="toolbar-actions">
-            <button type="button" class="toolbar-btn" id="show-all-columns">Show all</button>
-            <button type="button" class="toolbar-btn" id="hide-all-columns">Hide all</button>
-        </div>
-        <div class="column-controls">
-            <label class="column-chip"><input class="col-toggle" type="checkbox" data-column="0" checked><span>name</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="1"><span>textual description</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="2" checked><span>suite/generator/single</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="3" checked><span>objectives</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="4" checked><span>dimensionality</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="5" checked><span>variable type</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="6" checked><span>constraints</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="7" checked><span>dynamic</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="8" checked><span>noise</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="9" checked><span>multi-fidelity</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="10" checked><span>source (real-world/artificial)</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="11"><span>reference</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="12"><span>implementation</span></label>
-        </div>
-    </div>
-    <div class="table-wrap">
-        <table class="dataframe display compact display styled-table" id="problems">
-  <thead>
-    <tr style="text-align: right;">
-      <th>name</th>
-      <th>textual description</th>
-      <th>suite/generator/single</th>
-      <th>objectives</th>
-      <th>dimensionality</th>
-      <th>variable type</th>
-      <th>constraints</th>
-      <th>dynamic</th>
-      <th>noise</th>
-      <th>multi-fidelity</th>
-      <th>source (real-world/artificial)</th>
-      <th>reference</th>
-      <th>implementation</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr data-problem-id="bbob">
-      <td>BBOB</td>
-      <td></td>
-      <td>suite</td>
-      <td>1</td>
-      <td>scalable</td>
-      <td>continuous</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td></td>
-      <td><a href="https://doi.org/10.1080/10556788.2020.1808977">https://doi.org/10.1080/10556788.2020.1808977</a></td>
-      <td><a href="https://github.com/numbbo/coco">https://github.com/numbbo/coco</a></td>
-    </tr>
-    <tr data-problem-id="bbob_biobj">
-      <td>BBOB-biobj</td>
-      <td></td>
-      <td>suite</td>
-      <td>2</td>
-      <td>2-40</td>
-      <td>continuous</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td></td>
-      <td><a href="https://doi.org/10.48550/arXiv.1604.00359">https://doi.org/10.48550/arXiv.1604.00359</a></td>
-      <td><a href="https://github.com/numbbo/coco">https://github.com/numbbo/coco</a></td>
-    </tr>
-    <tr data-problem-id="bbob_noisy">
-      <td>BBOB-noisy</td>
-      <td></td>
-      <td>suite</td>
-      <td>1</td>
-      <td>scalable</td>
-      <td>continuous</td>
-      <td>no</td>
-      <td>no</td>
-      <td>yes</td>
-      <td>no</td>
-      <td></td>
-      <td><a href="https://hal.inria.fr/inria-00369466">https://hal.inria.fr/inria-00369466</a></td>
-      <td><a href="https://web.archive.org/web/20210416065610/https://coco.gforge.inria.fr/doku.php?id=downloads">https://web.archive.org/web/20210416065610/https://coco.gforge.inria.fr/doku.php?id=downloads</a></td>
-    </tr>
-    <tr data-problem-id="bbob_largescale">
-      <td>BBOB-largescale</td>
-      <td></td>
-      <td>suite</td>
-      <td>1</td>
-      <td>20-640</td>
-      <td>continuous</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td></td>
-      <td><a href="https://doi.org/10.48550/arXiv.1903.06396">https://doi.org/10.48550/arXiv.1903.06396</a></td>
-      <td><a href="https://github.com/numbbo/coco">https://github.com/numbbo/coco</a></td>
-    </tr>
-    <tr data-problem-id="bbob_mixint">
-      <td>BBOB-mixint</td>
-      <td></td>
-      <td>suite</td>
-      <td>1</td>
-      <td>5-160</td>
-      <td>integer;continuous;mixed</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td></td>
-      <td><a href="https://doi.org/10.1145/3321707.3321868">https://doi.org/10.1145/3321707.3321868</a></td>
-      <td><a href="https://github.com/numbbo/coco">https://github.com/numbbo/coco</a></td>
-    </tr>
-    <tr data-problem-id="bbob_biobj_mixint">
-      <td>BBOB-biobj-mixint</td>
-      <td></td>
-      <td>suite</td>
-      <td>2</td>
-      <td>5-160</td>
-      <td>integer;continuous;mixed</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td></td>
-      <td><a href="https://doi.org/10.1145/3321707.3321868">https://doi.org/10.1145/3321707.3321868</a></td>
-      <td><a href="https://github.com/numbbo/coco">https://github.com/numbbo/coco</a></td>
-    </tr>
-    <tr data-problem-id="bbob_constrained">
-      <td>BBOB-constrained</td>
-      <td></td>
-      <td>suite</td>
-      <td>1</td>
-      <td>2-40</td>
-      <td>continuous</td>
-      <td>yes</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td></td>
-      <td><a href="http://numbbo.github.io/coco-doc/bbob-constrained/">http://numbbo.github.io/coco-doc/bbob-constrained/</a></td>
-      <td><a href="https://github.com/numbbo/coco">https://github.com/numbbo/coco</a></td>
-    </tr>
-    <tr data-problem-id="morepo">
-      <td>MOrepo</td>
-      <td></td>
-      <td>suite</td>
-      <td>2</td>
-      <td>?</td>
-      <td>combinatorial</td>
-      <td>?</td>
-      <td>?</td>
-      <td>?</td>
-      <td>no</td>
-      <td></td>
-      <td></td>
-      <td><a href="https://github.com/MCDMSociety/MOrepo">https://github.com/MCDMSociety/MOrepo</a></td>
-    </tr>
-    <tr data-problem-id="zdt">
-      <td>ZDT</td>
-      <td></td>
-      <td>suite</td>
-      <td>2</td>
-      <td>scalable</td>
-      <td>continuous;binary</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td></td>
-      <td><a href="https://doi.org/10.1162/106365600568202">https://doi.org/10.1162/106365600568202</a></td>
-      <td><a href="https://github.com/anyoptimization/pymoo">https://github.com/anyoptimization/pymoo</a></td>
-    </tr>
-    <tr data-problem-id="dtlz">
-      <td>DTLZ</td>
-      <td></td>
-      <td>suite</td>
-      <td>2+</td>
-      <td>scalable</td>
-      <td>continuous</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td></td>
-      <td><a href="https://doi.org/10.1109/CEC.2002.1007032">https://doi.org/10.1109/CEC.2002.1007032</a></td>
-      <td><a href="https://pymoo.org/problems/many/dtlz.html">https://pymoo.org/problems/many/dtlz.html</a></td>
-    </tr>
-    <tr data-problem-id="wfg">
-      <td>WFG</td>
-      <td></td>
-      <td>suite</td>
-      <td>2+</td>
-      <td>scalable</td>
-      <td>continuous</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td></td>
-      <td><a href="https://doi.org/10.1109/TEVC.2005.861417">https://doi.org/10.1109/TEVC.2005.861417</a></td>
-      <td><a href="https://pymoo.org/problems/many/wfg.html">https://pymoo.org/problems/many/wfg.html</a></td>
-    </tr>
-    <tr data-problem-id="cdmp">
-      <td>CDMP</td>
-      <td></td>
-      <td>suite</td>
-      <td>2+</td>
-      <td>scalable</td>
-      <td>continuous</td>
-      <td>yes</td>
-      <td>?</td>
-      <td>?</td>
-      <td>no</td>
-      <td></td>
-      <td><a href="https://doi.org/10.1145/3321707.3321878">https://doi.org/10.1145/3321707.3321878</a></td>
-      <td>?</td>
-    </tr>
-    <tr data-problem-id="sdp">
-      <td>SDP</td>
-      <td></td>
-      <td>suite</td>
-      <td>2+</td>
-      <td>scalable</td>
-      <td>continuous</td>
-      <td>no</td>
-      <td>yes</td>
-      <td>?</td>
-      <td>no</td>
-      <td></td>
-      <td><a href="https://doi.org/10.1109/TCYB.2019.2896021">https://doi.org/10.1109/TCYB.2019.2896021</a></td>
-      <td>?</td>
-    </tr>
-    <tr data-problem-id="maop">
-      <td>MaOP</td>
-      <td></td>
-      <td>suite</td>
-      <td>2+</td>
-      <td>scalable</td>
-      <td>continuous</td>
-      <td>no</td>
-      <td>no</td>
-      <td>?</td>
-      <td>no</td>
-      <td></td>
-      <td><a href="https://doi.org/10.1016/j.swevo.2019.02.003">https://doi.org/10.1016/j.swevo.2019.02.003</a></td>
-      <td>?</td>
-    </tr>
-    <tr data-problem-id="bp">
-      <td>BP</td>
-      <td></td>
-      <td>suite</td>
-      <td>2+</td>
-      <td>scalable</td>
-      <td>continuous</td>
-      <td>no</td>
-      <td>no</td>
-      <td>?</td>
-      <td>no</td>
-      <td></td>
-      <td><a href="https://doi.org/10.1109/CEC.2019.8790277">https://doi.org/10.1109/CEC.2019.8790277</a></td>
-      <td>?</td>
-    </tr>
-    <tr data-problem-id="gpd">
-      <td>GPD</td>
-      <td></td>
-      <td>generator</td>
-      <td>2+</td>
-      <td>scalable</td>
-      <td>continuous</td>
-      <td>optional</td>
-      <td>no</td>
-      <td>optional</td>
-      <td>no</td>
-      <td></td>
-      <td><a href="https://doi.org/10.1016/j.asoc.2020.106139">https://doi.org/10.1016/j.asoc.2020.106139</a></td>
-      <td>?</td>
-    </tr>
-    <tr data-problem-id="etmof">
-      <td>ETMOF</td>
-      <td></td>
-      <td>suite</td>
-      <td>2-50</td>
-      <td>25-10000</td>
-      <td>continuous</td>
-      <td>no</td>
-      <td>yes</td>
-      <td>no</td>
-      <td>no</td>
-      <td></td>
-      <td><a href="https://doi.org/10.48550/arXiv.2110.08033">https://doi.org/10.48550/arXiv.2110.08033</a></td>
-      <td><a href="https://github.com/songbai-liu/etmo">https://github.com/songbai-liu/etmo</a></td>
-    </tr>
-    <tr data-problem-id="mmopp">
-      <td>MMOPP</td>
-      <td></td>
-      <td>suite</td>
-      <td>2-7</td>
-      <td>?</td>
-      <td>?</td>
-      <td>yes</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td></td>
-      <td><a href="http://www5.zzu.edu.cn/system/_content/download.jsp?urltype=news.DownloadAttachUrl&owner=1327567121&wbfileid=4764412">http://www5.zzu.edu.cn/system/_content/download.jsp?urltype=news.DownloadAttachUrl&owner=1327567121&wbfileid=4764412</a></td>
-      <td><a href="http://www5.zzu.edu.cn/ecilab/info/1036/1251.htm">http://www5.zzu.edu.cn/ecilab/info/1036/1251.htm</a></td>
-    </tr>
-    <tr data-problem-id="cfd">
-      <td>CFD</td>
-      <td>expensive evaluations 30s-15m</td>
-      <td>suite</td>
-      <td>1-2</td>
-      <td>scalable</td>
-      <td>?</td>
-      <td>yes</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>real world</td>
-      <td><a href="https://doi.org/10.1007/978-3-319-99259-4_24">https://doi.org/10.1007/978-3-319-99259-4_24</a></td>
-      <td><a href="https://bitbucket.org/arahat/cfd-test-problem-suite">https://bitbucket.org/arahat/cfd-test-problem-suite</a></td>
-    </tr>
-    <tr data-problem-id="gbea">
-      <td>GBEA</td>
-      <td>expensive evaluations 5s-35s, RW-GAN-Mario and TopTrumps are part of GBEA</td>
-      <td>suite</td>
-      <td>1-2</td>
-      <td>scalable</td>
-      <td>continuous</td>
-      <td>no</td>
-      <td>no</td>
-      <td>yes</td>
-      <td>no</td>
-      <td>real world</td>
-      <td><a href="https://doi.org/10.1145/3321707.3321805">https://doi.org/10.1145/3321707.3321805</a></td>
-      <td><a href="https://github.com/ttusar/coco-gbea">https://github.com/ttusar/coco-gbea</a></td>
-    </tr>
-    <tr data-problem-id="car_structure">
-      <td>Car structure</td>
-      <td>54 constraints</td>
-      <td>suite</td>
-      <td>2</td>
-      <td>144-222</td>
-      <td>discrete</td>
-      <td>yes</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>real world</td>
-      <td><a href="https://doi.org/10.1145/3205651.3205702">https://doi.org/10.1145/3205651.3205702</a></td>
-      <td><a href="http://ladse.eng.isas.jaxa.jp/benchmark/">http://ladse.eng.isas.jaxa.jp/benchmark/</a></td>
-    </tr>
-    <tr data-problem-id="emo2017">
-      <td>EMO2017</td>
-      <td></td>
-      <td>suite</td>
-      <td>2</td>
-      <td>4-24</td>
-      <td>continuous</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>real world</td>
-      <td><a href="https://www.ini.rub.de/PEOPLE/glasmtbl/projects/bbcomp/">https://www.ini.rub.de/PEOPLE/glasmtbl/projects/bbcomp/</a></td>
-      <td><a href="https://www.ini.rub.de/PEOPLE/glasmtbl/projects/bbcomp/downloads/realworld-problems-bbcomp-EMO-2017.zip">https://www.ini.rub.de/PEOPLE/glasmtbl/projects/bbcomp/downloads/realworld-problems-bbcomp-EMO-2017.zip</a></td>
-    </tr>
-    <tr data-problem-id="jsec2019">
-      <td>JSEC2019</td>
-      <td>expensive evaluations 3s; 22 constraints</td>
-      <td>single</td>
-      <td>1-5</td>
-      <td>32</td>
-      <td>continuous</td>
-      <td>yes</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>real world</td>
-      <td><a href="http://www.jpnsec.org/files/competition2019/EC-Symposium-2019-Competition-English.html">http://www.jpnsec.org/files/competition2019/EC-Symposium-2019-Competition-English.html</a></td>
-      <td><a href="http://www.jpnsec.org/files/competition2019/EC-Symposium-2019-Competition-English.html">http://www.jpnsec.org/files/competition2019/EC-Symposium-2019-Competition-English.html</a></td>
-    </tr>
-    <tr data-problem-id="re">
-      <td>RE</td>
-      <td></td>
-      <td>suite</td>
-      <td>2-9</td>
-      <td>2-7</td>
-      <td>continuous;integer;mixed</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>real world like</td>
-      <td><a href="https://doi.org/10.1016/j.asoc.2020.106078">https://doi.org/10.1016/j.asoc.2020.106078</a></td>
-      <td><a href="https://github.com/ryojitanabe/reproblems">https://github.com/ryojitanabe/reproblems</a></td>
-    </tr>
-    <tr data-problem-id="cre">
-      <td>CRE</td>
-      <td></td>
-      <td>suite</td>
-      <td>2-5</td>
-      <td>3-7</td>
-      <td>continuous;integer;mixed</td>
-      <td>yes</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>real world like</td>
-      <td><a href="https://doi.org/10.1016/j.asoc.2020.106078">https://doi.org/10.1016/j.asoc.2020.106078</a></td>
-      <td><a href="https://github.com/ryojitanabe/reproblems">https://github.com/ryojitanabe/reproblems</a></td>
-    </tr>
-    <tr data-problem-id="radar_waveform">
-      <td>Radar waveform</td>
-      <td></td>
-      <td>single</td>
-      <td>9</td>
-      <td>4-12</td>
-      <td>integer</td>
-      <td>yes</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>real world</td>
-      <td><a href="https://doi.org/10.1007/978-3-540-70928-2_53">https://doi.org/10.1007/978-3-540-70928-2_53</a></td>
-      <td><a href="http://code.evanhughes.org/">http://code.evanhughes.org/</a></td>
-    </tr>
-    <tr data-problem-id="mf2">
-      <td>MF2</td>
-      <td></td>
-      <td>suite</td>
-      <td>1</td>
-      <td>1-n</td>
-      <td>continuous</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>yes</td>
-      <td></td>
-      <td><a href="https://doi.org/10.21105/joss.02049">https://doi.org/10.21105/joss.02049</a></td>
-      <td><a href="https://github.com/sjvrijn/mf2">https://github.com/sjvrijn/mf2</a></td>
-    </tr>
-    <tr data-problem-id="amvop">
-      <td>AMVOP</td>
-      <td></td>
-      <td>suite</td>
-      <td>1</td>
-      <td>scalable</td>
-      <td>mixed continuous+ordinal+categorical+both</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td></td>
-      <td><a href="https://doi.org/10.1109/TEVC.2013.2281531">https://doi.org/10.1109/TEVC.2013.2281531</a></td>
-      <td>?</td>
-    </tr>
-    <tr data-problem-id="rwmvop">
-      <td>RWMVOP</td>
-      <td></td>
-      <td>suite</td>
-      <td>1</td>
-      <td>scalable</td>
-      <td>continuous;mixed continuous+ordinal+categorical+both</td>
-      <td>yes</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>real world</td>
-      <td><a href="https://doi.org/10.1109/TEVC.2013.2281531">https://doi.org/10.1109/TEVC.2013.2281531</a></td>
-      <td>?</td>
-    </tr>
-    <tr data-problem-id="sbox_cost">
-      <td>SBOX-COST</td>
-      <td>problems from BBOB but allows instances with the optimum close to the boundary</td>
-      <td>suite</td>
-      <td>1</td>
-      <td>scalable</td>
-      <td>continuous</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td></td>
-      <td><a href="https://doi.org/10.48550/arXiv.2305.12221">https://doi.org/10.48550/arXiv.2305.12221</a></td>
-      <td><a href="https://github.com/IOHprofiler/IOHexperimenter/">https://github.com/IOHprofiler/IOHexperimenter/</a></td>
-    </tr>
-    <tr data-problem-id="mnk_landscapes">
-      <td>ρMNK-Landscapes</td>
-      <td>tunable variable and objective dimensions; tunable multimodality and correlation between objectives</td>
-      <td>generator</td>
-      <td>scalable</td>
-      <td>scalable</td>
-      <td>binary</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td></td>
-      <td><a href="https://doi.org/10.1016/j.ejor.2012.12.019">https://doi.org/10.1016/j.ejor.2012.12.019</a></td>
-      <td><a href="https://gitlab.com/aliefooghe/mocobench/">https://gitlab.com/aliefooghe/mocobench/</a></td>
-    </tr>
-    <tr data-problem-id="mubqp">
-      <td>mUBQP</td>
-      <td>tunable variable and objective dimensions; tunable density and correlation between objectives</td>
-      <td>generator</td>
-      <td>scalable</td>
-      <td>scalable</td>
-      <td>binary</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td></td>
-      <td><a href="https://doi.org/10.1016/j.asoc.2013.11.008">https://doi.org/10.1016/j.asoc.2013.11.008</a></td>
-      <td><a href="https://gitlab.com/aliefooghe/mocobench/">https://gitlab.com/aliefooghe/mocobench/</a></td>
-    </tr>
-    <tr data-problem-id="mtsp">
-      <td>ρmTSP</td>
-      <td>tunable variable and objective dimensions; tunable instance type (euclidian/random); tunable correlation between objectives</td>
-      <td>generator</td>
-      <td>scalable</td>
-      <td>scalable</td>
-      <td>permutations</td>
-      <td>no (apart from being permutations)</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td></td>
-      <td><a href="https://doi.org/10.1007/978-3-319-45823-6_40">https://doi.org/10.1007/978-3-319-45823-6_40</a></td>
-      <td><a href="https://gitlab.com/aliefooghe/mocobench/">https://gitlab.com/aliefooghe/mocobench/</a></td>
-    </tr>
-    <tr data-problem-id="cec2015_dmoo">
-      <td>CEC2015-DMOO</td>
-      <td></td>
-      <td>suite</td>
-      <td>2-3</td>
-      <td>?</td>
-      <td>continuous</td>
-      <td>?</td>
-      <td>yes</td>
-      <td>no</td>
-      <td>no</td>
-      <td></td>
-      <td>Benchmark Functions for CEC 2015 Special Session and Competition on Dynamic Multi-objective Optimization</td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="ealain">
-      <td>Ealain</td>
-      <td>Real-world-like, easily extensible to increase complexity</td>
-      <td>generator</td>
-      <td>1+</td>
-      <td>scalable</td>
-      <td>continuous,binary,integer</td>
-      <td>optional</td>
-      <td>optional</td>
-      <td>no</td>
-      <td>optional</td>
-      <td>Real-world-like</td>
-      <td><a href="https://doi.org/10.1145/3638530.3654299">https://doi.org/10.1145/3638530.3654299</a></td>
-      <td><a href="https://github.com/qrenau/Ealain">https://github.com/qrenau/Ealain</a></td>
-    </tr>
-    <tr data-problem-id="ma_bbob">
-      <td>MA-BBOB</td>
-      <td>Generator that creates affine combinations of BBOB functions</td>
-      <td>generator</td>
-      <td>1</td>
-      <td>scalable</td>
-      <td>continuous</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>artificial</td>
-      <td><a href="https://doi.org/10.1145/3673908">https://doi.org/10.1145/3673908</a></td>
-      <td><a href="https://github.com/IOHprofiler/IOHexperimenter/blob/master/example/Competitions/MA-BBOB/Example_MABBOB.ipynb">https://github.com/IOHprofiler/IOHexperimenter/blob/master/example/Competitions/MA-BBOB/Example_MABBOB.ipynb</a></td>
-    </tr>
-    <tr data-problem-id="mpm2">
-      <td>MPM2</td>
-      <td>nonlinear nonseparable nonsymmetric; scalable in terms of time to evaluate the objective function</td>
-      <td>generator</td>
-      <td>1</td>
-      <td>scalable</td>
-      <td>continuous</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td></td>
-      <td><a href="https://ls11-www.cs.tu-dortmund.de/_media/techreports/tr15-01.pdf">https://ls11-www.cs.tu-dortmund.de/_media/techreports/tr15-01.pdf</a></td>
-      <td><a href="https://github.com/jakobbossek/smoof/blob/master/inst/mpm2.py">https://github.com/jakobbossek/smoof/blob/master/inst/mpm2.py</a></td>
-    </tr>
-    <tr data-problem-id="convex_dtlz2">
-      <td>Convex DTLZ2</td>
-      <td>Variant of DTLZ2 with a convex Pareto front (instead of concave)</td>
-      <td>single</td>
-      <td>2+</td>
-      <td>scalable</td>
-      <td>continuous</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td></td>
-      <td><a href="https://doi.org/10.1109/TEVC.2013.2281535">https://doi.org/10.1109/TEVC.2013.2281535</a></td>
-      <td>?</td>
-    </tr>
-    <tr data-problem-id="inverted_dtlz1">
-      <td>Inverted DTLZ1</td>
-      <td>Variant of DTLZ1 with an inverted Pareto front</td>
-      <td>single</td>
-      <td>2+</td>
-      <td>scalable</td>
-      <td>continuous</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td></td>
-      <td><a href="https://doi.org/10.1109/TEVC.2013.2281534">https://doi.org/10.1109/TEVC.2013.2281534</a></td>
-      <td>?</td>
-    </tr>
-    <tr data-problem-id="minus_dtlz">
-      <td>Minus DTLZ</td>
-      <td>Variant of DTLZ that minimises the inverse of the base DTLZ functions</td>
-      <td>suite</td>
-      <td>2+</td>
-      <td>scalable</td>
-      <td>continuous</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td></td>
-      <td><a href="https://doi.org/10.1109/TEVC.2016.2587749">https://doi.org/10.1109/TEVC.2016.2587749</a></td>
-      <td>?</td>
-    </tr>
-    <tr data-problem-id="minus_wfg">
-      <td>Minus WFG</td>
-      <td>Variant of WFG that minimises the inverse of the base WFG functions</td>
-      <td>suite</td>
-      <td>2+</td>
-      <td>scalable</td>
-      <td>continuous</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td></td>
-      <td><a href="https://doi.org/10.1109/TEVC.2016.2587749">https://doi.org/10.1109/TEVC.2016.2587749</a></td>
-      <td>?</td>
-    </tr>
-    <tr data-problem-id="l1_zdt">
-      <td>L1-ZDT</td>
-      <td>Variant of ZDT with linkages between variables within one of two groups but not between variables in a different group; Linear recombination operators can potentially take advantage of the problem structure</td>
-      <td>suite</td>
-      <td>2</td>
-      <td>scalable</td>
-      <td>continuous;binary</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td></td>
-      <td><a href="https://doi.org/10.1145/1143997.1144179">https://doi.org/10.1145/1143997.1144179</a></td>
-      <td>?</td>
-    </tr>
-    <tr data-problem-id="l2_zdt">
-      <td>L2-ZDT</td>
-      <td>Variant of ZDT with linkages between all variables; Linear recombination operators can potentially take advantage of the problem structure</td>
-      <td>suite</td>
-      <td>2</td>
-      <td>scalable</td>
-      <td>continuous;binary</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td></td>
-      <td><a href="https://doi.org/10.1145/1143997.1144179">https://doi.org/10.1145/1143997.1144179</a></td>
-      <td>?</td>
-    </tr>
-    <tr data-problem-id="l3_zdt">
-      <td>L3-ZDT</td>
-      <td>Variant of L2-ZDT using a mapping to prevent linear recombination operators from potentially taking advantage of the problem structure</td>
-      <td>suite</td>
-      <td>2</td>
-      <td>scalable</td>
-      <td>continuous;binary</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td></td>
-      <td><a href="https://doi.org/10.1145/1143997.1144179">https://doi.org/10.1145/1143997.1144179</a></td>
-      <td>?</td>
-    </tr>
-    <tr data-problem-id="l2_dtlz">
-      <td>L2-DTLZ</td>
-      <td>Variant of DTLZ2 and DTLZ3 with linkages between all variables; Linear recombination operators can potentially take advantage of the problem structure</td>
-      <td>suite</td>
-      <td>2+</td>
-      <td>scalable</td>
-      <td>continuous</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td></td>
-      <td><a href="https://doi.org/10.1145/1143997.1144179">https://doi.org/10.1145/1143997.1144179</a></td>
-      <td>?</td>
-    </tr>
-    <tr data-problem-id="l3_dtlz">
-      <td>L3-DTLZ</td>
-      <td>Variant of L2-DTLZ using a mapping to prevent linear recombination operators from potentially taking advantage of the problem structure</td>
-      <td>suite</td>
-      <td>2+</td>
-      <td>scalable</td>
-      <td>continuous</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td></td>
-      <td><a href="https://doi.org/10.1145/1143997.1144179">https://doi.org/10.1145/1143997.1144179</a></td>
-      <td>?</td>
-    </tr>
-    <tr data-problem-id="cec2018_dt_cec2018_competition_on_dynamic_multiobjective_optimisation">
-      <td>CEC2018 DT - CEC2018 Competition on Dynamic Multiobjective Optimisation</td>
-      <td>14 problems. Time-dependent: Pareto front/Pareto set geometry; irregular Pareto front shapes; variable-linkage; number of disconnected Pareto front segments; etc.</td>
-      <td>suite</td>
-      <td>2 or 3</td>
-      <td>scalable?</td>
-      <td>?</td>
-      <td>no</td>
-      <td>yes</td>
-      <td>no</td>
-      <td>no</td>
-      <td>artificial</td>
-      <td><a href="https://www.academia.edu/download/94499025/TR-CEC2018-DMOP-Competition.pdf">https://www.academia.edu/download/94499025/TR-CEC2018-DMOP-Competition.pdf</a></td>
-      <td><a href="https://pymoo.org/problems/dynamic/df.html">https://pymoo.org/problems/dynamic/df.html</a></td>
-    </tr>
-    <tr data-problem-id="modact_multiobjective_design_of_actuators">
-      <td>MODAct - multiobjective design of actuators</td>
-      <td>Realistic Constrained Multi-Objective Optimization Benchmark Problems from Design. Need the <a href="https://github.com/epfl-lamd/modact">https://github.com/epfl-lamd/modact</a> package installed; evaluation times around 20ms</td>
-      <td>suite</td>
-      <td>2 3 4 or 5</td>
-      <td>20</td>
-      <td>mixed; integer and continuous</td>
-      <td>yes</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>real-world</td>
-      <td><a href="https://doi.org/10.1109/TEVC.2020.3020046">https://doi.org/10.1109/TEVC.2020.3020046</a></td>
-      <td><a href="https://pymoo.org/problems/constrained/modact.html">https://pymoo.org/problems/constrained/modact.html</a></td>
-    </tr>
-    <tr data-problem-id="iohclustering">
-      <td>IOHClustering</td>
-      <td>Set of benchmark problems from clustering: optimization task is selecting cluster centers for a given set of data, with the number of clusters defining problem dimensionality. Includes both a suite and a generator. Based on ML clustering datasets</td>
-      <td>suite; generator</td>
-      <td>1</td>
-      <td>scalable</td>
-      <td>continuous</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>artificial, but based on real data</td>
-      <td><a href="https://arxiv.org/pdf/2505.09233">https://arxiv.org/pdf/2505.09233</a></td>
-      <td><a href="https://github.com/IOHprofiler/IOHClustering">https://github.com/IOHprofiler/IOHClustering</a></td>
-    </tr>
-    <tr data-problem-id="gnbg_ii">
-      <td>GNBG-II</td>
-      <td>Generalized Numerical Benchmark Generator (version 2). Also in IOH <a href="https://github.com/IOHprofiler/IOHGNBG">https://github.com/IOHprofiler/IOHGNBG</a></td>
-      <td>suite; generator</td>
-      <td>1</td>
-      <td>scalable</td>
-      <td>continuous</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>artificial</td>
-      <td><a href="https://dl.acm.org/doi/pdf/10.1145/3712255.3734271">https://dl.acm.org/doi/pdf/10.1145/3712255.3734271</a></td>
-      <td><a href="https://github.com/rohitsalgotra/GNBG-II">https://github.com/rohitsalgotra/GNBG-II</a></td>
-    </tr>
-    <tr data-problem-id="gnbg">
-      <td>GNBG</td>
-      <td>Generalized Numerical Benchmark Generator</td>
-      <td>suite; generator</td>
-      <td>1</td>
-      <td>scalable</td>
-      <td>continuous</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>artificial</td>
-      <td><a href="https://arxiv.org/abs/2312.07083">https://arxiv.org/abs/2312.07083</a></td>
-      <td><a href="https://github.com/Danial-Yazdani/GNBG-Generator">https://github.com/Danial-Yazdani/GNBG-Generator</a></td>
-    </tr>
-    <tr data-problem-id="dynamicbinval">
-      <td>DynamicBinVal</td>
-      <td>Four versions of the dynamic binary value problem</td>
-      <td>suite</td>
-      <td>1</td>
-      <td>scalable</td>
-      <td>binary</td>
-      <td>no</td>
-      <td>yes</td>
-      <td>no</td>
-      <td>no</td>
-      <td>artificial</td>
-      <td><a href="https://arxiv.org/pdf/2404.15837">https://arxiv.org/pdf/2404.15837</a></td>
-      <td><a href="https://github.com/IOHprofiler/IOHexperimenter">https://github.com/IOHprofiler/IOHexperimenter</a></td>
-    </tr>
-    <tr data-problem-id="pbo">
-      <td>PBO</td>
-      <td>Suite of 25 binary optimization problems</td>
-      <td>suite</td>
-      <td>1</td>
-      <td>scalable</td>
-      <td>binary</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>artificial</td>
-      <td><a href="https://dl.acm.org/doi/pdf/10.1145/3319619.3326810">https://dl.acm.org/doi/pdf/10.1145/3319619.3326810</a></td>
-      <td><a href="https://github.com/IOHprofiler/IOHexperimenter">https://github.com/IOHprofiler/IOHexperimenter</a></td>
-    </tr>
-    <tr data-problem-id="w_model">
-      <td>W-model</td>
-      <td>Tunable generator for binary optimization based on several difficulty features</td>
-      <td>generator</td>
-      <td>1</td>
-      <td>scalable</td>
-      <td>binary</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>artificial</td>
-      <td><a href="https://dl.acm.org/doi/abs/10.1145/3205651.3208240?casa_token=S4U_Pi9f6MwAAAAA:U9ztNTPwmupT8K3GamWZfBL7-8fqjxPtr_kprv51vdwA-REsp0EyOFGa99BtbANb0XbqyrVg795hIw">https://dl.acm.org/doi/abs/10.1145/3205651.3208240?casa_token=S4U_Pi9f6MwAAAAA:U9ztNTPwmupT8K3GamWZfBL7-8fqjxPtr_kprv51vdwA-REsp0EyOFGa99BtbANb0XbqyrVg795hIw</a></td>
-      <td><a href="https://github.com/thomasWeise/BBDOB_W_Model">https://github.com/thomasWeise/BBDOB_W_Model</a></td>
-    </tr>
-    <tr data-problem-id="submodular_optimitzation">
-      <td>Submodular Optimitzation</td>
-      <td>set of graph-based submodular optimization problems from 4 problem types</td>
-      <td>suite</td>
-      <td>1</td>
-      <td>scalable</td>
-      <td>binary</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>artificial</td>
-      <td><a href="https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10254181">https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10254181</a></td>
-      <td><a href="https://github.com/IOHprofiler/IOHexperimenter">https://github.com/IOHprofiler/IOHexperimenter</a></td>
-    </tr>
-    <tr data-problem-id="cec2013">
-      <td>CEC2013</td>
-      <td>suite used for cec2013 competition. Also in IOH <a href="https://github.com/IOHprofiler/IOHexperimenter">https://github.com/IOHprofiler/IOHexperimenter</a></td>
-      <td>suite</td>
-      <td>1</td>
-      <td>scalable</td>
-      <td>continuous</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>artificial</td>
-      <td><a href="https://peerj.com/articles/cs-2671/CEC2013.pdf">https://peerj.com/articles/cs-2671/CEC2013.pdf</a></td>
-      <td><a href="https://github.com/P-N-Suganthan/CEC2013">https://github.com/P-N-Suganthan/CEC2013</a></td>
-    </tr>
-    <tr data-problem-id="cec2022">
-      <td>CEC2022</td>
-      <td>suite used for cec2022 competition. Also in IOH <a href="https://github.com/IOHprofiler/IOHexperimenter">https://github.com/IOHprofiler/IOHexperimenter</a></td>
-      <td>suite</td>
-      <td>1</td>
-      <td>scalable</td>
-      <td>continuous</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>artificial</td>
-      <td><a href="https://github.com/P-N-Suganthan/2022-SO-BO/blob/main/CEC2022%20TR.pdf">https://github.com/P-N-Suganthan/2022-SO-BO/blob/main/CEC2022%20TR.pdf</a></td>
-      <td><a href="https://github.com/P-N-Suganthan/2022-SO-BO">https://github.com/P-N-Suganthan/2022-SO-BO</a></td>
-    </tr>
-    <tr data-problem-id="onemaxsphere_zeromaxsphere">
-      <td>Onemax+Sphere / Zeromax+Sphere</td>
-      <td></td>
-      <td>single</td>
-      <td>2</td>
-      <td>scalable</td>
-      <td>binary and continuous;mixed;</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>artificial</td>
-      <td><a href="https://doi.org/10.1145/3449726.3459521">https://doi.org/10.1145/3449726.3459521</a></td>
-      <td>None</td>
-    </tr>
-    <tr data-problem-id="onemaxsphere_deceptivetraprotatedellipsoid">
-      <td>Onemax+Sphere / DeceptiveTrap+RotatedEllipsoid</td>
-      <td></td>
-      <td>single</td>
-      <td>2</td>
-      <td>scalable</td>
-      <td>binary and continuous;mixed;</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>artificial</td>
-      <td><a href="https://doi.org/10.1145/3449726.3459521">https://doi.org/10.1145/3449726.3459521</a></td>
-      <td>None</td>
-    </tr>
-    <tr data-problem-id="inversedeceptivetraprotatedellipsoid_deceptivetraprotatedellipsoid">
-      <td>InverseDeceptiveTrap+RotatedEllipsoid / DeceptiveTrap+RotatedEllipsoid</td>
-      <td></td>
-      <td>single</td>
-      <td>2</td>
-      <td>scalable</td>
-      <td>binary and continuous;mixed;</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>artificial</td>
-      <td><a href="https://doi.org/10.1145/3449726.3459521">https://doi.org/10.1145/3449726.3459521</a></td>
-      <td>None</td>
-    </tr>
-    <tr data-problem-id="porkchopplotinterplanetarytrajectory">
-      <td>PorkchopPlotInterplanetaryTrajectory</td>
-      <td></td>
-      <td>suite</td>
-      <td>1</td>
-      <td>2</td>
-      <td>continuous</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>real-world</td>
-      <td><a href="https://doi.org/10.1109/CEC65147.2025.11042973">https://doi.org/10.1109/CEC65147.2025.11042973</a></td>
-      <td><a href="https://github.com/ShuaiqunPan/Transfer_Random_forests_BBOB_Real_world">https://github.com/ShuaiqunPan/Transfer_Random_forests_BBOB_Real_world</a></td>
-    </tr>
-    <tr data-problem-id="kinematicsrobotarm">
-      <td>KinematicsRobotArm</td>
-      <td></td>
-      <td>suite</td>
-      <td>1</td>
-      <td>21</td>
-      <td>continuous</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>real-world</td>
-      <td><a href="https://doi.org/10.1023/A:1013258808932">https://doi.org/10.1023/A:1013258808932</a></td>
-      <td><a href="https://github.com/ShuaiqunPan/Transfer_Random_forests_BBOB_Real_world">https://github.com/ShuaiqunPan/Transfer_Random_forests_BBOB_Real_world</a></td>
-    </tr>
-    <tr data-problem-id="vehicledynamics">
-      <td>VehicleDynamics</td>
-      <td></td>
-      <td>suite</td>
-      <td>1</td>
-      <td>2</td>
-      <td>continuous</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>real-world</td>
-      <td><a href="https://www.scitepress.org/Papers/2023/121580/121580.pdf">https://www.scitepress.org/Papers/2023/121580/121580.pdf</a></td>
-      <td><a href="https://zenodo.org/records/8307853">https://zenodo.org/records/8307853</a></td>
-    </tr>
-    <tr data-problem-id="mechbench">
-      <td>MECHBench</td>
-      <td>This is a set of problems with inspiration from Structural Mechanics Design Optimization. The suite comprises three physical models, from which the user may define different kind of problems which impact the final design output.</td>
-      <td>Problem Suite</td>
-      <td>1</td>
-      <td>scalable'</td>
-      <td>Continuous</td>
-      <td>yes</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>Real-World Application</td>
-      <td><a href="https://arxiv.org/abs/2511.10821">https://arxiv.org/abs/2511.10821</a></td>
-      <td><a href="https://github.com/BayesOptApp/MECHBench">https://github.com/BayesOptApp/MECHBench</a></td>
-    </tr>
-    <tr data-problem-id="expobench">
-      <td>EXPObench</td>
-      <td>Wind farm layout optimization, gas filter design, pipe shape optimization, hyperparameter tuning, and hospital simulation</td>
-      <td>Problem Suite</td>
-      <td>1</td>
-      <td>10 to 135</td>
-      <td>Continuous, Integer, Categorical, Conditional</td>
-      <td>yes</td>
-      <td>no</td>
-      <td>yes</td>
-      <td>no</td>
-      <td>Real-World Application</td>
-      <td><a href="https://doi.org/10.1016/j.asoc.2023.110744">https://doi.org/10.1016/j.asoc.2023.110744</a></td>
-      <td><a href="https://github.com/AlgTUDelft/ExpensiveOptimBenchmark">https://github.com/AlgTUDelft/ExpensiveOptimBenchmark</a></td>
-    </tr>
-    <tr data-problem-id="gasoline_direct_injection_engine_design">
-      <td>Gasoline direct injection engine design</td>
-      <td>A multi-objective optimization problem seeking to minimize fuel consumption and NOx emissions over a two-minute dynamic duty cycle, subject to five constraints (turbine inlet temperature, number of knock occurrences, peak cylinder pressure, peak cylinder pressure rise, total work). Seven decision variables are defined: four define the hardware choices of cylinder compression ratio, turbo machinery and EGR cooler sizing; three relate to control variables that parameterise the engine control logic.</td>
-      <td>Single Problem</td>
-      <td>2</td>
-      <td>7</td>
-      <td>Continuous, Ordinal</td>
-      <td>yes</td>
-      <td>no</td>
-      <td>no</td>
-      <td>yes</td>
-      <td>Real-World Application</td>
-      <td></td>
-      <td><a href="https://doi.org/10.1016/j.ejor.2022.08.032">https://doi.org/10.1016/j.ejor.2022.08.032</a></td>
-    </tr>
-    <tr data-problem-id="beacon">
-      <td>BEACON</td>
-      <td>Generator for bi-objective benchmark problems with explicitly controlled correlations in continuous spaces.</td>
-      <td>Generator</td>
-      <td>2</td>
-      <td>scalable</td>
-      <td>Continuous</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>Artificially Generated</td>
-      <td><a href="https://dl.acm.org/doi/10.1145/3712255.3734303">https://dl.acm.org/doi/10.1145/3712255.3734303</a></td>
-      <td><a href="https://github.com/Stebbet/BEACON/">https://github.com/Stebbet/BEACON/</a></td>
-    </tr>
-    <tr data-problem-id="tulipaenergy">
-      <td>TulipaEnergy</td>
-      <td>Determine the optimal investment and operation decisions  for different types of assets in the energy system (production, consumption, conversion, storage, and transport), while minimizing loss of load.</td>
-      <td>Problem Suite</td>
-      <td>1</td>
-      <td>scalable</td>
-      <td>Continuous</td>
-      <td>yes</td>
-      <td>no</td>
-      <td>yes</td>
-      <td>yes</td>
-      <td>Real-World Application</td>
-      <td>See <a href="https://tulipaenergy.github.io/TulipaEnergyModel.jl/stable/40-scientific-foundation/45-scientific-references">https://tulipaenergy.github.io/TulipaEnergyModel.jl/stable/40-scientific-foundation/45-scientific-references</a></td>
-      <td><a href="https://tulipaenergy.github.io/TulipaEnergyModel.jl/stable/">https://tulipaenergy.github.io/TulipaEnergyModel.jl/stable/</a></td>
-    </tr>
-    <tr data-problem-id="ato">
-      <td>ATO</td>
-      <td>Parameters of the Modules of the Automatic Train Operation should be optimized. The parameters are continuous with different ranges. There are two objectives (minimizing energy consumption, minimizing driving duration.</td>
-      <td>Single Problem</td>
-      <td>2</td>
-      <td>10</td>
-      <td>Continuous</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>Real-World Application</td>
-      <td></td>
-      <td>-</td>
-    </tr>
-    <tr data-problem-id="brachytherapy_treatment_planning">
-      <td>Brachytherapy treatment planning</td>
-      <td>Treatment planning for internal radiation therapy</td>
-      <td>Problem Suite</td>
-      <td>2-3</td>
-      <td>100-500</td>
-      <td>Continuous</td>
-      <td>yes</td>
-      <td>no</td>
-      <td>no</td>
-      <td>yes</td>
-      <td>Real-World Application</td>
-      <td><a href="https://www.sciencedirect.com/science/article/pii/S1538472123016781">https://www.sciencedirect.com/science/article/pii/S1538472123016781</a></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="fleetopt">
-      <td>FleetOpt</td>
-      <td>Healthcare organisation in the UK provided data about their current fleet of vehicles to conduct non-emergency heathcare trips in the Argyll and Bute region of Scotland, UK. They also provided historical data about the trips the vehicles took and about the bases which the vehicles return to. The aim is to reduce the existing fleet of vehicles while still ensuring all trips can be covered. Moving a vehicle from one base to another to help cover trips is OK as long as the original base can still cover its trips. Link to paper with more details: <a href="https://dl.acm.org/doi/abs/10.1145/3638530.3664137">https://dl.acm.org/doi/abs/10.1145/3638530.3664137</a></td>
-      <td>Single Problem</td>
-      <td>1</td>
-      <td>Upper level: 54; lower level: 13208</td>
-      <td>Integer</td>
-      <td>yes</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>Real-World Application</td>
-      <td><a href="https://dl.acm.org/doi/abs/10.1145/3638530.3664137">https://dl.acm.org/doi/abs/10.1145/3638530.3664137</a></td>
-      <td>Not public: was done for real client with their private data</td>
-    </tr>
-    <tr data-problem-id="building_spatial_design">
-      <td>Building spatial design</td>
-      <td>Optimise the spatial layout of a building to: minimise energy consumption for climate control, and minimise the strain on the structure</td>
-      <td>Single Problem</td>
-      <td>2</td>
-      <td>scalable depending on problem size (e.g. 90 for)</td>
-      <td>Continuous, Boolean</td>
-      <td>yes</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>Real-World Application</td>
-      <td><a href="https://hdl.handle.net/1887/81789">https://hdl.handle.net/1887/81789</a></td>
-      <td><a href="https://github.com/TUe-excellent-buildings/BSO-toolbox">https://github.com/TUe-excellent-buildings/BSO-toolbox</a></td>
-    </tr>
-    <tr data-problem-id="electric_motor_design_optimization">
-      <td>Electric Motor Design Optimization</td>
-      <td>The goal is to find a design of a synchronous electric motor for power steering systems that minimizes costs and satisfies all constraints.</td>
-      <td>Single Problem</td>
-      <td>1</td>
-      <td>13</td>
-      <td>Continuous, Integer</td>
-      <td>yes</td>
-      <td>no</td>
-      <td>yes</td>
-      <td>no</td>
-      <td>Real-World Application</td>
-      <td><a href="https://dis.ijs.si/tea/Publications/Tusar23Multistep.pdf">https://dis.ijs.si/tea/Publications/Tusar23Multistep.pdf</a> (paper in Slovene)</td>
-      <td>Implementation not freely available</td>
-    </tr>
-    <tr data-problem-id="bono_bench">
-      <td>BONO-Bench</td>
-      <td>Bi-objective problem generator and suite with scalable continuous decision space. Features complex problem properties (different types of multimodality and challenges in decision and objective space) as well as Pareto front approximations with error guarantees for the hypervolume and exact R2 indicators.</td>
-      <td>Generator</td>
-      <td>2</td>
-      <td>scalable</td>
-      <td>Continuous</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>Artificially Generated</td>
-      <td></td>
-      <td><a href="https://github.com/schaepermeier/bonobench">https://github.com/schaepermeier/bonobench</a></td>
-    </tr>
-    <tr data-problem-id="randoptgen">
-      <td>RandOptGen</td>
-      <td>RandOptGen: A Unified Random Problem Generator for Single-and Multi-Objective Optimization Problems with Mixed-Variable Input Spaces</td>
-      <td>Generator</td>
-      <td>scalable</td>
-      <td>scalable</td>
-      <td>Continuous, Integer, Boolean</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>Artificially Generated</td>
-      <td></td>
-      <td><a href="https://github.com/MALEO-research-group/RandOptGen">https://github.com/MALEO-research-group/RandOptGen</a></td>
-    </tr>
-    <tr data-problem-id="cuter">
-      <td>CUTEr</td>
-      <td>A constrained and unconstrained testing environment</td>
-      <td>Problem Suite</td>
-      <td>1</td>
-      <td>scalable</td>
-      <td>Continuous, Integer, Boolean</td>
-      <td>yes</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>Artificially Generated</td>
-      <td><a href="https://dl.acm.org/doi/10.1145/962437.962439">https://dl.acm.org/doi/10.1145/962437.962439</a></td>
-      <td>Not Found</td>
-    </tr>
-    <tr data-problem-id="cutest">
-      <td>CUTEst</td>
-      <td>The Constrained and Unconstrained Testing Environment with safe threads (CUTEst) for optimization software</td>
-      <td>Problem Suite</td>
-      <td>1</td>
-      <td>scalable</td>
-      <td>Continuous, Integer, Boolean</td>
-      <td>yes</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>Artificially Generated</td>
-      <td><a href="https://link.springer.com/article/10.1007/s10589-014-9687-3">https://link.springer.com/article/10.1007/s10589-014-9687-3</a></td>
-      <td><a href="https://github.com/jfowkes/pycutest">https://github.com/jfowkes/pycutest</a></td>
-    </tr>
-    <tr data-problem-id="puboi">
-      <td>PUBOi</td>
-      <td>A benchmark in which variable importance is tunable, based on the Walsh function</td>
-      <td>Generator</td>
-      <td>1</td>
-      <td>scalable</td>
-      <td>Boolean</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>no</td>
-      <td>Artificially Generated</td>
-      <td><a href="https://link.springer.com/chapter/10.1007/978-3-031-04148-8_12">https://link.springer.com/chapter/10.1007/978-3-031-04148-8_12</a></td>
-      <td><a href="https://gitlab.com/verel/pubo-importance-benchmark">https://gitlab.com/verel/pubo-importance-benchmark</a></td>
-    </tr>
-  </tbody>
-<tfoot><tr><th>name</th> <th>textual description</th> <th>suite/generator/single</th> <th>objectives</th> <th>dimensionality</th> <th>variable type</th> <th>constraints</th> <th>dynamic</th> <th>noise</th> <th>multi-fidelity</th> <th>source (real-world/artificial)</th> <th>reference</th> <th>implementation</th></tr> </tfoot></table>
-    </div>
-</section>
-
-<section class="details-shell" id="problem-details" aria-live="polite">
-    <h3 class="details-title">Problem details</h3>
-    <p class="details-hint" id="problem-details-hint">Click a table row to inspect full details.</p>
-    <dl class="details-grid" id="problem-details-content"></dl>
-    <div class="snippet-shell" id="problem-snippet-shell">
-        <h4 class="snippet-title">README.md</h4>
-        <p class="snippet-hint" id="problem-readme-hint">README content for this snippet folder will appear here.</p>
-        <div class="snippet-readme markdown-body" id="problem-readme-content"></div>
-        <h4 class="snippet-title">Snippet: <code id="problem-snippet-path">call_{problem_id}.py</code></h4>
-        <p class="snippet-meta"><a id="problem-snippet-folder-link" href="#" target="_blank" rel="noopener noreferrer">Open snippet folder</a></p>
-        <p class="snippet-hint" id="problem-snippet-hint">Select a problem to check for an available code snippet.</p>
-        <pre class="snippet-code"><code id="problem-snippet-content"></code></pre>
-    </div>
-</section>
+<section class="table-shell">
+    <div class="table-toolbar">
+        <details class="column-toolbar-disclosure">
+            <summary class="toolbar-title">Visible Columns</summary>
+            <div class="toolbar-actions">
+                <button type="button" class="toolbar-btn" id="show-all-columns">Show all</button>
+                <button type="button" class="toolbar-btn" id="hide-all-columns">Hide all</button>
+            </div>
+            <div class="column-controls">
+                <label class="column-chip"><input class="col-toggle" type="checkbox" data-column="0"><span>ID</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="1" checked><span>Name</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="2" checked><span>Type</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="3" checked><span>Variable Types</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="4" checked><span>Total Variables</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="5" checked><span>Objectives</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="6" checked><span>Properties</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="7" checked><span>Constraint Types</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="8" checked><span>Total Constraints</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="9" checked><span>Dynamics</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="10" checked><span>Noise</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="11" checked><span>Partial Evaluations</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="12" checked><span>Independent Objectives</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="13" checked><span>Fidelity Levels</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="14"><span>Full Name</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="15"><span>Description</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="16"><span>Tags</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="17"><span>References</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="18"><span>Implementations</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="19"><span>Modality</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="20"><span>Examples</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="21"><span>Source</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="22"><span>Binary Vars</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="23"><span>Categorical Vars</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="24"><span>Continuous Vars</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="25"><span>Integer Vars</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="26"><span>Implementation Names</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="27"><span>Implementation Languages</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="28"><span>Implementation Evaluation Times</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="29"><span>Implementation Links</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="30"><span>Implementation Descriptions</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="31"><span>Implementation Requirements</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="32"><span>Hard Box Constraints</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="33"><span>Soft Box Constraints</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="34"><span>Hard Linear Constraints</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="35"><span>Soft Linear Constraints</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="36"><span>Hard Function Constraints</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="37"><span>Soft Function Constraints</span></label>
+            </div>
+        </details>
+    </div>
+    <div class="table-wrap">
+        <table class="dataframe display compact display styled-table" id="problems">
+  <thead>
+    <tr style="text-align: right;">
+      <th>ID</th>
+      <th>Name</th>
+      <th>Type</th>
+      <th>Variable Types</th>
+      <th>Total Variables</th>
+      <th>Objectives</th>
+      <th>Properties</th>
+      <th>Constraint Types</th>
+      <th>Total Constraints</th>
+      <th>Dynamics</th>
+      <th>Noise</th>
+      <th>Partial Evaluations</th>
+      <th>Independent Objectives</th>
+      <th>Fidelity Levels</th>
+      <th>Full Name</th>
+      <th>Description</th>
+      <th>Tags</th>
+      <th>References</th>
+      <th>Implementations</th>
+      <th>Modality</th>
+      <th>Examples</th>
+      <th>Source</th>
+      <th>Binary Vars</th>
+      <th>Categorical Vars</th>
+      <th>Continuous Vars</th>
+      <th>Integer Vars</th>
+      <th>Implementation Names</th>
+      <th>Implementation Languages</th>
+      <th>Implementation Evaluation Times</th>
+      <th>Implementation Links</th>
+      <th>Implementation Descriptions</th>
+      <th>Implementation Requirements</th>
+      <th>Hard Box Constraints</th>
+      <th>Soft Box Constraints</th>
+      <th>Hard Linear Constraints</th>
+      <th>Soft Linear Constraints</th>
+      <th>Hard Function Constraints</th>
+      <th>Soft Function Constraints</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr data-problem-id="fn_ato">
+      <td>fn_ato</td>
+      <td>ATO</td>
+      <td>Problem</td>
+      <td>continuous</td>
+      <td>10</td>
+      <td>2</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>no</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Parameters of the Modules of the Automatic Train Operation are optimized; two objectives: minimizing energy consumption and minimizing driving duration.</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>unimodal</td>
+      <td></td>
+      <td>real-world</td>
+      <td></td>
+      <td></td>
+      <td>10</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="fn_building_spatial">
+      <td>fn_building_spatial</td>
+      <td>Building spatial design</td>
+      <td>Problem</td>
+      <td>binary | continuous</td>
+      <td>>=2</td>
+      <td>2</td>
+      <td></td>
+      <td>unknown | box</td>
+      <td>>=2</td>
+      <td></td>
+      <td></td>
+      <td>no</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Optimise the spatial layout of a building to minimise energy consumption for climate control and minimise the strain on the structure. Many hard constraints; mixed-variable (continuous+binary); expensive evaluations.</td>
+      <td></td>
+      <td>Building spatial design, <a href="https://hdl.handle.net/1887/81789">https://hdl.handle.net/1887/81789</a></td>
+      <td>impl_bso_toolbox</td>
+      <td></td>
+      <td></td>
+      <td>real-world</td>
+      <td>>=1</td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>BSO-toolbox</td>
+      <td>C++</td>
+      <td>~1s (smallest) to ~40s (larger)</td>
+      <td><a href="https://github.com/TUe-excellent-buildings/BSO-toolbox">https://github.com/TUe-excellent-buildings/BSO-toolbox</a></td>
+      <td>Building Spatial Design toolbox (TU/e)</td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="fn_convex_dtlz2">
+      <td>fn_convex_dtlz2</td>
+      <td>Convex DTLZ2</td>
+      <td>Problem</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>[10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Variant of DTLZ2 with a convex Pareto front (instead of concave)</td>
+      <td></td>
+      <td>Convex DTLZ2, <a href="https://doi.org/10.1109/TEVC.2013.2281535">https://doi.org/10.1109/TEVC.2013.2281535</a></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="fn_emdo">
+      <td>fn_emdo</td>
+      <td>Electric Motor Design Optimization</td>
+      <td>Problem</td>
+      <td>integer | continuous</td>
+      <td>26</td>
+      <td>1</td>
+      <td>noisy</td>
+      <td>box | unknown</td>
+      <td>>=14</td>
+      <td></td>
+      <td>noisy</td>
+      <td>no</td>
+      <td></td>
+      <td></td>
+      <td>Electric Motor Design Optimization</td>
+      <td># Goal\nFind a design of a synchronous electric motor for power steering systems that minimizes costs and satisfies all constraints.\n\n# Motivation\nChallenging to find good solutions in a limited time.\n\n# Key Challenges\n* Time-consuming solution evaluation\n* Highly-constrained problem\n* Constraints are multimodal\n\nThis is not an available problem, but could be interesting to show to researchers which difficulties appear in real-world problems.</td>
+      <td></td>
+      <td>A Multi-Step Evaluation Process in Electric Motor Design, Tea Tušar; Peter Korošec; Bogdan Filipič, <a href="https://dis.ijs.si/tea/Publications/Tusar23Multistep.pdf">https://dis.ijs.si/tea/Publications/Tusar23Multistep.pdf</a></td>
+      <td>impl_emdo</td>
+      <td>multimodal</td>
+      <td></td>
+      <td>real-world</td>
+      <td></td>
+      <td></td>
+      <td>13</td>
+      <td>13</td>
+      <td>Electric Motor Design Optimization</td>
+      <td>Python</td>
+      <td>8 minutes</td>
+      <td></td>
+      <td>Not publicly available</td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="fn_fleetopt">
+      <td>fn_fleetopt</td>
+      <td>FleetOpt</td>
+      <td>Problem</td>
+      <td>integer</td>
+      <td>{54, 13208}</td>
+      <td>1</td>
+      <td></td>
+      <td>unknown</td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td>yes</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>UK healthcare organisation fleet optimisation: reduce the fleet of non-emergency healthcare trip vehicles while still ensuring all trips can be covered. Bilevel: upper level 54 vars, lower level 13208 vars.</td>
+      <td></td>
+      <td>FleetOpt, <a href="https://dl.acm.org/doi/abs/10.1145/3638530.3664137">https://dl.acm.org/doi/abs/10.1145/3638530.3664137</a></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>real-world</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>{54, 13208}</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="fn_gasoline">
+      <td>fn_gasoline</td>
+      <td>Gasoline direct injection engine design</td>
+      <td>Problem</td>
+      <td>integer | continuous</td>
+      <td>14</td>
+      <td>2</td>
+      <td>multi-fidelity</td>
+      <td>unknown</td>
+      <td>5</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>[1, 2]</td>
+      <td></td>
+      <td>Multi-objective optimization to minimize fuel consumption and NOx emissions over a two-minute dynamic duty cycle, subject to five constraints (turbine inlet temperature, knock occurrences, peak cylinder pressure, peak cylinder pressure rise, total work). Seven decision variables cover hardware choices and engine control parameters.</td>
+      <td></td>
+      <td>Gasoline direct injection engine design, <a href="https://doi.org/10.1016/j.ejor.2022.08.032">https://doi.org/10.1016/j.ejor.2022.08.032</a></td>
+      <td>impl_gasoline</td>
+      <td></td>
+      <td></td>
+      <td>real-world</td>
+      <td></td>
+      <td></td>
+      <td>7</td>
+      <td>7</td>
+      <td>Gasoline direct injection engine design</td>
+      <td>Matlab Simulink / Wave RT</td>
+      <td></td>
+      <td><a href="https://doi.org/10.1016/j.ejor.2022.08.032">https://doi.org/10.1016/j.ejor.2022.08.032</a></td>
+      <td>Proprietary Matlab Simulink + Wave RT co-simulation</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="fn_invdeceptive_deceptive_rotell">
+      <td>fn_invdeceptive_deceptive_rotell</td>
+      <td>InverseDeceptiveTrap+RotatedEllipsoid / DeceptiveTrap+RotatedEllipsoid</td>
+      <td>Problem</td>
+      <td>binary | continuous</td>
+      <td>>=2</td>
+      <td>2</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Mixed-variable multi-objective test problems, <a href="https://doi.org/10.1145/3449726.3459521">https://doi.org/10.1145/3449726.3459521</a></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>artificial</td>
+      <td>>=1</td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="fn_inverted_dtlz1">
+      <td>fn_inverted_dtlz1</td>
+      <td>Inverted DTLZ1</td>
+      <td>Problem</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>[10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Variant of DTLZ1 with an inverted Pareto front</td>
+      <td></td>
+      <td>Inverted DTLZ1, <a href="https://doi.org/10.1109/TEVC.2013.2281534">https://doi.org/10.1109/TEVC.2013.2281534</a></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="fn_jsec2019">
+      <td>fn_jsec2019</td>
+      <td>JSEC2019</td>
+      <td>Problem</td>
+      <td>continuous</td>
+      <td>32</td>
+      <td>[1, 2, 3, 4, 5]</td>
+      <td></td>
+      <td>unknown</td>
+      <td>22</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>expensive evaluations 3s; 22 constraints</td>
+      <td></td>
+      <td>JPNSEC EC-Symposium 2019 competition, <a href="http://www.jpnsec.org/files/competition2019/EC-Symposium-2019-Competition-English.html">http://www.jpnsec.org/files/competition2019/EC-Symposium-2019-Competition-English.html</a></td>
+      <td>impl_jsec2019</td>
+      <td></td>
+      <td></td>
+      <td>real-world</td>
+      <td></td>
+      <td></td>
+      <td>32</td>
+      <td></td>
+      <td>JSEC 2019 competition</td>
+      <td></td>
+      <td>3s</td>
+      <td><a href="http://www.jpnsec.org/files/competition2019/EC-Symposium-2019-Competition-English.html">http://www.jpnsec.org/files/competition2019/EC-Symposium-2019-Competition-English.html</a></td>
+      <td>JPNSEC EC-Symposium 2019 competition problem</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="fn_onemax_sphere_deceptive_rotell">
+      <td>fn_onemax_sphere_deceptive_rotell</td>
+      <td>Onemax+Sphere / DeceptiveTrap+RotatedEllipsoid</td>
+      <td>Problem</td>
+      <td>binary | continuous</td>
+      <td>>=2</td>
+      <td>2</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Mixed-variable multi-objective test problems, <a href="https://doi.org/10.1145/3449726.3459521">https://doi.org/10.1145/3449726.3459521</a></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>artificial</td>
+      <td>>=1</td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="fn_onemax_sphere_zeromax_sphere">
+      <td>fn_onemax_sphere_zeromax_sphere</td>
+      <td>Onemax+Sphere / Zeromax+Sphere</td>
+      <td>Problem</td>
+      <td>binary | continuous</td>
+      <td>>=2</td>
+      <td>2</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Onemax+Sphere / Zeromax+Sphere, <a href="https://doi.org/10.1145/3449726.3459521">https://doi.org/10.1145/3449726.3459521</a></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>artificial</td>
+      <td>>=1</td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="fn_radar_waveform">
+      <td>fn_radar_waveform</td>
+      <td>Radar waveform</td>
+      <td>Problem</td>
+      <td>integer</td>
+      <td>4-12</td>
+      <td>9</td>
+      <td></td>
+      <td>unknown</td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Radar waveform design, <a href="https://doi.org/10.1007/978-3-540-70928-2_53">https://doi.org/10.1007/978-3-540-70928-2_53</a></td>
+      <td>impl_radar_waveform</td>
+      <td></td>
+      <td></td>
+      <td>real-world</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>4-12</td>
+      <td>Evan Hughes radar waveform code</td>
+      <td></td>
+      <td></td>
+      <td><a href="http://code.evanhughes.org/">http://code.evanhughes.org/</a></td>
+      <td>Radar waveform design reference implementation</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="gen_beacon">
+      <td>gen_beacon</td>
+      <td>BEACON</td>
+      <td>Generator</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>2</td>
+      <td></td>
+      <td>box</td>
+      <td>0</td>
+      <td></td>
+      <td></td>
+      <td>no</td>
+      <td></td>
+      <td></td>
+      <td>Continuous Bi-objective Benchmark problems with Explicit Adjustable COrrelatioN control</td>
+      <td>Generator for bi-objective benchmark problems with explicitly controlled correlations in continuous spaces. Multimodal with random structure.</td>
+      <td></td>
+      <td>BEACON, <a href="https://dl.acm.org/doi/10.1145/3712255.3734303">https://dl.acm.org/doi/10.1145/3712255.3734303</a></td>
+      <td>impl_beacon</td>
+      <td>multimodal</td>
+      <td></td>
+      <td>artificial</td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>BEACON</td>
+      <td>Python</td>
+      <td>negligible</td>
+      <td><a href="https://github.com/Stebbet/BEACON/">https://github.com/Stebbet/BEACON/</a></td>
+      <td>Continuous Bi-objective Benchmark with Explicit Adjustable COrrelatioN control</td>
+      <td></td>
+      <td>0</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="gen_bono_bench">
+      <td>gen_bono_bench</td>
+      <td>BONO-Bench</td>
+      <td>Generator</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>2</td>
+      <td></td>
+      <td>box</td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td>no</td>
+      <td></td>
+      <td></td>
+      <td>Bi-objective Numerical Optimization Benchmark</td>
+      <td>Bi-objective problem generator and suite with scalable continuous decision space. Features complex problem properties and Pareto front approximations with error guarantees for the hypervolume and exact R2 indicators.</td>
+      <td></td>
+      <td></td>
+      <td>impl_bonobench</td>
+      <td>multimodal</td>
+      <td></td>
+      <td>artificial</td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>BONO-Bench</td>
+      <td>Python</td>
+      <td></td>
+      <td><a href="https://github.com/schaepermeier/bonobench">https://github.com/schaepermeier/bonobench</a></td>
+      <td>Bi-objective Numerical Optimization Benchmark (BONO-Bench)</td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="gen_ealain">
+      <td>gen_ealain</td>
+      <td>Ealain</td>
+      <td>Generator</td>
+      <td>binary | continuous | integer</td>
+      <td>>=3</td>
+      <td>[1, 10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
+      <td>dynamic | multi-fidelity</td>
+      <td>unknown</td>
+      <td>>=1</td>
+      <td>optional</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>[1, 2]</td>
+      <td></td>
+      <td>Real-world-like, easily extensible to increase complexity</td>
+      <td></td>
+      <td>Ealain, <a href="https://doi.org/10.1145/3638530.3654299">https://doi.org/10.1145/3638530.3654299</a></td>
+      <td>impl_ealain</td>
+      <td></td>
+      <td></td>
+      <td>real-world-like</td>
+      <td>>=1</td>
+      <td></td>
+      <td>>=1</td>
+      <td>>=1</td>
+      <td>Ealain</td>
+      <td></td>
+      <td></td>
+      <td><a href="https://github.com/qrenau/Ealain">https://github.com/qrenau/Ealain</a></td>
+      <td>Real-world-like extensible benchmark problem generator</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="gen_gnbg">
+      <td>gen_gnbg</td>
+      <td>GNBG</td>
+      <td>Generator</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Generator counterpart of GNBG.</td>
+      <td></td>
+      <td>GNBG, <a href="https://arxiv.org/abs/2312.07083">https://arxiv.org/abs/2312.07083</a></td>
+      <td>impl_gnbg</td>
+      <td></td>
+      <td></td>
+      <td>artificial</td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>GNBG Generator</td>
+      <td></td>
+      <td></td>
+      <td><a href="https://github.com/Danial-Yazdani/GNBG-Generator">https://github.com/Danial-Yazdani/GNBG-Generator</a></td>
+      <td>Generalized Numerical Benchmark Generator</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="gen_gnbg_ii">
+      <td>gen_gnbg_ii</td>
+      <td>GNBG-II</td>
+      <td>Generator</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Generator counterpart of GNBG-II.</td>
+      <td></td>
+      <td>GNBG-II, <a href="https://dl.acm.org/doi/pdf/10.1145/3712255.3734271">https://dl.acm.org/doi/pdf/10.1145/3712255.3734271</a></td>
+      <td>["impl_gnbg_ii", "impl_iohgnbg"]</td>
+      <td></td>
+      <td></td>
+      <td>artificial</td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>GNBG-II | IOHGNBG</td>
+      <td></td>
+      <td></td>
+      <td><a href="https://github.com/rohitsalgotra/GNBG-II">https://github.com/rohitsalgotra/GNBG-II</a> | <a href="https://github.com/IOHprofiler/IOHGNBG">https://github.com/IOHprofiler/IOHGNBG</a></td>
+      <td>Generalized Numerical Benchmark Generator version 2 | IOHprofiler version of GNBG</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="gen_gpd">
+      <td>gen_gpd</td>
+      <td>GPD</td>
+      <td>Generator</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>[10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
+      <td>noisy</td>
+      <td>unknown</td>
+      <td>>=1</td>
+      <td></td>
+      <td>optional</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>GPD generator, <a href="https://doi.org/10.1016/j.asoc.2020.106139">https://doi.org/10.1016/j.asoc.2020.106139</a></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="gen_iohclustering">
+      <td>gen_iohclustering</td>
+      <td>IOHClustering</td>
+      <td>Generator</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Generator counterpart of the IOHClustering suite.</td>
+      <td></td>
+      <td>IOHClustering, <a href="https://arxiv.org/pdf/2505.09233">https://arxiv.org/pdf/2505.09233</a></td>
+      <td>impl_iohclustering</td>
+      <td>multimodal</td>
+      <td></td>
+      <td>artificial-from-real-data</td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>IOHClustering</td>
+      <td></td>
+      <td></td>
+      <td><a href="https://github.com/IOHprofiler/IOHClustering">https://github.com/IOHprofiler/IOHClustering</a></td>
+      <td>Clustering-based optimization benchmark built on ML datasets</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="gen_ma_bbob">
+      <td>gen_ma_bbob</td>
+      <td>MA-BBOB</td>
+      <td>Generator</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Generator that creates affine combinations of BBOB functions</td>
+      <td></td>
+      <td>MA-BBOB, <a href="https://doi.org/10.1145/3673908">https://doi.org/10.1145/3673908</a></td>
+      <td>["impl_iohexperimenter", "impl_ma_bbob"]</td>
+      <td>multimodal</td>
+      <td></td>
+      <td>artificial</td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>IOHexperimenter | MA-BBOB (IOHexperimenter)</td>
+      <td>C++/Python</td>
+      <td></td>
+      <td><a href="https://github.com/IOHprofiler/IOHexperimenter">https://github.com/IOHprofiler/IOHexperimenter</a> | <a href="https://github.com/IOHprofiler/IOHexperimenter/blob/master/example/Competitions/MA-BBOB/Example_MABBOB.ipynb">https://github.com/IOHprofiler/IOHexperimenter/blob/master/example/Competitions/MA-BBOB/Example_MABBOB.ipynb</a></td>
+      <td>IOHprofiler experimenter framework | Example notebook for MA-BBOB in IOHexperimenter</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="gen_mpm2">
+      <td>gen_mpm2</td>
+      <td>MPM2</td>
+      <td>Generator</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>nonlinear nonseparable nonsymmetric; scalable in terms of time to evaluate the objective function</td>
+      <td></td>
+      <td>MPM2 technical report TR15-01, <a href="https://ls11-www.cs.tu-dortmund.de/_media/techreports/tr15-01.pdf">https://ls11-www.cs.tu-dortmund.de/_media/techreports/tr15-01.pdf</a></td>
+      <td>impl_mpm2</td>
+      <td>multimodal</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>MPM2 (smoof)</td>
+      <td>Python</td>
+      <td></td>
+      <td><a href="https://github.com/jakobbossek/smoof/blob/master/inst/mpm2.py">https://github.com/jakobbossek/smoof/blob/master/inst/mpm2.py</a></td>
+      <td>Python implementation of MPM2 distributed with smoof</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="gen_mubqp">
+      <td>gen_mubqp</td>
+      <td>mUBQP</td>
+      <td>Generator</td>
+      <td>binary</td>
+      <td>>=1</td>
+      <td>[1, 10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>tunable variable and objective dimensions; tunable density and correlation between objectives</td>
+      <td></td>
+      <td>mUBQP benchmark, <a href="https://doi.org/10.1016/j.asoc.2013.11.008">https://doi.org/10.1016/j.asoc.2013.11.008</a></td>
+      <td>impl_mocobench</td>
+      <td>["multimodal", "quadratic"]</td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>mocobench</td>
+      <td>C++</td>
+      <td></td>
+      <td><a href="https://gitlab.com/aliefooghe/mocobench/">https://gitlab.com/aliefooghe/mocobench/</a></td>
+      <td>Multi-objective combinatorial optimization benchmark</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="gen_puboi">
+      <td>gen_puboi</td>
+      <td>PUBOi</td>
+      <td>Generator</td>
+      <td>binary</td>
+      <td>>=1</td>
+      <td>1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>no</td>
+      <td></td>
+      <td></td>
+      <td>Polynomial Unconstrained Binary Optimization with tunable importance</td>
+      <td>A benchmark in which variable importance is tunable, based on the Walsh function.</td>
+      <td></td>
+      <td>PUBOi, <a href="https://link.springer.com/chapter/10.1007/978-3-031-04148-8_12">https://link.springer.com/chapter/10.1007/978-3-031-04148-8_12</a></td>
+      <td>impl_puboi</td>
+      <td></td>
+      <td></td>
+      <td>artificial</td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>PUBO Importance Benchmark</td>
+      <td>Python / C++</td>
+      <td></td>
+      <td><a href="https://gitlab.com/verel/pubo-importance-benchmark">https://gitlab.com/verel/pubo-importance-benchmark</a></td>
+      <td>A benchmark in which variable importance is tunable, based on the Walsh function</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="gen_randoptgen">
+      <td>gen_randoptgen</td>
+      <td>RandOptGen</td>
+      <td>Generator</td>
+      <td>binary | continuous | integer</td>
+      <td>>=3</td>
+      <td>[1, 10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>no</td>
+      <td></td>
+      <td></td>
+      <td>RandOptGen</td>
+      <td>A Unified Random Problem Generator for Single- and Multi-Objective Optimization Problems with Mixed-Variable Input Spaces.</td>
+      <td></td>
+      <td></td>
+      <td>impl_randoptgen</td>
+      <td>multimodal</td>
+      <td></td>
+      <td>artificial</td>
+      <td>>=1</td>
+      <td></td>
+      <td>>=1</td>
+      <td>>=1</td>
+      <td>RandOptGen</td>
+      <td>Python</td>
+      <td>milliseconds</td>
+      <td><a href="https://github.com/MALEO-research-group/RandOptGen">https://github.com/MALEO-research-group/RandOptGen</a> | <a href="https://doi.org/10.1145/3712256.3726478">https://doi.org/10.1145/3712256.3726478</a></td>
+      <td>Unified Random Problem Generator for Single- and Multi-Objective Optimization with Mixed-Variable Input Spaces</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="gen_rho_mnk_landscapes">
+      <td>gen_rho_mnk_landscapes</td>
+      <td>ρMNK-Landscapes</td>
+      <td>Generator</td>
+      <td>binary</td>
+      <td>>=1</td>
+      <td>[1, 10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>tunable variable and objective dimensions; tunable multimodality and correlation between objectives</td>
+      <td></td>
+      <td>On the design of multi-objective evolutionary algorithms based on NK-landscapes, <a href="https://doi.org/10.1016/j.ejor.2012.12.019">https://doi.org/10.1016/j.ejor.2012.12.019</a></td>
+      <td>impl_mocobench</td>
+      <td>multimodal</td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>mocobench</td>
+      <td>C++</td>
+      <td></td>
+      <td><a href="https://gitlab.com/aliefooghe/mocobench/">https://gitlab.com/aliefooghe/mocobench/</a></td>
+      <td>Multi-objective combinatorial optimization benchmark</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="gen_rho_mtsp">
+      <td>gen_rho_mtsp</td>
+      <td>ρmTSP</td>
+      <td>Generator</td>
+      <td>unknown</td>
+      <td>>=1</td>
+      <td>[1, 10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>tunable variable and objective dimensions; tunable instance type (euclidean/random); tunable correlation between objectives</td>
+      <td></td>
+      <td>On the impact of multi-objective scalability for the ρmTSP, <a href="https://doi.org/10.1007/978-3-319-45823-6_40">https://doi.org/10.1007/978-3-319-45823-6_40</a></td>
+      <td>impl_mocobench</td>
+      <td>["multimodal", "quadratic"]</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>mocobench</td>
+      <td>C++</td>
+      <td></td>
+      <td><a href="https://gitlab.com/aliefooghe/mocobench/">https://gitlab.com/aliefooghe/mocobench/</a></td>
+      <td>Multi-objective combinatorial optimization benchmark</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="gen_wmodel">
+      <td>gen_wmodel</td>
+      <td>W-model</td>
+      <td>Generator</td>
+      <td>binary</td>
+      <td>>=1</td>
+      <td>1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Tunable generator for binary optimization based on several difficulty features</td>
+      <td></td>
+      <td>W-model, <a href="https://dl.acm.org/doi/abs/10.1145/3205651.3208240">https://dl.acm.org/doi/abs/10.1145/3205651.3208240</a></td>
+      <td>impl_wmodel</td>
+      <td></td>
+      <td></td>
+      <td>artificial</td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>BBDOB W-Model</td>
+      <td></td>
+      <td></td>
+      <td><a href="https://github.com/thomasWeise/BBDOB_W_Model">https://github.com/thomasWeise/BBDOB_W_Model</a></td>
+      <td>Tunable generator for binary optimization</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_amvop">
+      <td>suite_amvop</td>
+      <td>AMVOP</td>
+      <td>Suite</td>
+      <td>integer | continuous | categorical</td>
+      <td>>=3</td>
+      <td>1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>AMVOP, <a href="https://doi.org/10.1109/TEVC.2013.2281531">https://doi.org/10.1109/TEVC.2013.2281531</a></td>
+      <td></td>
+      <td>multimodal</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td>>=1</td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_bbob">
+      <td>suite_bbob</td>
+      <td>BBOB</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>COCO: a platform for comparing continuous optimizers in a black-box setting, <a href="https://doi.org/10.1080/10556788.2020.1808977">https://doi.org/10.1080/10556788.2020.1808977</a></td>
+      <td>impl_coco</td>
+      <td>multimodal</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>COCO framework</td>
+      <td>C/Python</td>
+      <td></td>
+      <td><a href="https://github.com/numbbo/coco">https://github.com/numbbo/coco</a></td>
+      <td>Comparing Continuous Optimizers: black-box optimization benchmarking platform</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_bbob_biobj">
+      <td>suite_bbob_biobj</td>
+      <td>BBOB-biobj</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>2-40</td>
+      <td>2</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>BBOB bi-objective test suite, <a href="https://doi.org/10.48550/arXiv.1604.00359">https://doi.org/10.48550/arXiv.1604.00359</a></td>
+      <td>impl_coco</td>
+      <td>multimodal</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>2-40</td>
+      <td></td>
+      <td>COCO framework</td>
+      <td>C/Python</td>
+      <td></td>
+      <td><a href="https://github.com/numbbo/coco">https://github.com/numbbo/coco</a></td>
+      <td>Comparing Continuous Optimizers: black-box optimization benchmarking platform</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_bbob_biobj_mixint">
+      <td>suite_bbob_biobj_mixint</td>
+      <td>BBOB-biobj-mixint</td>
+      <td>Suite</td>
+      <td>continuous | integer</td>
+      <td>10-320</td>
+      <td>2</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>BBOB bi-objective mixed-integer test suite, <a href="https://doi.org/10.1145/3321707.3321868">https://doi.org/10.1145/3321707.3321868</a></td>
+      <td>impl_coco</td>
+      <td>multimodal</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>5-160</td>
+      <td>5-160</td>
+      <td>COCO framework</td>
+      <td>C/Python</td>
+      <td></td>
+      <td><a href="https://github.com/numbbo/coco">https://github.com/numbbo/coco</a></td>
+      <td>Comparing Continuous Optimizers: black-box optimization benchmarking platform</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_bbob_constrained">
+      <td>suite_bbob_constrained</td>
+      <td>BBOB-constrained</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>2-40</td>
+      <td>1</td>
+      <td></td>
+      <td>unknown</td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>bbob-constrained documentation, <a href="http://numbbo.github.io/coco-doc/bbob-constrained/">http://numbbo.github.io/coco-doc/bbob-constrained/</a></td>
+      <td>impl_coco</td>
+      <td>multimodal</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>2-40</td>
+      <td></td>
+      <td>COCO framework</td>
+      <td>C/Python</td>
+      <td></td>
+      <td><a href="https://github.com/numbbo/coco">https://github.com/numbbo/coco</a></td>
+      <td>Comparing Continuous Optimizers: black-box optimization benchmarking platform</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_bbob_largescale">
+      <td>suite_bbob_largescale</td>
+      <td>BBOB-largescale</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>20-640</td>
+      <td>1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>BBOB large-scale test suite, <a href="https://doi.org/10.48550/arXiv.1903.06396">https://doi.org/10.48550/arXiv.1903.06396</a></td>
+      <td>impl_coco</td>
+      <td>multimodal</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>20-640</td>
+      <td></td>
+      <td>COCO framework</td>
+      <td>C/Python</td>
+      <td></td>
+      <td><a href="https://github.com/numbbo/coco">https://github.com/numbbo/coco</a></td>
+      <td>Comparing Continuous Optimizers: black-box optimization benchmarking platform</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_bbob_mixint">
+      <td>suite_bbob_mixint</td>
+      <td>BBOB-mixint</td>
+      <td>Suite</td>
+      <td>continuous | integer</td>
+      <td>10-320</td>
+      <td>1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>BBOB mixed-integer test suite, <a href="https://doi.org/10.1145/3321707.3321868">https://doi.org/10.1145/3321707.3321868</a></td>
+      <td>impl_coco</td>
+      <td>multimodal</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>5-160</td>
+      <td>5-160</td>
+      <td>COCO framework</td>
+      <td>C/Python</td>
+      <td></td>
+      <td><a href="https://github.com/numbbo/coco">https://github.com/numbbo/coco</a></td>
+      <td>Comparing Continuous Optimizers: black-box optimization benchmarking platform</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_bbob_noisy">
+      <td>suite_bbob_noisy</td>
+      <td>BBOB-noisy</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>1</td>
+      <td>noisy</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>noisy</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Real-parameter black-box optimization benchmarking: noisy functions definitions, <a href="https://hal.inria.fr/inria-00369466">https://hal.inria.fr/inria-00369466</a></td>
+      <td>impl_coco_legacy</td>
+      <td>multimodal</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>COCO legacy (bbob-noisy)</td>
+      <td>C/Python</td>
+      <td></td>
+      <td><a href="https://web.archive.org/web/20210416065610/https://coco.gforge.inria.fr/doku.php?id=downloads">https://web.archive.org/web/20210416065610/https://coco.gforge.inria.fr/doku.php?id=downloads</a></td>
+      <td>Archived COCO download page that hosted the bbob-noisy suite</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_bp">
+      <td>suite_bp</td>
+      <td>BP</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>[10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
+      <td>noisy</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>unknown</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>BP benchmark, <a href="https://doi.org/10.1109/CEC.2019.8790277">https://doi.org/10.1109/CEC.2019.8790277</a></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_brachytherapy">
+      <td>suite_brachytherapy</td>
+      <td>Brachytherapy treatment planning</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>100-500</td>
+      <td>[2, 3]</td>
+      <td>multi-fidelity</td>
+      <td>unknown</td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td>yes</td>
+      <td></td>
+      <td>[1, 2]</td>
+      <td>Brachytherapy treatment planning</td>
+      <td>Treatment planning for internal radiation therapy. Multi-objective with aggregated objectives; no public source code.</td>
+      <td></td>
+      <td>Brachytherapy treatment planning, <a href="https://www.sciencedirect.com/science/article/pii/S1538472123016781">https://www.sciencedirect.com/science/article/pii/S1538472123016781</a></td>
+      <td></td>
+      <td>multimodal</td>
+      <td></td>
+      <td>real-world</td>
+      <td></td>
+      <td></td>
+      <td>100-500</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_car_structure">
+      <td>suite_car_structure</td>
+      <td>Car structure</td>
+      <td>Suite</td>
+      <td>integer</td>
+      <td>144-222</td>
+      <td>2</td>
+      <td></td>
+      <td>unknown</td>
+      <td>54</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>54 constraints</td>
+      <td></td>
+      <td>Car structure design benchmark, <a href="https://doi.org/10.1145/3205651.3205702">https://doi.org/10.1145/3205651.3205702</a></td>
+      <td>impl_car_structure</td>
+      <td></td>
+      <td></td>
+      <td>real-world</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>144-222</td>
+      <td>Car-structure benchmark</td>
+      <td></td>
+      <td></td>
+      <td><a href="http://ladse.eng.isas.jaxa.jp/benchmark/">http://ladse.eng.isas.jaxa.jp/benchmark/</a></td>
+      <td>JAXA LADSE benchmark problems</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_cdmp">
+      <td>suite_cdmp</td>
+      <td>CDMP</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>[10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
+      <td>dynamic | noisy</td>
+      <td>unknown</td>
+      <td>>=1</td>
+      <td>unknown</td>
+      <td>unknown</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>CDMP benchmark, <a href="https://doi.org/10.1145/3321707.3321878">https://doi.org/10.1145/3321707.3321878</a></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_cec2013">
+      <td>suite_cec2013</td>
+      <td>CEC2013</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>suite used for cec2013 competition. Also in IOH.</td>
+      <td></td>
+      <td>CEC2013 definitions, <a href="https://peerj.com/articles/cs-2671/CEC2013.pdf">https://peerj.com/articles/cs-2671/CEC2013.pdf</a></td>
+      <td>["impl_cec2013", "impl_iohexperimenter"]</td>
+      <td></td>
+      <td></td>
+      <td>artificial</td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>IOHexperimenter | CEC2013 reference code</td>
+      <td>C++/Python</td>
+      <td></td>
+      <td><a href="https://github.com/IOHprofiler/IOHexperimenter">https://github.com/IOHprofiler/IOHexperimenter</a> | <a href="https://github.com/P-N-Suganthan/CEC2013">https://github.com/P-N-Suganthan/CEC2013</a></td>
+      <td>IOHprofiler experimenter framework | Suganthan's reference implementation</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_cec2015_dmoo">
+      <td>suite_cec2015_dmoo</td>
+      <td>CEC2015-DMOO</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>0</td>
+      <td>[2, 3]</td>
+      <td>dynamic</td>
+      <td>unknown</td>
+      <td>>=1</td>
+      <td>dynamic</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Benchmark Functions for CEC 2015 Special Session and Competition on Dynamic Multi-objective Optimization</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>0</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_cec2018_dt">
+      <td>suite_cec2018_dt</td>
+      <td>CEC2018 DT</td>
+      <td>Suite</td>
+      <td>unknown</td>
+      <td>>=1</td>
+      <td>[2, 3]</td>
+      <td>dynamic</td>
+      <td></td>
+      <td></td>
+      <td>dynamic</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>CEC2018 Competition on Dynamic Multiobjective Optimisation</td>
+      <td>14 problems. Time-dependent: Pareto front/Pareto set geometry; irregular Pareto front shapes; variable-linkage; number of disconnected Pareto front segments; etc.</td>
+      <td></td>
+      <td>CEC2018 DMOP Competition TR, <a href="https://www.academia.edu/download/94499025/TR-CEC2018-DMOP-Competition.pdf">https://www.academia.edu/download/94499025/TR-CEC2018-DMOP-Competition.pdf</a></td>
+      <td>impl_pymoo</td>
+      <td></td>
+      <td></td>
+      <td>artificial</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>pymoo</td>
+      <td>Python</td>
+      <td></td>
+      <td><a href="https://github.com/anyoptimization/pymoo">https://github.com/anyoptimization/pymoo</a></td>
+      <td>Multi-objective optimization in Python</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_cec2022">
+      <td>suite_cec2022</td>
+      <td>CEC2022</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>suite used for cec2022 competition. Also in IOH.</td>
+      <td></td>
+      <td>CEC2022 TR, <a href="https://github.com/P-N-Suganthan/2022-SO-BO/blob/main/CEC2022%20TR.pdf">https://github.com/P-N-Suganthan/2022-SO-BO/blob/main/CEC2022%20TR.pdf</a></td>
+      <td>["impl_cec2022", "impl_iohexperimenter"]</td>
+      <td></td>
+      <td></td>
+      <td>artificial</td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>IOHexperimenter | CEC2022 reference code</td>
+      <td>C++/Python</td>
+      <td></td>
+      <td><a href="https://github.com/IOHprofiler/IOHexperimenter">https://github.com/IOHprofiler/IOHexperimenter</a> | <a href="https://github.com/P-N-Suganthan/2022-SO-BO">https://github.com/P-N-Suganthan/2022-SO-BO</a></td>
+      <td>IOHprofiler experimenter framework | Suganthan's reference implementation</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_cfd">
+      <td>suite_cfd</td>
+      <td>CFD</td>
+      <td>Suite</td>
+      <td>unknown</td>
+      <td>>=1</td>
+      <td>[1, 2]</td>
+      <td></td>
+      <td>unknown</td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>expensive evaluations 30s-15m</td>
+      <td></td>
+      <td>CFD test problem suite, <a href="https://doi.org/10.1007/978-3-319-99259-4_24">https://doi.org/10.1007/978-3-319-99259-4_24</a></td>
+      <td>impl_cfd</td>
+      <td></td>
+      <td></td>
+      <td>real-world</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>CFD test problem suite</td>
+      <td></td>
+      <td>30s-15m</td>
+      <td><a href="https://bitbucket.org/arahat/cfd-test-problem-suite">https://bitbucket.org/arahat/cfd-test-problem-suite</a></td>
+      <td>Expensive real-world CFD-based test problems</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_cre">
+      <td>suite_cre</td>
+      <td>CRE</td>
+      <td>Suite</td>
+      <td>integer | continuous</td>
+      <td>6-14</td>
+      <td>[2, 3, 4, 5]</td>
+      <td></td>
+      <td>unknown</td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Easy-to-evaluate real-world multi-objective optimization problems, Ryoji Tanabe; Hisao Ishibuchi, <a href="https://doi.org/10.1016/j.asoc.2020.106078">https://doi.org/10.1016/j.asoc.2020.106078</a></td>
+      <td>impl_reproblems</td>
+      <td></td>
+      <td></td>
+      <td>real-world-like</td>
+      <td></td>
+      <td></td>
+      <td>3-7</td>
+      <td>3-7</td>
+      <td>reproblems</td>
+      <td>Python</td>
+      <td></td>
+      <td><a href="https://github.com/ryojitanabe/reproblems">https://github.com/ryojitanabe/reproblems</a></td>
+      <td>Real-world inspired multi-objective optimization problem suite</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_cuter">
+      <td>suite_cuter</td>
+      <td>CUTEr</td>
+      <td>Suite</td>
+      <td>binary | continuous | integer</td>
+      <td>>=3</td>
+      <td>1</td>
+      <td></td>
+      <td>unknown</td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td>no</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>A constrained and unconstrained testing environment.</td>
+      <td></td>
+      <td>CUTEr, <a href="https://dl.acm.org/doi/10.1145/962437.962439">https://dl.acm.org/doi/10.1145/962437.962439</a></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>artificial</td>
+      <td>>=1</td>
+      <td></td>
+      <td>>=1</td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_cutest">
+      <td>suite_cutest</td>
+      <td>CUTEst</td>
+      <td>Suite</td>
+      <td>binary | continuous | integer</td>
+      <td>>=3</td>
+      <td>1</td>
+      <td></td>
+      <td>unknown | box</td>
+      <td>>=2</td>
+      <td></td>
+      <td></td>
+      <td>no</td>
+      <td></td>
+      <td></td>
+      <td>Constrained and Unconstrained Testing Environment with safe threads</td>
+      <td>CUTEst for optimization software</td>
+      <td></td>
+      <td>CUTEst, <a href="https://link.springer.com/article/10.1007/s10589-014-9687-3">https://link.springer.com/article/10.1007/s10589-014-9687-3</a></td>
+      <td>impl_pycutest</td>
+      <td>multimodal</td>
+      <td></td>
+      <td>artificial</td>
+      <td>>=1</td>
+      <td></td>
+      <td>>=1</td>
+      <td>>=1</td>
+      <td>pycutest</td>
+      <td>Python / C++ / Fortran</td>
+      <td></td>
+      <td><a href="https://github.com/jfowkes/pycutest">https://github.com/jfowkes/pycutest</a></td>
+      <td>Python interface to CUTEst</td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_dtlz">
+      <td>suite_dtlz</td>
+      <td>DTLZ</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>[10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Scalable multi-objective optimization test problems, Kalyanmoy Deb; Lothar Thiele; Marco Laumanns; Eckart Zitzler, <a href="https://doi.org/10.1109/CEC.2002.1007032">https://doi.org/10.1109/CEC.2002.1007032</a></td>
+      <td>impl_pymoo</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>pymoo</td>
+      <td>Python</td>
+      <td></td>
+      <td><a href="https://github.com/anyoptimization/pymoo">https://github.com/anyoptimization/pymoo</a></td>
+      <td>Multi-objective optimization in Python</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_dynamicbinval">
+      <td>suite_dynamicbinval</td>
+      <td>DynamicBinVal</td>
+      <td>Suite</td>
+      <td>binary</td>
+      <td>>=1</td>
+      <td>1</td>
+      <td>dynamic</td>
+      <td></td>
+      <td></td>
+      <td>dynamic</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Four versions of the dynamic binary value problem</td>
+      <td></td>
+      <td>DynamicBinVal, <a href="https://arxiv.org/pdf/2404.15837">https://arxiv.org/pdf/2404.15837</a></td>
+      <td>impl_iohexperimenter</td>
+      <td></td>
+      <td></td>
+      <td>artificial</td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>IOHexperimenter</td>
+      <td>C++/Python</td>
+      <td></td>
+      <td><a href="https://github.com/IOHprofiler/IOHexperimenter">https://github.com/IOHprofiler/IOHexperimenter</a></td>
+      <td>IOHprofiler experimenter framework</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_emo2017">
+      <td>suite_emo2017</td>
+      <td>EMO2017</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>4-24</td>
+      <td>2</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>BBComp EMO 2017, <a href="https://www.ini.rub.de/PEOPLE/glasmtbl/projects/bbcomp/">https://www.ini.rub.de/PEOPLE/glasmtbl/projects/bbcomp/</a></td>
+      <td>impl_emo2017</td>
+      <td></td>
+      <td></td>
+      <td>real-world</td>
+      <td></td>
+      <td></td>
+      <td>4-24</td>
+      <td></td>
+      <td>EMO 2017 real-world problems</td>
+      <td></td>
+      <td></td>
+      <td><a href="https://www.ini.rub.de/PEOPLE/glasmtbl/projects/bbcomp/downloads/realworld-problems-bbcomp-EMO-2017.zip">https://www.ini.rub.de/PEOPLE/glasmtbl/projects/bbcomp/downloads/realworld-problems-bbcomp-EMO-2017.zip</a></td>
+      <td>BBComp EMO-2017 real-world problem archive</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_etmof">
+      <td>suite_etmof</td>
+      <td>ETMOF</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>25-10000</td>
+      <td>[10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 2, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 3, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 4, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 5, 50, 6, 7, 8, 9]</td>
+      <td>dynamic</td>
+      <td></td>
+      <td></td>
+      <td>dynamic</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Evolutionary many-task optimization framework, <a href="https://doi.org/10.48550/arXiv.2110.08033">https://doi.org/10.48550/arXiv.2110.08033</a></td>
+      <td>impl_etmof</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>25-10000</td>
+      <td></td>
+      <td>ETMOF</td>
+      <td></td>
+      <td></td>
+      <td><a href="https://github.com/songbai-liu/etmo">https://github.com/songbai-liu/etmo</a></td>
+      <td>Evolutionary many-task optimization framework</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_expobench">
+      <td>suite_expobench</td>
+      <td>EXPObench</td>
+      <td>Suite</td>
+      <td>categorical | continuous | integer</td>
+      <td>30-405</td>
+      <td>1</td>
+      <td>noisy</td>
+      <td>unknown | box</td>
+      <td>>=2</td>
+      <td></td>
+      <td>["observational", "real-life"]</td>
+      <td>no</td>
+      <td></td>
+      <td></td>
+      <td>EXPensive Optimization benchmark library</td>
+      <td>Wind farm layout optimization, gas filter design, pipe shape optimization, hyperparameter tuning, and hospital simulation</td>
+      <td></td>
+      <td>EXPObench, <a href="https://doi.org/10.1016/j.asoc.2023.110744">https://doi.org/10.1016/j.asoc.2023.110744</a></td>
+      <td>impl_expobench</td>
+      <td></td>
+      <td></td>
+      <td>real-world</td>
+      <td></td>
+      <td>10-135</td>
+      <td>10-135</td>
+      <td>10-135</td>
+      <td>EXPObench</td>
+      <td>Python</td>
+      <td>2 to 80 seconds</td>
+      <td><a href="https://github.com/AlgTUDelft/ExpensiveOptimBenchmark">https://github.com/AlgTUDelft/ExpensiveOptimBenchmark</a></td>
+      <td>EXPensive Optimization benchmark library (wind farm layout, gas filter design, pipe shape, hyperparameter tuning, hospital simulation)</td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_gbea">
+      <td>suite_gbea</td>
+      <td>GBEA</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>[1, 2]</td>
+      <td>noisy</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>noisy</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>expensive evaluations 5s-35s, RW-GAN-Mario and TopTrumps are part of GBEA</td>
+      <td></td>
+      <td>Game benchmark for evolutionary algorithms, <a href="https://doi.org/10.1145/3321707.3321805">https://doi.org/10.1145/3321707.3321805</a></td>
+      <td>impl_gbea</td>
+      <td>multimodal</td>
+      <td></td>
+      <td>real-world</td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>coco-gbea</td>
+      <td></td>
+      <td>5s-35s</td>
+      <td><a href="https://github.com/ttusar/coco-gbea">https://github.com/ttusar/coco-gbea</a></td>
+      <td>Game-Benchmark for Evolutionary Algorithms (COCO fork)</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_gnbg">
+      <td>suite_gnbg</td>
+      <td>GNBG</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Generalized Numerical Benchmark Generator</td>
+      <td></td>
+      <td>GNBG, <a href="https://arxiv.org/abs/2312.07083">https://arxiv.org/abs/2312.07083</a></td>
+      <td>impl_gnbg</td>
+      <td></td>
+      <td></td>
+      <td>artificial</td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>GNBG Generator</td>
+      <td></td>
+      <td></td>
+      <td><a href="https://github.com/Danial-Yazdani/GNBG-Generator">https://github.com/Danial-Yazdani/GNBG-Generator</a></td>
+      <td>Generalized Numerical Benchmark Generator</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_gnbg_ii">
+      <td>suite_gnbg_ii</td>
+      <td>GNBG-II</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Generalized Numerical Benchmark Generator (version 2). Also available in IOH.</td>
+      <td></td>
+      <td>GNBG-II, <a href="https://dl.acm.org/doi/pdf/10.1145/3712255.3734271">https://dl.acm.org/doi/pdf/10.1145/3712255.3734271</a></td>
+      <td>["impl_gnbg_ii", "impl_iohgnbg"]</td>
+      <td></td>
+      <td></td>
+      <td>artificial</td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>GNBG-II | IOHGNBG</td>
+      <td></td>
+      <td></td>
+      <td><a href="https://github.com/rohitsalgotra/GNBG-II">https://github.com/rohitsalgotra/GNBG-II</a> | <a href="https://github.com/IOHprofiler/IOHGNBG">https://github.com/IOHprofiler/IOHGNBG</a></td>
+      <td>Generalized Numerical Benchmark Generator version 2 | IOHprofiler version of GNBG</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_iohclustering">
+      <td>suite_iohclustering</td>
+      <td>IOHClustering</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Set of benchmark problems from clustering: optimization task is selecting cluster centers for a given set of data.</td>
+      <td></td>
+      <td>IOHClustering, <a href="https://arxiv.org/pdf/2505.09233">https://arxiv.org/pdf/2505.09233</a></td>
+      <td>impl_iohclustering</td>
+      <td>multimodal</td>
+      <td></td>
+      <td>artificial-from-real-data</td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>IOHClustering</td>
+      <td></td>
+      <td></td>
+      <td><a href="https://github.com/IOHprofiler/IOHClustering">https://github.com/IOHprofiler/IOHClustering</a></td>
+      <td>Clustering-based optimization benchmark built on ML datasets</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_kinematics_robotarm">
+      <td>suite_kinematics_robotarm</td>
+      <td>KinematicsRobotArm</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>21</td>
+      <td>1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Kinematics of a robot arm, <a href="https://doi.org/10.1023/A:1013258808932">https://doi.org/10.1023/A:1013258808932</a></td>
+      <td>impl_transfer_rf_bbob_rw</td>
+      <td>unimodal</td>
+      <td></td>
+      <td>real-world</td>
+      <td></td>
+      <td></td>
+      <td>21</td>
+      <td></td>
+      <td>Transfer Random Forests BBOB Real-world</td>
+      <td></td>
+      <td></td>
+      <td><a href="https://github.com/ShuaiqunPan/Transfer_Random_forests_BBOB_Real_world">https://github.com/ShuaiqunPan/Transfer_Random_forests_BBOB_Real_world</a></td>
+      <td>Real-world BBOB-like problem implementations (Porkchop, KinematicsRobotArm)</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_l1_zdt">
+      <td>suite_l1_zdt</td>
+      <td>L1-ZDT</td>
+      <td>Suite</td>
+      <td>binary | continuous</td>
+      <td>>=2</td>
+      <td>2</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Variant of ZDT with linkages between variables within groups</td>
+      <td></td>
+      <td>Linkage ZDT/DTLZ variants, <a href="https://doi.org/10.1145/1143997.1144179">https://doi.org/10.1145/1143997.1144179</a></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_l2_dtlz">
+      <td>suite_l2_dtlz</td>
+      <td>L2-DTLZ</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>[10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Variant of DTLZ2/DTLZ3 with linkages between all variables</td>
+      <td></td>
+      <td>Linkage ZDT/DTLZ variants, <a href="https://doi.org/10.1145/1143997.1144179">https://doi.org/10.1145/1143997.1144179</a></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_l2_zdt">
+      <td>suite_l2_zdt</td>
+      <td>L2-ZDT</td>
+      <td>Suite</td>
+      <td>binary | continuous</td>
+      <td>>=2</td>
+      <td>2</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Variant of ZDT with linkages between all variables</td>
+      <td></td>
+      <td>Linkage ZDT/DTLZ variants, <a href="https://doi.org/10.1145/1143997.1144179">https://doi.org/10.1145/1143997.1144179</a></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_l3_dtlz">
+      <td>suite_l3_dtlz</td>
+      <td>L3-DTLZ</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>[10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Variant of L2-DTLZ with anti-linkage mapping</td>
+      <td></td>
+      <td>Linkage ZDT/DTLZ variants, <a href="https://doi.org/10.1145/1143997.1144179">https://doi.org/10.1145/1143997.1144179</a></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_l3_zdt">
+      <td>suite_l3_zdt</td>
+      <td>L3-ZDT</td>
+      <td>Suite</td>
+      <td>binary | continuous</td>
+      <td>>=2</td>
+      <td>2</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Variant of L2-ZDT with anti-linkage mapping</td>
+      <td></td>
+      <td>Linkage ZDT/DTLZ variants, <a href="https://doi.org/10.1145/1143997.1144179">https://doi.org/10.1145/1143997.1144179</a></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_maop">
+      <td>suite_maop</td>
+      <td>MaOP</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>[10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
+      <td>noisy</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>unknown</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>MaOP benchmark, <a href="https://doi.org/10.1016/j.swevo.2019.02.003">https://doi.org/10.1016/j.swevo.2019.02.003</a></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_mechbench">
+      <td>suite_mechbench</td>
+      <td>MECHBench</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>1</td>
+      <td></td>
+      <td>unknown</td>
+      <td>{1, 2}</td>
+      <td></td>
+      <td></td>
+      <td>no</td>
+      <td></td>
+      <td></td>
+      <td>MECHBench</td>
+      <td>Set of problems inspired by Structural Mechanics Design Optimization. Embeds physical simulations (plasticity only, no fracture/damage). Unstructured/non-isotropic multimodality.</td>
+      <td></td>
+      <td>MECHBench, <a href="https://arxiv.org/abs/2511.10821">https://arxiv.org/abs/2511.10821</a></td>
+      <td>impl_mechbench</td>
+      <td>multimodal</td>
+      <td></td>
+      <td>real-world</td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>MECHBench</td>
+      <td>Python</td>
+      <td>1-7 minutes</td>
+      <td><a href="https://github.com/BayesOptApp/MECHBench">https://github.com/BayesOptApp/MECHBench</a></td>
+      <td>Structural mechanics design optimization benchmark</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_mf2">
+      <td>suite_mf2</td>
+      <td>MF2</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>1</td>
+      <td>multi-fidelity</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>[1, 2]</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>mf2: a collection of multi-fidelity benchmark functions in Python, <a href="https://doi.org/10.21105/joss.02049">https://doi.org/10.21105/joss.02049</a></td>
+      <td>impl_mf2</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>mf2</td>
+      <td>Python</td>
+      <td></td>
+      <td><a href="https://github.com/sjvrijn/mf2">https://github.com/sjvrijn/mf2</a></td>
+      <td>Multi-fidelity test function collection</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_minus_dtlz">
+      <td>suite_minus_dtlz</td>
+      <td>Minus DTLZ</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>[10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Variant of DTLZ that minimises the inverse of the base DTLZ functions</td>
+      <td></td>
+      <td>Minus DTLZ / Minus WFG, <a href="https://doi.org/10.1109/TEVC.2016.2587749">https://doi.org/10.1109/TEVC.2016.2587749</a></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_minus_wfg">
+      <td>suite_minus_wfg</td>
+      <td>Minus WFG</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>[10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Variant of WFG that minimises the inverse of the base WFG functions</td>
+      <td></td>
+      <td>Minus DTLZ / Minus WFG, <a href="https://doi.org/10.1109/TEVC.2016.2587749">https://doi.org/10.1109/TEVC.2016.2587749</a></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_mmopp">
+      <td>suite_mmopp</td>
+      <td>MMOPP</td>
+      <td>Suite</td>
+      <td>unknown</td>
+      <td>0</td>
+      <td>[2, 3, 4, 5, 6, 7]</td>
+      <td></td>
+      <td>unknown</td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>MMOPP technical report, <a href="http://www5.zzu.edu.cn/system/_content/download.jsp?urltype=news.DownloadAttachUrl&owner=1327567121&wbfileid=4764412">http://www5.zzu.edu.cn/system/_content/download.jsp?urltype=news.DownloadAttachUrl&owner=1327567121&wbfileid=4764412</a></td>
+      <td>impl_mmopp</td>
+      <td>multimodal</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>MMOPP</td>
+      <td></td>
+      <td></td>
+      <td><a href="http://www5.zzu.edu.cn/ecilab/info/1036/1251.htm">http://www5.zzu.edu.cn/ecilab/info/1036/1251.htm</a></td>
+      <td>ECI lab distribution page for MMOPP</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_modact">
+      <td>suite_modact</td>
+      <td>MODAct</td>
+      <td>Suite</td>
+      <td>continuous | integer</td>
+      <td>40</td>
+      <td>[2, 3, 4, 5]</td>
+      <td></td>
+      <td>unknown</td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>multiobjective design of actuators</td>
+      <td>Realistic Constrained Multi-Objective Optimization Benchmark Problems from Design.</td>
+      <td></td>
+      <td>MODAct, <a href="https://doi.org/10.1109/TEVC.2020.3020046">https://doi.org/10.1109/TEVC.2020.3020046</a></td>
+      <td>["impl_modact", "impl_pymoo"]</td>
+      <td></td>
+      <td></td>
+      <td>real-world</td>
+      <td></td>
+      <td></td>
+      <td>20</td>
+      <td>20</td>
+      <td>modact | pymoo</td>
+      <td>Python</td>
+      <td>20ms</td>
+      <td><a href="https://github.com/epfl-lamd/modact">https://github.com/epfl-lamd/modact</a> | <a href="https://github.com/anyoptimization/pymoo">https://github.com/anyoptimization/pymoo</a></td>
+      <td>EPFL-LAMD modact package | Multi-objective optimization in Python</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_morepo">
+      <td>suite_morepo</td>
+      <td>MOrepo</td>
+      <td>Suite</td>
+      <td>unknown</td>
+      <td>0</td>
+      <td>2</td>
+      <td>dynamic | noisy</td>
+      <td>unknown</td>
+      <td>>=1</td>
+      <td>unknown</td>
+      <td>unknown</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>impl_morepo</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>MOrepo</td>
+      <td></td>
+      <td></td>
+      <td><a href="https://github.com/MCDMSociety/MOrepo">https://github.com/MCDMSociety/MOrepo</a></td>
+      <td>Multi-objective optimisation problem repository</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_pbo">
+      <td>suite_pbo</td>
+      <td>PBO</td>
+      <td>Suite</td>
+      <td>binary</td>
+      <td>>=1</td>
+      <td>1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Suite of 25 binary optimization problems</td>
+      <td></td>
+      <td>PBO benchmarks, <a href="https://dl.acm.org/doi/pdf/10.1145/3319619.3326810">https://dl.acm.org/doi/pdf/10.1145/3319619.3326810</a></td>
+      <td>impl_iohexperimenter</td>
+      <td></td>
+      <td></td>
+      <td>artificial</td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>IOHexperimenter</td>
+      <td>C++/Python</td>
+      <td></td>
+      <td><a href="https://github.com/IOHprofiler/IOHexperimenter">https://github.com/IOHprofiler/IOHexperimenter</a></td>
+      <td>IOHprofiler experimenter framework</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_porkchop">
+      <td>suite_porkchop</td>
+      <td>PorkchopPlotInterplanetaryTrajectory</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>2</td>
+      <td>1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Porkchop plot interplanetary trajectory benchmark, <a href="https://doi.org/10.1109/CEC65147.2025.11042973">https://doi.org/10.1109/CEC65147.2025.11042973</a></td>
+      <td>impl_transfer_rf_bbob_rw</td>
+      <td>multimodal</td>
+      <td></td>
+      <td>real-world</td>
+      <td></td>
+      <td></td>
+      <td>2</td>
+      <td></td>
+      <td>Transfer Random Forests BBOB Real-world</td>
+      <td></td>
+      <td></td>
+      <td><a href="https://github.com/ShuaiqunPan/Transfer_Random_forests_BBOB_Real_world">https://github.com/ShuaiqunPan/Transfer_Random_forests_BBOB_Real_world</a></td>
+      <td>Real-world BBOB-like problem implementations (Porkchop, KinematicsRobotArm)</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_re">
+      <td>suite_re</td>
+      <td>RE</td>
+      <td>Suite</td>
+      <td>continuous | integer</td>
+      <td>4-14</td>
+      <td>[2, 3, 4, 5, 6, 7, 8, 9]</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Easy-to-evaluate real-world multi-objective optimization problems, Ryoji Tanabe; Hisao Ishibuchi, <a href="https://doi.org/10.1016/j.asoc.2020.106078">https://doi.org/10.1016/j.asoc.2020.106078</a></td>
+      <td>impl_reproblems</td>
+      <td></td>
+      <td></td>
+      <td>real-world-like</td>
+      <td></td>
+      <td></td>
+      <td>2-7</td>
+      <td>2-7</td>
+      <td>reproblems</td>
+      <td>Python</td>
+      <td></td>
+      <td><a href="https://github.com/ryojitanabe/reproblems">https://github.com/ryojitanabe/reproblems</a></td>
+      <td>Real-world inspired multi-objective optimization problem suite</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_rwmvop">
+      <td>suite_rwmvop</td>
+      <td>RWMVOP</td>
+      <td>Suite</td>
+      <td>integer | continuous | categorical</td>
+      <td>>=3</td>
+      <td>1</td>
+      <td></td>
+      <td>unknown</td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>RWMVOP, <a href="https://doi.org/10.1109/TEVC.2013.2281531">https://doi.org/10.1109/TEVC.2013.2281531</a></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>real-world</td>
+      <td></td>
+      <td>>=1</td>
+      <td>>=1</td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_sbox_cost">
+      <td>suite_sbox_cost</td>
+      <td>SBOX-COST</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>problems from BBOB but allows instances with the optimum close to the boundary</td>
+      <td></td>
+      <td>SBOX-COST, <a href="https://doi.org/10.48550/arXiv.2305.12221">https://doi.org/10.48550/arXiv.2305.12221</a></td>
+      <td>impl_iohexperimenter</td>
+      <td>multimodal</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>IOHexperimenter</td>
+      <td>C++/Python</td>
+      <td></td>
+      <td><a href="https://github.com/IOHprofiler/IOHexperimenter">https://github.com/IOHprofiler/IOHexperimenter</a></td>
+      <td>IOHprofiler experimenter framework</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_sdp">
+      <td>suite_sdp</td>
+      <td>SDP</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>[10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
+      <td>dynamic | noisy</td>
+      <td></td>
+      <td></td>
+      <td>dynamic</td>
+      <td>unknown</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>SDP dynamic multi-objective benchmark, <a href="https://doi.org/10.1109/TCYB.2019.2896021">https://doi.org/10.1109/TCYB.2019.2896021</a></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_submodular">
+      <td>suite_submodular</td>
+      <td>Submodular Optimization</td>
+      <td>Suite</td>
+      <td>binary</td>
+      <td>>=1</td>
+      <td>1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>set of graph-based submodular optimization problems from 4 problem types</td>
+      <td></td>
+      <td>Submodular optimization benchmark, <a href="https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10254181">https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10254181</a></td>
+      <td>impl_iohexperimenter</td>
+      <td></td>
+      <td></td>
+      <td>artificial</td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>IOHexperimenter</td>
+      <td>C++/Python</td>
+      <td></td>
+      <td><a href="https://github.com/IOHprofiler/IOHexperimenter">https://github.com/IOHprofiler/IOHexperimenter</a></td>
+      <td>IOHprofiler experimenter framework</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_tulipa_energy">
+      <td>suite_tulipa_energy</td>
+      <td>TulipaEnergy</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>1</td>
+      <td>noisy | multi-fidelity</td>
+      <td>unknown</td>
+      <td>>=2</td>
+      <td></td>
+      <td>parameter</td>
+      <td></td>
+      <td></td>
+      <td>[1, 2]</td>
+      <td>TulipaEnergyModel.jl</td>
+      <td>Determine the optimal investment and operation decisions for different assets in the energy system (production, consumption, conversion, storage, transport) while minimizing loss of load. Modelled as a potentially very large linear program with multiple fidelity levels.</td>
+      <td></td>
+      <td>TulipaEnergyModel.jl scientific references, <a href="https://tulipaenergy.github.io/TulipaEnergyModel.jl/stable/40-scientific-foundation/45-scientific-references">https://tulipaenergy.github.io/TulipaEnergyModel.jl/stable/40-scientific-foundation/45-scientific-references</a></td>
+      <td>impl_tulipa</td>
+      <td>unimodal</td>
+      <td></td>
+      <td>real-world</td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>TulipaEnergyModel.jl</td>
+      <td>Julia / JuMP</td>
+      <td>minutes to hours</td>
+      <td><a href="https://tulipaenergy.github.io/TulipaEnergyModel.jl/stable/">https://tulipaenergy.github.io/TulipaEnergyModel.jl/stable/</a> | <a href="https://github.com/TulipaEnergy/Tulipa-OBZ-CaseStudy">https://github.com/TulipaEnergy/Tulipa-OBZ-CaseStudy</a></td>
+      <td>Large linear program for optimal investment and operation of energy systems</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_vehicle_dynamics">
+      <td>suite_vehicle_dynamics</td>
+      <td>VehicleDynamics</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>2</td>
+      <td>1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>VehicleDynamics benchmark, <a href="https://www.scitepress.org/Papers/2023/121580/121580.pdf">https://www.scitepress.org/Papers/2023/121580/121580.pdf</a></td>
+      <td>impl_vehicle_dynamics</td>
+      <td>multimodal</td>
+      <td></td>
+      <td>real-world</td>
+      <td></td>
+      <td></td>
+      <td>2</td>
+      <td></td>
+      <td>VehicleDynamics (Zenodo)</td>
+      <td></td>
+      <td></td>
+      <td><a href="https://zenodo.org/records/8307853">https://zenodo.org/records/8307853</a></td>
+      <td>Zenodo archive for the vehicle dynamics benchmark</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_wfg">
+      <td>suite_wfg</td>
+      <td>WFG</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>[10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>A review of multiobjective test problems and a scalable test problem toolkit, Simon Huband; Philip Hingston; Luigi Barone; Lyndon While, <a href="https://doi.org/10.1109/TEVC.2005.861417">https://doi.org/10.1109/TEVC.2005.861417</a></td>
+      <td>impl_pymoo</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>pymoo</td>
+      <td>Python</td>
+      <td></td>
+      <td><a href="https://github.com/anyoptimization/pymoo">https://github.com/anyoptimization/pymoo</a></td>
+      <td>Multi-objective optimization in Python</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_zdt">
+      <td>suite_zdt</td>
+      <td>ZDT</td>
+      <td>Suite</td>
+      <td>binary | continuous</td>
+      <td>>=2</td>
+      <td>2</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Comparison of multiobjective evolutionary algorithms: empirical results, Eckart Zitzler; Kalyanmoy Deb; Lothar Thiele, <a href="https://doi.org/10.1162/106365600568202">https://doi.org/10.1162/106365600568202</a></td>
+      <td>impl_pymoo</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>pymoo</td>
+      <td>Python</td>
+      <td></td>
+      <td><a href="https://github.com/anyoptimization/pymoo">https://github.com/anyoptimization/pymoo</a></td>
+      <td>Multi-objective optimization in Python</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+  </tbody>
+<tfoot><tr><th>ID</th> <th>Name</th> <th>Type</th> <th>Variable Types</th> <th>Total Variables</th> <th>Objectives</th> <th>Properties</th> <th>Constraint Types</th> <th>Total Constraints</th> <th>Dynamics</th> <th>Noise</th> <th>Partial Evaluations</th> <th>Independent Objectives</th> <th>Fidelity Levels</th> <th>Full Name</th> <th>Description</th> <th>Tags</th> <th>References</th> <th>Implementations</th> <th>Modality</th> <th>Examples</th> <th>Source</th> <th>Binary Vars</th> <th>Categorical Vars</th> <th>Continuous Vars</th> <th>Integer Vars</th> <th>Implementation Names</th> <th>Implementation Languages</th> <th>Implementation Evaluation Times</th> <th>Implementation Links</th> <th>Implementation Descriptions</th> <th>Implementation Requirements</th> <th>Hard Box Constraints</th> <th>Soft Box Constraints</th> <th>Hard Linear Constraints</th> <th>Soft Linear Constraints</th> <th>Hard Function Constraints</th> <th>Soft Function Constraints</th></tr></tfoot></table>
+    </div>
+</section>
+
+<section class="details-shell" id="problem-details" aria-live="polite">
+    <h3 class="details-title">Problem details</h3>
+    <p class="details-hint" id="problem-details-hint">Click a table row to inspect full details.</p>
+    <dl class="details-grid" id="problem-details-content"></dl>
+    <div class="snippet-shell" id="problem-snippet-shell">
+        <h4 class="snippet-title">README.md</h4>
+        <p class="snippet-hint" id="problem-readme-hint">README content for this snippet folder will appear here.</p>
+        <div class="snippet-readme markdown-body" id="problem-readme-content"></div>
+        <h4 class="snippet-title">Snippet: <code id="problem-snippet-path">call_{problem_id}.py</code></h4>
+        <p class="snippet-meta"><a id="problem-snippet-folder-link" href="#" target="_blank" rel="noopener noreferrer">Open snippet folder</a></p>
+        <p class="snippet-hint" id="problem-snippet-hint">Select a problem to check for an available code snippet.</p>
+        <pre class="snippet-code"><code id="problem-snippet-content"></code></pre>
+    </div>
+</section>
diff --git a/docs/table_styles.css b/docs/table_styles.css
index 6d3a9fd..d7cf374 100644
--- a/docs/table_styles.css
+++ b/docs/table_styles.css
@@ -53,6 +53,34 @@ h2 {
     color: var(--opl-muted);
 }
 
+.column-toolbar-disclosure {
+    display: grid;
+    gap: 0.65rem;
+}
+
+.column-toolbar-disclosure > summary {
+    cursor: pointer;
+    list-style: none;
+}
+
+.column-toolbar-disclosure > summary::-webkit-details-marker {
+    display: none;
+}
+
+.column-toolbar-disclosure > summary::after {
+    content: "Show";
+    margin-left: 0.5rem;
+    font-size: 0.72rem;
+    font-weight: 600;
+    letter-spacing: 0.04em;
+    text-transform: none;
+    color: #355670;
+}
+
+.column-toolbar-disclosure[open] > summary::after {
+    content: "Hide";
+}
+
 .toolbar-actions {
     display: flex;
     flex-wrap: wrap;
diff --git a/docs/table_template.html b/docs/table_template.html
index db77aac..3e7a13b 100644
--- a/docs/table_template.html
+++ b/docs/table_template.html
@@ -1,13 +1,15 @@
 <section class="table-shell">
     <div class="table-toolbar">
-        <div class="toolbar-title">Visible columns</div>
-        <div class="toolbar-actions">
-            <button type="button" class="toolbar-btn" id="show-all-columns">Show all</button>
-            <button type="button" class="toolbar-btn" id="hide-all-columns">Hide all</button>
-        </div>
-        <div class="column-controls">
-            __COLUMN_TOGGLES__
-        </div>
+        <details class="column-toolbar-disclosure">
+            <summary class="toolbar-title">Visible Columns</summary>
+            <div class="toolbar-actions">
+                <button type="button" class="toolbar-btn" id="show-all-columns">Show all</button>
+                <button type="button" class="toolbar-btn" id="hide-all-columns">Hide all</button>
+            </div>
+            <div class="column-controls">
+                __COLUMN_TOGGLES__
+            </div>
+        </details>
     </div>
     <div class="table-wrap">
         __TABLE__
diff --git a/yaml_to_html.py b/yaml_to_html.py
index 639f00b..7ee5a6e 100755
--- a/yaml_to_html.py
+++ b/yaml_to_html.py
@@ -1,21 +1,83 @@
 #!/usr/bin/env python3
 
-import pandas as pd
-import yaml
+import json
+import re
 import shutil
 from html import escape
 
-import re
+import pandas as pd
+from pydantic_yaml import parse_yaml_raw_as
+
+from opltools.schema import ConstraintType, Implementation, Library, ProblemLike
 
 URL_RE = re.compile(r'\b((?:https?://|www\.)[^\s<>"\']+)', re.IGNORECASE)
+VISIBLE_VARIABLE_TYPES = ["binary", "categorical", "continuous", "integer"]
+VARIABLE_COLUMN_NAME_BY_TYPE = {
+    "binary": "binary Variables",
+    "categorical": "categorical Variables",
+    "continuous": "continuous Variables",
+    "integer": "integer Variables",
+}
+COLUMN_DISPLAY_NAMES = {
+    "problem_id": "ID",
+    "long_name": "Full Name",
+    "description": "Description",
+    "tags": "Tags",
+    "references": "References",
+    "implementations": "Implementations",
+    "dynamic_type": "Dynamics",
+    "noise_type": "Noise",
+    "allows_partial_evaluation": "Partial Evaluations",
+    "can_evaluate_objectives_independently": "Independent Objectives",
+    "modality": "Modality",
+    "fidelity_levels": "Fidelity Levels",
+    "code_examples": "Examples",
+    "source": "Source",
+    "binary Variables": "Binary Vars",
+    "categorical Variables": "Categorical Vars",
+    "continuous Variables": "Continuous Vars",
+    "integer Variables": "Integer Vars",
+    "Total Variables": "Total Variables",
+    "Hard box Constraints": "Hard Box Constraints",
+    "Soft box Constraints": "Soft Box Constraints",
+    "Hard linear Constraints": "Hard Linear Constraints",
+    "Soft linear Constraints": "Soft Linear Constraints",
+    "Hard function Constraints": "Hard Function Constraints",
+    "Soft function Constraints": "Soft Function Constraints",
+    "Total Constraints": "Total Constraints",
+    "Variable Types": "Variable Types",
+    "Constraint Types": "Constraint Types",
+    "Properties": "Properties",
+    "Implementation Names": "Implementation Names",
+    "Implementation Languages": "Implementation Languages",
+    "Implementation Evaluation Times": "Implementation Evaluation Times",
+    "Implementation Links": "Implementation Links",
+    "Implementation Descriptions": "Implementation Descriptions",
+    "Implementation Requirements": "Implementation Requirements",
+}
+DEFAULT_VISIBLE_COLUMNS = {
+    "Name",
+    "Type",
+    "Objectives",
+    "Dynamics",
+    "Noise",
+    "Partial Evaluations",
+    "Independent Objectives",
+    "Fidelity Levels",
+    "Variable Types",
+    "Constraint Types",
+    "Properties",
+    "Total Variables",
+    "Total Constraints",
+}
 
 
 def linkify_cell(value):
     if not isinstance(value, str):
         return value
 
-    def repl(m):
-        url = m.group(1)
+    def repl(match):
+        url = match.group(1)
         href = url if url.lower().startswith(("http://", "https://")) else f"https://{url}"
         return f'<a href="{href}">{url}</a>'
 
@@ -32,6 +94,21 @@ def to_problem_id(value):
     return normalized
 
 
+def display_column_name(column):
+    mapped = COLUMN_DISPLAY_NAMES.get(column)
+    if mapped:
+        return mapped
+
+    text = str(column or "").replace("_", " ").strip()
+    if not text:
+        return str(column)
+
+    words = []
+    for word in text.split():
+        words.append("ID" if word.lower() == "id" else word.capitalize())
+    return " ".join(words)
+
+
 def add_problem_id_row_attributes(table_html, problem_ids):
     marker = "<tbody>"
     if marker not in table_html:
@@ -47,97 +124,660 @@ def add_problem_id_row_attributes(table_html, problem_ids):
 
     return prefix + body
 
-yaml_file = "problems.yaml"
-
-html_dir = "docs/"
-html_table = f"{html_dir}problems.html"
-html_header = f"{html_dir}header.html"
-html_scripts = f"{html_dir}javascript.html"
-html_footer = f"{html_dir}footer.html"
-html_index = f"{html_dir}index.html"
-html_table_template = f"{html_dir}table_template.html"
-default_columns = ["name",
-                "textual description",
-                "suite/generator/single",
-                "objectives",
-                "dimensionality",
-                "variable type",
-                "constraints",
-                "dynamic",
-                "noise",
-                "multi-fidelity",
-                "source (real-world/artificial)",
-                "reference",
-                "implementation"]
 
-if __name__ == "__main__":
+def format_type(value):
+    text = str(value or "").strip().lower()
+    if text == "opltype.problem":
+        return "Problem"
+    if text == "opltype.generator":
+        return "Generator"
+    if text == "opltype.suite":
+        return "Suite"
+    return str(value or "")
 
-    # Load data
-    with open(yaml_file) as yaml_input:
-        raw_data = pd.json_normalize(yaml.safe_load(yaml_input))
-
-    if "problem_id" in raw_data.columns:
-        problem_ids = raw_data["problem_id"].fillna("").map(str)
-    else:
-        problem_ids = raw_data["name"].fillna("").map(str).map(to_problem_id)
-
-    problem_ids = problem_ids.where(problem_ids.str.len() > 0, raw_data["name"].fillna("").map(str).map(to_problem_id))
-    data = raw_data.copy()
-
-    # Choose desired columns
-    all_columns = False
-
-    if all_columns is False:
-        columns = default_columns
-        data = data[columns]
-
-    data = data.map(linkify_cell)
-
-    # Generate plain table
-    table = data.to_html(render_links=False,
-                        escape=False,  # Don't escape HTML in cells (to allow links)
-                        index=False,
-                        table_id="problems",
-                        classes=["display compact", "display", "styled-table"],  # Set display style
-                        border=0,
-                        na_rep="")  # Leave NaN cells empty
-
-    table = add_problem_id_row_attributes(table, problem_ids.tolist())
-
-    # Add footer to facilitate individual column search
-    idx = table.index('</table>')
-    final_table = table[:idx] + "<tfoot><tr>" + " ".join(["<th>"+ i +"</th>" for i in data.columns])+"</tr> </tfoot>" + table[idx:]
-
-    default_hidden_columns = {"textual description", "reference", "implementation"}
-
-    column_toggles = "".join(
-        [
-            (
-                f'<label class="column-chip">'
-                f'<input class="col-toggle" type="checkbox" data-column="{i}"'
-                f'{" checked" if col not in default_hidden_columns else ""}>'
-                f'<span>{escape(col)}</span>'
-                f'</label>'
-            )
-            for i, col in enumerate(data.columns)
-        ]
-    )
 
-    with open(html_table_template, encoding="utf-8") as template_file:
-        table_template = template_file.read()
+def format_references(refs):
+    if not refs:
+        return ""
 
-    table_markup = (
-        table_template
-        .replace("__COLUMN_TOGGLES__", column_toggles)
-        .replace("__TABLE__", final_table)
+    parts = []
+    for ref in refs:
+        ref_dict = ref.model_dump() if hasattr(ref, "model_dump") else ref
+        if not isinstance(ref_dict, dict):
+            parts.append(str(ref_dict))
+            continue
+
+        title = str(ref_dict.get("title") or "").strip()
+        authors = ref_dict.get("authors") or []
+        if isinstance(authors, list):
+            authors_txt = "; ".join(str(author) for author in authors if author)
+        else:
+            authors_txt = str(authors).strip()
+
+        link = ref_dict.get("link") or {}
+        if hasattr(link, "model_dump"):
+            link = link.model_dump()
+        if isinstance(link, dict):
+            url = str(link.get("url") or "").strip()
+        else:
+            url = str(link).strip()
+
+        triplet = ", ".join(part for part in [title, authors_txt, url] if part)
+        if triplet:
+            parts.append(triplet)
+
+    return " | ".join(parts)
+
+
+def normalize_variable_type_name(raw_type):
+    text = str(raw_type or "").strip().lower().split(".")[-1]
+    if text in VISIBLE_VARIABLE_TYPES:
+        return text
+    return "unknown"
+
+
+def format_dim(dim):
+    if dim is None:
+        return ""
+    if isinstance(dim, (int, float)):
+        return str(dim)
+    if hasattr(dim, "model_dump"):
+        dim = dim.model_dump()
+    if isinstance(dim, set):
+        dim = sorted(dim)
+    if isinstance(dim, list):
+        return "{" + ", ".join(str(item) for item in dim) + "}"
+    if isinstance(dim, dict):
+        dmin = dim.get("min")
+        dmax = dim.get("max")
+        if dmin is not None and dmax is not None:
+            return f"{dmin}-{dmax}"
+        if dmin is not None:
+            return f">={dmin}"
+        if dmax is not None:
+            return f"<={dmax}"
+        return ""
+    return str(dim)
+
+
+def format_total_bounds(total_min, total_max):
+    if total_min is None and total_max is None:
+        return ""
+    if total_min is not None and total_max is not None and total_min == total_max:
+        return str(total_min)
+    if total_min is not None and total_max is not None:
+        return f"{total_min}-{total_max}"
+    if total_min is not None:
+        return f">={total_min}"
+    if total_max is not None:
+        return f"<={total_max}"
+    return ""
+
+
+def unique_preserve_order(values):
+    seen = set()
+    result = []
+    for value in values:
+        if value in seen:
+            continue
+        seen.add(value)
+        result.append(value)
+    return result
+
+
+def dim_domain(dim):
+    if dim is None:
+        return None
+
+    if hasattr(dim, "model_dump"):
+        dim = dim.model_dump()
+
+    if isinstance(dim, (int, float)):
+        return {"kind": "set", "values": [int(dim)]}
+
+    if isinstance(dim, set):
+        dim = sorted(dim)
+
+    if isinstance(dim, list):
+        numeric = [int(item) for item in dim if isinstance(item, (int, float))]
+        if not numeric:
+            return None
+        return {"kind": "set", "values": unique_preserve_order(numeric)}
+
+    if isinstance(dim, dict):
+        dmin = dim.get("min")
+        dmax = dim.get("max")
+        dmin = int(dmin) if isinstance(dmin, (int, float)) else None
+        dmax = int(dmax) if isinstance(dmax, (int, float)) else None
+        if dmin is None and dmax is None:
+            return None
+        return {"kind": "range", "min": dmin, "max": dmax}
+
+    return None
+
+
+def combine_domains_for_total(domains):
+    if not domains:
+        return ""
+
+    if any(domain["kind"] == "range" for domain in domains):
+        total_min = 0
+        total_max = 0
+        open_upper = False
+        for domain in domains:
+            if domain["kind"] == "set":
+                values = domain["values"]
+                if not values:
+                    continue
+                total_min += min(values)
+                total_max += max(values)
+                continue
+
+            dmin = domain.get("min")
+            dmax = domain.get("max")
+            total_min += dmin or 0
+            if dmax is None:
+                open_upper = True
+            else:
+                total_max += dmax
+
+        return format_total_bounds(total_min, None if open_upper else total_max)
+
+    totals = [0]
+    for domain in domains:
+        new_totals = []
+        for base in totals:
+            for value in domain["values"]:
+                new_totals.append(base + value)
+        totals = unique_preserve_order(new_totals)
+
+    if not totals:
+        return ""
+    if len(totals) == 1:
+        return str(totals[0])
+    return "{" + ", ".join(str(value) for value in totals) + "}"
+
+
+def format_variables_by_type(variables):
+    values = {variable_type: [] for variable_type in VISIBLE_VARIABLE_TYPES}
+    domains = []
+
+    if not variables:
+        return values, ""
+
+    for variable in variables:
+        variable_dict = variable.model_dump() if hasattr(variable, "model_dump") else variable
+        if not isinstance(variable_dict, dict):
+            continue
+
+        variable_type = normalize_variable_type_name(variable_dict.get("type"))
+        dim = variable_dict.get("dim")
+        dim_text = format_dim(dim)
+        if variable_type in values and dim_text:
+            values[variable_type].append(dim_text)
+
+        domain = dim_domain(dim)
+        if domain is not None:
+            domains.append(domain)
+
+    for variable_type in values:
+        values[variable_type] = " | ".join(values[variable_type])
+
+    return values, combine_domains_for_total(domains)
+
+
+def normalize_constraint_type(raw_type):
+    text = str(raw_type or "").strip().lower().split(".")[-1]
+    return text if text else "unknown"
+
+
+def normalize_constraint_hard(raw_hard):
+    text = str(raw_hard or "").strip().lower().split(".")[-1]
+    if text in {"yes", "hard", "true"}:
+        return "hard"
+    if text in {"no", "soft", "false"}:
+        return "soft"
+    if text in {"some", "mixed", "both"}:
+        return "mixed"
+    return "unknown"
+
+
+def list_variable_types(variables):
+    if not variables:
+        return ""
+
+    types = []
+    for variable in variables:
+        variable_dict = variable.model_dump() if hasattr(variable, "model_dump") else variable
+        if not isinstance(variable_dict, dict):
+            continue
+        types.append(normalize_variable_type_name(variable_dict.get("type")))
+
+    types = [v for v in unique_preserve_order(types) if v]
+    if not types:
+        return ""
+    return " | ".join(types)
+
+
+def list_constraint_types(constraints):
+    if not constraints:
+        return ""
+
+    types = []
+    for constraint in constraints:
+        constraint_dict = constraint.model_dump() if hasattr(constraint, "model_dump") else constraint
+        if not isinstance(constraint_dict, dict):
+            continue
+        types.append(normalize_constraint_type(constraint_dict.get("type")))
+
+    types = [c for c in unique_preserve_order(types) if c]
+    if not types:
+        return ""
+    return " | ".join(types)
+
+
+def has_nonzero_info(value, yes_only=False):
+    if value is None:
+        return False
+
+    if isinstance(value, (set, list, tuple, dict)):
+        return len(value) > 0
+
+    text = str(value).strip().lower()
+    if not text:
+        return False
+
+    if yes_only:
+        return text in {"yes", "some", "true", "1"}
+
+    return text not in {"no", "none", "null", "unknown", "?", "0", "false", "[]", "{}"}
+
+
+def build_properties(item):
+    properties = []
+    if has_nonzero_info(getattr(item, "dynamic_type", None)):
+        properties.append("dynamic")
+    if has_nonzero_info(getattr(item, "noise_type", None)):
+        properties.append("noisy")
+    if has_nonzero_info(getattr(item, "allows_partial_evaluation", None), yes_only=True):
+        properties.append("partial evaluations allowed")
+    if has_nonzero_info(getattr(item, "can_evaluate_objectives_independently", None), yes_only=True):
+        properties.append("independent objective evaluations")
+    if has_nonzero_info(getattr(item, "fidelity_levels", None)):
+        properties.append("multi-fidelity")
+    if not properties:
+        return ""
+    return " | ".join(properties)
+
+
+def format_implementation_links(links):
+    if not links:
+        return ""
+
+    urls = []
+    for link in links:
+        link_dict = link.model_dump() if hasattr(link, "model_dump") else link
+        if isinstance(link_dict, dict):
+            url = str(link_dict.get("url") or "").strip()
+            if url:
+                urls.append(url)
+        elif link_dict:
+            urls.append(str(link_dict))
+
+    if not urls:
+        return ""
+    return " | ".join(unique_preserve_order(urls))
+
+
+def normalize_requirements(requirements):
+    if requirements is None:
+        return ""
+    if isinstance(requirements, list):
+        values = [str(item).strip() for item in requirements if str(item).strip()]
+        if not values:
+            return ""
+        return " | ".join(unique_preserve_order(values))
+    return str(requirements).strip()
+
+
+def extract_implementation_fields(implementation_ids, library_items):
+    result = {
+        "Implementation Names": "",
+        "Implementation Languages": "",
+        "Implementation Evaluation Times": "",
+        "Implementation Links": "",
+        "Implementation Descriptions": "",
+        "Implementation Requirements": "",
+    }
+
+    if not implementation_ids:
+        return result
+
+    names = []
+    languages = []
+    evaluation_times = []
+    links = []
+    descriptions = []
+    requirements = []
+
+    for implementation_id in implementation_ids:
+        implementation = library_items.get(implementation_id)
+        if not isinstance(implementation, Implementation):
+            continue
+
+        if implementation.name:
+            names.append(str(implementation.name).strip())
+        if implementation.language:
+            languages.append(str(implementation.language).strip())
+        if implementation.evaluation_time:
+            evaluation_times.append(str(implementation.evaluation_time).strip())
+        if implementation.description:
+            descriptions.append(str(implementation.description).strip())
+
+        req = normalize_requirements(implementation.requirements)
+        if req:
+            requirements.append(req)
+
+        impl_links = format_implementation_links(implementation.links)
+        if impl_links:
+            links.extend(impl_links.split(" | "))
+
+    result["Implementation Names"] = " | ".join(unique_preserve_order([v for v in names if v]))
+    result["Implementation Languages"] = " | ".join(unique_preserve_order([v for v in languages if v]))
+    result["Implementation Evaluation Times"] = " | ".join(unique_preserve_order([v for v in evaluation_times if v]))
+    result["Implementation Links"] = " | ".join(unique_preserve_order([v for v in links if v]))
+    result["Implementation Descriptions"] = " | ".join(unique_preserve_order([v for v in descriptions if v]))
+    result["Implementation Requirements"] = " | ".join(unique_preserve_order([v for v in requirements if v]))
+
+    return result
+
+
+def format_constraint_count(number_value):
+    if number_value is None:
+        return ">=1"
+    return format_dim(number_value)
+
+
+def constraint_count_domain(number_value):
+    if number_value is None:
+        return {"kind": "range", "min": 1, "max": None}
+    return dim_domain(number_value)
+
+
+def format_constraints_by_type(constraints, constraint_types):
+    hard_values = {constraint_type: [] for constraint_type in constraint_types}
+    soft_values = {constraint_type: [] for constraint_type in constraint_types}
+    hard_domains = []
+    soft_domains = []
+    total_domains = []
+
+    if not constraints:
+        return (
+            {constraint_type: "" for constraint_type in constraint_types},
+            {constraint_type: "" for constraint_type in constraint_types},
+            "",
+            "",
+            "",
+        )
+
+    for constraint in constraints:
+        constraint_dict = constraint.model_dump() if hasattr(constraint, "model_dump") else constraint
+        if not isinstance(constraint_dict, dict):
+            continue
+
+        constraint_type = normalize_constraint_type(constraint_dict.get("type"))
+        count_text = format_constraint_count(constraint_dict.get("number"))
+        count_domain = constraint_count_domain(constraint_dict.get("number"))
+        hardness = normalize_constraint_hard(constraint_dict.get("hard"))
+        if count_domain is not None:
+            total_domains.append(count_domain)
+
+        if hardness == "hard":
+            if constraint_type in hard_values:
+                hard_values[constraint_type].append(count_text)
+            if count_domain is not None:
+                hard_domains.append(count_domain)
+        elif hardness == "soft":
+            if constraint_type in soft_values:
+                soft_values[constraint_type].append(count_text)
+            if count_domain is not None:
+                soft_domains.append(count_domain)
+        else:
+            if constraint_type in hard_values:
+                hard_values[constraint_type].append(count_text)
+            if constraint_type in soft_values:
+                soft_values[constraint_type].append(count_text)
+            if count_domain is not None:
+                hard_domains.append(count_domain)
+                soft_domains.append(count_domain)
+
+    for constraint_type in constraint_types:
+        hard_values[constraint_type] = " | ".join(hard_values[constraint_type])
+        soft_values[constraint_type] = " | ".join(soft_values[constraint_type])
+
+    hard_total = combine_domains_for_total(hard_domains)
+    soft_total = combine_domains_for_total(soft_domains)
+    total_constraints = combine_domains_for_total(total_domains)
+    return hard_values, soft_values, hard_total, soft_total, total_constraints
+
+
+def normalize_scalar(value):
+    if value is None:
+        return ""
+    if hasattr(value, "value") and not isinstance(value, (str, int, float, bool)):
+        return normalize_scalar(value.value)
+    if isinstance(value, (str, int, float, bool)):
+        return value
+    return value
+
+
+def normalize_recursive(value):
+    value = normalize_scalar(value)
+    if value in (None, ""):
+        return ""
+
+    if hasattr(value, "model_dump"):
+        value = value.model_dump()
+
+    if isinstance(value, dict):
+        normalized = {key: normalize_recursive(item) for key, item in value.items()}
+        return {key: item for key, item in normalized.items() if item not in ["", [], {}, set()]}
+
+    if isinstance(value, set):
+        normalized_items = [normalize_recursive(item) for item in value]
+        normalized_items = [item for item in normalized_items if item not in ["", [], {}, set()]]
+        normalized_items = sorted(normalized_items, key=str)
+        if len(normalized_items) == 1:
+            return normalized_items[0]
+        return normalized_items
+
+    if isinstance(value, list):
+        normalized_items = [normalize_recursive(item) for item in value]
+        normalized_items = [item for item in normalized_items if item not in ["", [], {}, set()]]
+        if len(normalized_items) == 1:
+            return normalized_items[0]
+        return normalized_items
+
+    return value
+
+
+def to_cell(value):
+    normalized = normalize_recursive(value)
+    if normalized in ("", None):
+        return ""
+    if isinstance(normalized, (str, int, float, bool)):
+        return str(normalized)
+    return json.dumps(normalized, ensure_ascii=False)
+
+
+def load_library(path):
+    with open(path, encoding="utf-8") as yaml_input:
+        raw = yaml_input.read()
+    return parse_yaml_raw_as(Library, raw)
+
+
+def build_problemlike_dataframe(library):
+    library_items = library.root if hasattr(library, "root") else {}
+    problemlike_items = {
+        item_key: item
+        for item_key, item in library_items.items()
+        if isinstance(item, ProblemLike)
+    }
+    problemlike_fields = list(ProblemLike.model_fields.keys())
+    constraint_types = [constraint_type.value for constraint_type in ConstraintType if constraint_type.value != "unknown"]
+
+    rows = []
+    for item_key, item in problemlike_items.items():
+        row = {"problem_id": item_key or to_problem_id(getattr(item, "name", ""))}
+        raw_variables = getattr(item, "variables", None)
+        raw_constraints = getattr(item, "constraints", None)
+        raw_implementations = getattr(item, "implementations", None)
+        variable_values, variable_total = format_variables_by_type(raw_variables)
+        hard_constraints, soft_constraints, hard_total, soft_total, total_constraints = format_constraints_by_type(
+            raw_constraints,
+            constraint_types,
+        )
+
+        for field in problemlike_fields:
+            value = getattr(item, field, None)
+            if field == "references":
+                row[field] = format_references(value)
+            elif field == "type":
+                row[field] = format_type(value)
+            elif field in {"variables", "constraints"}:
+                continue
+            else:
+                row[field] = to_cell(value)
+
+        for variable_type in VISIBLE_VARIABLE_TYPES:
+            row[VARIABLE_COLUMN_NAME_BY_TYPE[variable_type]] = variable_values.get(variable_type, "")
+        row["Total Variables"] = variable_total
+        row["Variable Types"] = list_variable_types(raw_variables)
+        row["Constraint Types"] = list_constraint_types(raw_constraints)
+        row["Properties"] = build_properties(item)
+        row.update(extract_implementation_fields(raw_implementations, library_items))
+
+        for constraint_type in constraint_types:
+            row[f"Hard {constraint_type} Constraints"] = hard_constraints.get(constraint_type, "")
+            row[f"Soft {constraint_type} Constraints"] = soft_constraints.get(constraint_type, "")
+        row["Total Constraints"] = total_constraints
+        rows.append(row)
+
+    base_columns = ["problem_id"] + [field for field in problemlike_fields if field not in {"variables", "constraints"}]
+    split_variable_columns = [
+        "Variable Types",
+        "binary Variables",
+        "categorical Variables",
+        "continuous Variables",
+        "integer Variables",
+        "Total Variables",
+    ]
+    split_implementation_columns = [
+        "Implementation Names",
+        "Implementation Languages",
+        "Implementation Evaluation Times",
+        "Implementation Links",
+        "Implementation Descriptions",
+        "Implementation Requirements",
+    ]
+    split_constraint_columns = []
+    for constraint_type in constraint_types:
+        split_constraint_columns.append(f"Hard {constraint_type} Constraints")
+        split_constraint_columns.append(f"Soft {constraint_type} Constraints")
+    split_constraint_columns += ["Constraint Types", "Total Constraints", "Properties"]
+
+    all_columns = base_columns + split_variable_columns + split_implementation_columns + split_constraint_columns
+
+    # Keep id/type before name, then enforce the requested high-priority reading order.
+    preferred_order = [
+        "problem_id",
+        "name",
+        "type",
+        "Variable Types",
+        "Total Variables",
+        "objectives",
+        "Properties",
+        "Constraint Types",
+        "Total Constraints",
+        "dynamic_type",
+        "noise_type",
+        "allows_partial_evaluation",
+        "can_evaluate_objectives_independently",
+        "fidelity_levels",
+    ]
+    table_columns = [column for column in preferred_order if column in all_columns]
+    table_columns += [column for column in all_columns if column not in table_columns]
+    dataframe = pd.DataFrame(rows, columns=table_columns)
+    dataframe = dataframe.fillna("")
+    return dataframe.rename(columns=display_column_name)
+
+
+def render_table(dataframe):
+    linked_data = dataframe.map(linkify_cell)
+    table_html = linked_data.to_html(
+        render_links=False,
+        escape=False,
+        index=False,
+        table_id="problems",
+        classes=["display compact", "display", "styled-table"],
+        border=0,
+        na_rep="",
+    )
+    table_html = add_problem_id_row_attributes(table_html, dataframe["ID"].astype(str).tolist())
+    footer = "<tfoot><tr>" + " ".join(f"<th>{escape(column)}</th>" for column in dataframe.columns) + "</tr></tfoot>"
+    idx = table_html.index("</table>")
+    return table_html[:idx] + footer + table_html[idx:]
+
+
+def render_column_toggles(columns):
+    return "".join(
+        (
+            f'<label class="column-chip">'
+            f'<input class="col-toggle" type="checkbox" data-column="{index}"'
+            f'{" checked" if column in DEFAULT_VISIBLE_COLUMNS else ""}>'
+            f'<span>{escape(column)}</span>'
+            f'</label>'
+        )
+        for index, column in enumerate(columns)
     )
 
-    # Write table to file
+
+def build_html_page(table_markup, docs_dir):
+    html_table = f"{docs_dir}problems.html"
+    html_header = f"{docs_dir}header.html"
+    html_scripts = f"{docs_dir}javascript.html"
+    html_footer = f"{docs_dir}footer.html"
+    html_index = f"{docs_dir}index.html"
+
     with open(html_table, "w", encoding="utf-8") as table_file:
         table_file.write(table_markup)
 
-    # Merge table and scripts into HTML page
     with open(html_index, "wb") as output_file:
         for part_path in [html_header, html_table, html_scripts, html_footer]:
             with open(part_path, "rb") as part_file:
                 shutil.copyfileobj(part_file, output_file)
+
+
+if __name__ == "__main__":
+    yaml_file = "problems.yaml"
+    html_dir = "docs/"
+    html_table_template = f"{html_dir}table_template.html"
+
+    try:
+        library = load_library(yaml_file)
+    except Exception as exc:
+        raise SystemExit(f"Error parsing YAML file '{yaml_file}': {exc}") from exc
+
+    data = build_problemlike_dataframe(library)
+    final_table = render_table(data)
+    column_toggles = render_column_toggles(data.columns)
+
+    with open(html_table_template, encoding="utf-8") as template_file:
+        table_template = template_file.read()
+
+    table_markup = (
+        table_template
+        .replace("__COLUMN_TOGGLES__", column_toggles)
+        .replace("__TABLE__", final_table)
+    )
+    build_html_page(table_markup, html_dir)

From 8d89fe46556123c881c12232fcf0cad5249eaaaa Mon Sep 17 00:00:00 2001
From: Koen van der Blom <kvdb@cwi.nl>
Date: Thu, 23 Apr 2026 16:19:11 +0200
Subject: [PATCH 9/9] Change toggle visible columns section title

---
 docs/index.html          | 6634 +++++++++++++++++++-------------------
 docs/problems.html       | 6634 +++++++++++++++++++-------------------
 docs/table_template.html |    2 +-
 pyproject.toml           |    1 +
 uv.lock                  |  337 +-
 5 files changed, 6972 insertions(+), 6636 deletions(-)

diff --git a/docs/index.html b/docs/index.html
index ce97309..978566e 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -24,3323 +24,3323 @@ <h2>OPL &ndash; Optimisation problem library</h2>
 <p>
 Submit problems and corrections on <a target="_blank" href="https://github.com/OpenOptimizationOrg/OPL/">GitHub</a> with pull requests / issues, through the <a target="_blank" href="https://docs.google.com/forms/d/e/1FAIpQLSehQp24AuFAH2j9jizDhq8K_BYgNGMKXWTMu6s-2RwEJrK59Q/viewform">Google form</a>, or by email: <a href="mailto:koen.van.der.blom@cwi.nl" itemprop="email">koen.van.der.blom@cwi.nl</a>
 </p>
-<section class="table-shell">
-    <div class="table-toolbar">
-        <details class="column-toolbar-disclosure">
-            <summary class="toolbar-title">Visible Columns</summary>
-            <div class="toolbar-actions">
-                <button type="button" class="toolbar-btn" id="show-all-columns">Show all</button>
-                <button type="button" class="toolbar-btn" id="hide-all-columns">Hide all</button>
-            </div>
-            <div class="column-controls">
-                <label class="column-chip"><input class="col-toggle" type="checkbox" data-column="0"><span>ID</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="1" checked><span>Name</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="2" checked><span>Type</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="3" checked><span>Variable Types</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="4" checked><span>Total Variables</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="5" checked><span>Objectives</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="6" checked><span>Properties</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="7" checked><span>Constraint Types</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="8" checked><span>Total Constraints</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="9" checked><span>Dynamics</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="10" checked><span>Noise</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="11" checked><span>Partial Evaluations</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="12" checked><span>Independent Objectives</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="13" checked><span>Fidelity Levels</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="14"><span>Full Name</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="15"><span>Description</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="16"><span>Tags</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="17"><span>References</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="18"><span>Implementations</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="19"><span>Modality</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="20"><span>Examples</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="21"><span>Source</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="22"><span>Binary Vars</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="23"><span>Categorical Vars</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="24"><span>Continuous Vars</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="25"><span>Integer Vars</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="26"><span>Implementation Names</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="27"><span>Implementation Languages</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="28"><span>Implementation Evaluation Times</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="29"><span>Implementation Links</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="30"><span>Implementation Descriptions</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="31"><span>Implementation Requirements</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="32"><span>Hard Box Constraints</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="33"><span>Soft Box Constraints</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="34"><span>Hard Linear Constraints</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="35"><span>Soft Linear Constraints</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="36"><span>Hard Function Constraints</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="37"><span>Soft Function Constraints</span></label>
-            </div>
-        </details>
-    </div>
-    <div class="table-wrap">
-        <table class="dataframe display compact display styled-table" id="problems">
-  <thead>
-    <tr style="text-align: right;">
-      <th>ID</th>
-      <th>Name</th>
-      <th>Type</th>
-      <th>Variable Types</th>
-      <th>Total Variables</th>
-      <th>Objectives</th>
-      <th>Properties</th>
-      <th>Constraint Types</th>
-      <th>Total Constraints</th>
-      <th>Dynamics</th>
-      <th>Noise</th>
-      <th>Partial Evaluations</th>
-      <th>Independent Objectives</th>
-      <th>Fidelity Levels</th>
-      <th>Full Name</th>
-      <th>Description</th>
-      <th>Tags</th>
-      <th>References</th>
-      <th>Implementations</th>
-      <th>Modality</th>
-      <th>Examples</th>
-      <th>Source</th>
-      <th>Binary Vars</th>
-      <th>Categorical Vars</th>
-      <th>Continuous Vars</th>
-      <th>Integer Vars</th>
-      <th>Implementation Names</th>
-      <th>Implementation Languages</th>
-      <th>Implementation Evaluation Times</th>
-      <th>Implementation Links</th>
-      <th>Implementation Descriptions</th>
-      <th>Implementation Requirements</th>
-      <th>Hard Box Constraints</th>
-      <th>Soft Box Constraints</th>
-      <th>Hard Linear Constraints</th>
-      <th>Soft Linear Constraints</th>
-      <th>Hard Function Constraints</th>
-      <th>Soft Function Constraints</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr data-problem-id="fn_ato">
-      <td>fn_ato</td>
-      <td>ATO</td>
-      <td>Problem</td>
-      <td>continuous</td>
-      <td>10</td>
-      <td>2</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>no</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>Parameters of the Modules of the Automatic Train Operation are optimized; two objectives: minimizing energy consumption and minimizing driving duration.</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>unimodal</td>
-      <td></td>
-      <td>real-world</td>
-      <td></td>
-      <td></td>
-      <td>10</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="fn_building_spatial">
-      <td>fn_building_spatial</td>
-      <td>Building spatial design</td>
-      <td>Problem</td>
-      <td>binary | continuous</td>
-      <td>>=2</td>
-      <td>2</td>
-      <td></td>
-      <td>unknown | box</td>
-      <td>>=2</td>
-      <td></td>
-      <td></td>
-      <td>no</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>Optimise the spatial layout of a building to minimise energy consumption for climate control and minimise the strain on the structure. Many hard constraints; mixed-variable (continuous+binary); expensive evaluations.</td>
-      <td></td>
-      <td>Building spatial design, <a href="https://hdl.handle.net/1887/81789">https://hdl.handle.net/1887/81789</a></td>
-      <td>impl_bso_toolbox</td>
-      <td></td>
-      <td></td>
-      <td>real-world</td>
-      <td>>=1</td>
-      <td></td>
-      <td>>=1</td>
-      <td></td>
-      <td>BSO-toolbox</td>
-      <td>C++</td>
-      <td>~1s (smallest) to ~40s (larger)</td>
-      <td><a href="https://github.com/TUe-excellent-buildings/BSO-toolbox">https://github.com/TUe-excellent-buildings/BSO-toolbox</a></td>
-      <td>Building Spatial Design toolbox (TU/e)</td>
-      <td></td>
-      <td>>=1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="fn_convex_dtlz2">
-      <td>fn_convex_dtlz2</td>
-      <td>Convex DTLZ2</td>
-      <td>Problem</td>
-      <td>continuous</td>
-      <td>>=1</td>
-      <td>[10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>Variant of DTLZ2 with a convex Pareto front (instead of concave)</td>
-      <td></td>
-      <td>Convex DTLZ2, <a href="https://doi.org/10.1109/TEVC.2013.2281535">https://doi.org/10.1109/TEVC.2013.2281535</a></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>>=1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="fn_emdo">
-      <td>fn_emdo</td>
-      <td>Electric Motor Design Optimization</td>
-      <td>Problem</td>
-      <td>integer | continuous</td>
-      <td>26</td>
-      <td>1</td>
-      <td>noisy</td>
-      <td>box | unknown</td>
-      <td>>=14</td>
-      <td></td>
-      <td>noisy</td>
-      <td>no</td>
-      <td></td>
-      <td></td>
-      <td>Electric Motor Design Optimization</td>
-      <td># Goal\nFind a design of a synchronous electric motor for power steering systems that minimizes costs and satisfies all constraints.\n\n# Motivation\nChallenging to find good solutions in a limited time.\n\n# Key Challenges\n* Time-consuming solution evaluation\n* Highly-constrained problem\n* Constraints are multimodal\n\nThis is not an available problem, but could be interesting to show to researchers which difficulties appear in real-world problems.</td>
-      <td></td>
-      <td>A Multi-Step Evaluation Process in Electric Motor Design, Tea Tušar; Peter Korošec; Bogdan Filipič, <a href="https://dis.ijs.si/tea/Publications/Tusar23Multistep.pdf">https://dis.ijs.si/tea/Publications/Tusar23Multistep.pdf</a></td>
-      <td>impl_emdo</td>
-      <td>multimodal</td>
-      <td></td>
-      <td>real-world</td>
-      <td></td>
-      <td></td>
-      <td>13</td>
-      <td>13</td>
-      <td>Electric Motor Design Optimization</td>
-      <td>Python</td>
-      <td>8 minutes</td>
-      <td></td>
-      <td>Not publicly available</td>
-      <td></td>
-      <td>>=1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="fn_fleetopt">
-      <td>fn_fleetopt</td>
-      <td>FleetOpt</td>
-      <td>Problem</td>
-      <td>integer</td>
-      <td>{54, 13208}</td>
-      <td>1</td>
-      <td></td>
-      <td>unknown</td>
-      <td>>=1</td>
-      <td></td>
-      <td></td>
-      <td>yes</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>UK healthcare organisation fleet optimisation: reduce the fleet of non-emergency healthcare trip vehicles while still ensuring all trips can be covered. Bilevel: upper level 54 vars, lower level 13208 vars.</td>
-      <td></td>
-      <td>FleetOpt, <a href="https://dl.acm.org/doi/abs/10.1145/3638530.3664137">https://dl.acm.org/doi/abs/10.1145/3638530.3664137</a></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>real-world</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>{54, 13208}</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="fn_gasoline">
-      <td>fn_gasoline</td>
-      <td>Gasoline direct injection engine design</td>
-      <td>Problem</td>
-      <td>integer | continuous</td>
-      <td>14</td>
-      <td>2</td>
-      <td>multi-fidelity</td>
-      <td>unknown</td>
-      <td>5</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>[1, 2]</td>
-      <td></td>
-      <td>Multi-objective optimization to minimize fuel consumption and NOx emissions over a two-minute dynamic duty cycle, subject to five constraints (turbine inlet temperature, knock occurrences, peak cylinder pressure, peak cylinder pressure rise, total work). Seven decision variables cover hardware choices and engine control parameters.</td>
-      <td></td>
-      <td>Gasoline direct injection engine design, <a href="https://doi.org/10.1016/j.ejor.2022.08.032">https://doi.org/10.1016/j.ejor.2022.08.032</a></td>
-      <td>impl_gasoline</td>
-      <td></td>
-      <td></td>
-      <td>real-world</td>
-      <td></td>
-      <td></td>
-      <td>7</td>
-      <td>7</td>
-      <td>Gasoline direct injection engine design</td>
-      <td>Matlab Simulink / Wave RT</td>
-      <td></td>
-      <td><a href="https://doi.org/10.1016/j.ejor.2022.08.032">https://doi.org/10.1016/j.ejor.2022.08.032</a></td>
-      <td>Proprietary Matlab Simulink + Wave RT co-simulation</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="fn_invdeceptive_deceptive_rotell">
-      <td>fn_invdeceptive_deceptive_rotell</td>
-      <td>InverseDeceptiveTrap+RotatedEllipsoid / DeceptiveTrap+RotatedEllipsoid</td>
-      <td>Problem</td>
-      <td>binary | continuous</td>
-      <td>>=2</td>
-      <td>2</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>Mixed-variable multi-objective test problems, <a href="https://doi.org/10.1145/3449726.3459521">https://doi.org/10.1145/3449726.3459521</a></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>artificial</td>
-      <td>>=1</td>
-      <td></td>
-      <td>>=1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="fn_inverted_dtlz1">
-      <td>fn_inverted_dtlz1</td>
-      <td>Inverted DTLZ1</td>
-      <td>Problem</td>
-      <td>continuous</td>
-      <td>>=1</td>
-      <td>[10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>Variant of DTLZ1 with an inverted Pareto front</td>
-      <td></td>
-      <td>Inverted DTLZ1, <a href="https://doi.org/10.1109/TEVC.2013.2281534">https://doi.org/10.1109/TEVC.2013.2281534</a></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>>=1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="fn_jsec2019">
-      <td>fn_jsec2019</td>
-      <td>JSEC2019</td>
-      <td>Problem</td>
-      <td>continuous</td>
-      <td>32</td>
-      <td>[1, 2, 3, 4, 5]</td>
-      <td></td>
-      <td>unknown</td>
-      <td>22</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>expensive evaluations 3s; 22 constraints</td>
-      <td></td>
-      <td>JPNSEC EC-Symposium 2019 competition, <a href="http://www.jpnsec.org/files/competition2019/EC-Symposium-2019-Competition-English.html">http://www.jpnsec.org/files/competition2019/EC-Symposium-2019-Competition-English.html</a></td>
-      <td>impl_jsec2019</td>
-      <td></td>
-      <td></td>
-      <td>real-world</td>
-      <td></td>
-      <td></td>
-      <td>32</td>
-      <td></td>
-      <td>JSEC 2019 competition</td>
-      <td></td>
-      <td>3s</td>
-      <td><a href="http://www.jpnsec.org/files/competition2019/EC-Symposium-2019-Competition-English.html">http://www.jpnsec.org/files/competition2019/EC-Symposium-2019-Competition-English.html</a></td>
-      <td>JPNSEC EC-Symposium 2019 competition problem</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="fn_onemax_sphere_deceptive_rotell">
-      <td>fn_onemax_sphere_deceptive_rotell</td>
-      <td>Onemax+Sphere / DeceptiveTrap+RotatedEllipsoid</td>
-      <td>Problem</td>
-      <td>binary | continuous</td>
-      <td>>=2</td>
-      <td>2</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>Mixed-variable multi-objective test problems, <a href="https://doi.org/10.1145/3449726.3459521">https://doi.org/10.1145/3449726.3459521</a></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>artificial</td>
-      <td>>=1</td>
-      <td></td>
-      <td>>=1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="fn_onemax_sphere_zeromax_sphere">
-      <td>fn_onemax_sphere_zeromax_sphere</td>
-      <td>Onemax+Sphere / Zeromax+Sphere</td>
-      <td>Problem</td>
-      <td>binary | continuous</td>
-      <td>>=2</td>
-      <td>2</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>Onemax+Sphere / Zeromax+Sphere, <a href="https://doi.org/10.1145/3449726.3459521">https://doi.org/10.1145/3449726.3459521</a></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>artificial</td>
-      <td>>=1</td>
-      <td></td>
-      <td>>=1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="fn_radar_waveform">
-      <td>fn_radar_waveform</td>
-      <td>Radar waveform</td>
-      <td>Problem</td>
-      <td>integer</td>
-      <td>4-12</td>
-      <td>9</td>
-      <td></td>
-      <td>unknown</td>
-      <td>>=1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>Radar waveform design, <a href="https://doi.org/10.1007/978-3-540-70928-2_53">https://doi.org/10.1007/978-3-540-70928-2_53</a></td>
-      <td>impl_radar_waveform</td>
-      <td></td>
-      <td></td>
-      <td>real-world</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>4-12</td>
-      <td>Evan Hughes radar waveform code</td>
-      <td></td>
-      <td></td>
-      <td><a href="http://code.evanhughes.org/">http://code.evanhughes.org/</a></td>
-      <td>Radar waveform design reference implementation</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="gen_beacon">
-      <td>gen_beacon</td>
-      <td>BEACON</td>
-      <td>Generator</td>
-      <td>continuous</td>
-      <td>>=1</td>
-      <td>2</td>
-      <td></td>
-      <td>box</td>
-      <td>0</td>
-      <td></td>
-      <td></td>
-      <td>no</td>
-      <td></td>
-      <td></td>
-      <td>Continuous Bi-objective Benchmark problems with Explicit Adjustable COrrelatioN control</td>
-      <td>Generator for bi-objective benchmark problems with explicitly controlled correlations in continuous spaces. Multimodal with random structure.</td>
-      <td></td>
-      <td>BEACON, <a href="https://dl.acm.org/doi/10.1145/3712255.3734303">https://dl.acm.org/doi/10.1145/3712255.3734303</a></td>
-      <td>impl_beacon</td>
-      <td>multimodal</td>
-      <td></td>
-      <td>artificial</td>
-      <td></td>
-      <td></td>
-      <td>>=1</td>
-      <td></td>
-      <td>BEACON</td>
-      <td>Python</td>
-      <td>negligible</td>
-      <td><a href="https://github.com/Stebbet/BEACON/">https://github.com/Stebbet/BEACON/</a></td>
-      <td>Continuous Bi-objective Benchmark with Explicit Adjustable COrrelatioN control</td>
-      <td></td>
-      <td>0</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="gen_bono_bench">
-      <td>gen_bono_bench</td>
-      <td>BONO-Bench</td>
-      <td>Generator</td>
-      <td>continuous</td>
-      <td>>=1</td>
-      <td>2</td>
-      <td></td>
-      <td>box</td>
-      <td>>=1</td>
-      <td></td>
-      <td></td>
-      <td>no</td>
-      <td></td>
-      <td></td>
-      <td>Bi-objective Numerical Optimization Benchmark</td>
-      <td>Bi-objective problem generator and suite with scalable continuous decision space. Features complex problem properties and Pareto front approximations with error guarantees for the hypervolume and exact R2 indicators.</td>
-      <td></td>
-      <td></td>
-      <td>impl_bonobench</td>
-      <td>multimodal</td>
-      <td></td>
-      <td>artificial</td>
-      <td></td>
-      <td></td>
-      <td>>=1</td>
-      <td></td>
-      <td>BONO-Bench</td>
-      <td>Python</td>
-      <td></td>
-      <td><a href="https://github.com/schaepermeier/bonobench">https://github.com/schaepermeier/bonobench</a></td>
-      <td>Bi-objective Numerical Optimization Benchmark (BONO-Bench)</td>
-      <td></td>
-      <td>>=1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="gen_ealain">
-      <td>gen_ealain</td>
-      <td>Ealain</td>
-      <td>Generator</td>
-      <td>binary | continuous | integer</td>
-      <td>>=3</td>
-      <td>[1, 10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
-      <td>dynamic | multi-fidelity</td>
-      <td>unknown</td>
-      <td>>=1</td>
-      <td>optional</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>[1, 2]</td>
-      <td></td>
-      <td>Real-world-like, easily extensible to increase complexity</td>
-      <td></td>
-      <td>Ealain, <a href="https://doi.org/10.1145/3638530.3654299">https://doi.org/10.1145/3638530.3654299</a></td>
-      <td>impl_ealain</td>
-      <td></td>
-      <td></td>
-      <td>real-world-like</td>
-      <td>>=1</td>
-      <td></td>
-      <td>>=1</td>
-      <td>>=1</td>
-      <td>Ealain</td>
-      <td></td>
-      <td></td>
-      <td><a href="https://github.com/qrenau/Ealain">https://github.com/qrenau/Ealain</a></td>
-      <td>Real-world-like extensible benchmark problem generator</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="gen_gnbg">
-      <td>gen_gnbg</td>
-      <td>GNBG</td>
-      <td>Generator</td>
-      <td>continuous</td>
-      <td>>=1</td>
-      <td>1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>Generator counterpart of GNBG.</td>
-      <td></td>
-      <td>GNBG, <a href="https://arxiv.org/abs/2312.07083">https://arxiv.org/abs/2312.07083</a></td>
-      <td>impl_gnbg</td>
-      <td></td>
-      <td></td>
-      <td>artificial</td>
-      <td></td>
-      <td></td>
-      <td>>=1</td>
-      <td></td>
-      <td>GNBG Generator</td>
-      <td></td>
-      <td></td>
-      <td><a href="https://github.com/Danial-Yazdani/GNBG-Generator">https://github.com/Danial-Yazdani/GNBG-Generator</a></td>
-      <td>Generalized Numerical Benchmark Generator</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="gen_gnbg_ii">
-      <td>gen_gnbg_ii</td>
-      <td>GNBG-II</td>
-      <td>Generator</td>
-      <td>continuous</td>
-      <td>>=1</td>
-      <td>1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>Generator counterpart of GNBG-II.</td>
-      <td></td>
-      <td>GNBG-II, <a href="https://dl.acm.org/doi/pdf/10.1145/3712255.3734271">https://dl.acm.org/doi/pdf/10.1145/3712255.3734271</a></td>
-      <td>["impl_gnbg_ii", "impl_iohgnbg"]</td>
-      <td></td>
-      <td></td>
-      <td>artificial</td>
-      <td></td>
-      <td></td>
-      <td>>=1</td>
-      <td></td>
-      <td>GNBG-II | IOHGNBG</td>
-      <td></td>
-      <td></td>
-      <td><a href="https://github.com/rohitsalgotra/GNBG-II">https://github.com/rohitsalgotra/GNBG-II</a> | <a href="https://github.com/IOHprofiler/IOHGNBG">https://github.com/IOHprofiler/IOHGNBG</a></td>
-      <td>Generalized Numerical Benchmark Generator version 2 | IOHprofiler version of GNBG</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="gen_gpd">
-      <td>gen_gpd</td>
-      <td>GPD</td>
-      <td>Generator</td>
-      <td>continuous</td>
-      <td>>=1</td>
-      <td>[10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
-      <td>noisy</td>
-      <td>unknown</td>
-      <td>>=1</td>
-      <td></td>
-      <td>optional</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>GPD generator, <a href="https://doi.org/10.1016/j.asoc.2020.106139">https://doi.org/10.1016/j.asoc.2020.106139</a></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>>=1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="gen_iohclustering">
-      <td>gen_iohclustering</td>
-      <td>IOHClustering</td>
-      <td>Generator</td>
-      <td>continuous</td>
-      <td>>=1</td>
-      <td>1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>Generator counterpart of the IOHClustering suite.</td>
-      <td></td>
-      <td>IOHClustering, <a href="https://arxiv.org/pdf/2505.09233">https://arxiv.org/pdf/2505.09233</a></td>
-      <td>impl_iohclustering</td>
-      <td>multimodal</td>
-      <td></td>
-      <td>artificial-from-real-data</td>
-      <td></td>
-      <td></td>
-      <td>>=1</td>
-      <td></td>
-      <td>IOHClustering</td>
-      <td></td>
-      <td></td>
-      <td><a href="https://github.com/IOHprofiler/IOHClustering">https://github.com/IOHprofiler/IOHClustering</a></td>
-      <td>Clustering-based optimization benchmark built on ML datasets</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="gen_ma_bbob">
-      <td>gen_ma_bbob</td>
-      <td>MA-BBOB</td>
-      <td>Generator</td>
-      <td>continuous</td>
-      <td>>=1</td>
-      <td>1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>Generator that creates affine combinations of BBOB functions</td>
-      <td></td>
-      <td>MA-BBOB, <a href="https://doi.org/10.1145/3673908">https://doi.org/10.1145/3673908</a></td>
-      <td>["impl_iohexperimenter", "impl_ma_bbob"]</td>
-      <td>multimodal</td>
-      <td></td>
-      <td>artificial</td>
-      <td></td>
-      <td></td>
-      <td>>=1</td>
-      <td></td>
-      <td>IOHexperimenter | MA-BBOB (IOHexperimenter)</td>
-      <td>C++/Python</td>
-      <td></td>
-      <td><a href="https://github.com/IOHprofiler/IOHexperimenter">https://github.com/IOHprofiler/IOHexperimenter</a> | <a href="https://github.com/IOHprofiler/IOHexperimenter/blob/master/example/Competitions/MA-BBOB/Example_MABBOB.ipynb">https://github.com/IOHprofiler/IOHexperimenter/blob/master/example/Competitions/MA-BBOB/Example_MABBOB.ipynb</a></td>
-      <td>IOHprofiler experimenter framework | Example notebook for MA-BBOB in IOHexperimenter</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="gen_mpm2">
-      <td>gen_mpm2</td>
-      <td>MPM2</td>
-      <td>Generator</td>
-      <td>continuous</td>
-      <td>>=1</td>
-      <td>1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>nonlinear nonseparable nonsymmetric; scalable in terms of time to evaluate the objective function</td>
-      <td></td>
-      <td>MPM2 technical report TR15-01, <a href="https://ls11-www.cs.tu-dortmund.de/_media/techreports/tr15-01.pdf">https://ls11-www.cs.tu-dortmund.de/_media/techreports/tr15-01.pdf</a></td>
-      <td>impl_mpm2</td>
-      <td>multimodal</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>>=1</td>
-      <td></td>
-      <td>MPM2 (smoof)</td>
-      <td>Python</td>
-      <td></td>
-      <td><a href="https://github.com/jakobbossek/smoof/blob/master/inst/mpm2.py">https://github.com/jakobbossek/smoof/blob/master/inst/mpm2.py</a></td>
-      <td>Python implementation of MPM2 distributed with smoof</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="gen_mubqp">
-      <td>gen_mubqp</td>
-      <td>mUBQP</td>
-      <td>Generator</td>
-      <td>binary</td>
-      <td>>=1</td>
-      <td>[1, 10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>tunable variable and objective dimensions; tunable density and correlation between objectives</td>
-      <td></td>
-      <td>mUBQP benchmark, <a href="https://doi.org/10.1016/j.asoc.2013.11.008">https://doi.org/10.1016/j.asoc.2013.11.008</a></td>
-      <td>impl_mocobench</td>
-      <td>["multimodal", "quadratic"]</td>
-      <td></td>
-      <td></td>
-      <td>>=1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>mocobench</td>
-      <td>C++</td>
-      <td></td>
-      <td><a href="https://gitlab.com/aliefooghe/mocobench/">https://gitlab.com/aliefooghe/mocobench/</a></td>
-      <td>Multi-objective combinatorial optimization benchmark</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="gen_puboi">
-      <td>gen_puboi</td>
-      <td>PUBOi</td>
-      <td>Generator</td>
-      <td>binary</td>
-      <td>>=1</td>
-      <td>1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>no</td>
-      <td></td>
-      <td></td>
-      <td>Polynomial Unconstrained Binary Optimization with tunable importance</td>
-      <td>A benchmark in which variable importance is tunable, based on the Walsh function.</td>
-      <td></td>
-      <td>PUBOi, <a href="https://link.springer.com/chapter/10.1007/978-3-031-04148-8_12">https://link.springer.com/chapter/10.1007/978-3-031-04148-8_12</a></td>
-      <td>impl_puboi</td>
-      <td></td>
-      <td></td>
-      <td>artificial</td>
-      <td>>=1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>PUBO Importance Benchmark</td>
-      <td>Python / C++</td>
-      <td></td>
-      <td><a href="https://gitlab.com/verel/pubo-importance-benchmark">https://gitlab.com/verel/pubo-importance-benchmark</a></td>
-      <td>A benchmark in which variable importance is tunable, based on the Walsh function</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="gen_randoptgen">
-      <td>gen_randoptgen</td>
-      <td>RandOptGen</td>
-      <td>Generator</td>
-      <td>binary | continuous | integer</td>
-      <td>>=3</td>
-      <td>[1, 10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>no</td>
-      <td></td>
-      <td></td>
-      <td>RandOptGen</td>
-      <td>A Unified Random Problem Generator for Single- and Multi-Objective Optimization Problems with Mixed-Variable Input Spaces.</td>
-      <td></td>
-      <td></td>
-      <td>impl_randoptgen</td>
-      <td>multimodal</td>
-      <td></td>
-      <td>artificial</td>
-      <td>>=1</td>
-      <td></td>
-      <td>>=1</td>
-      <td>>=1</td>
-      <td>RandOptGen</td>
-      <td>Python</td>
-      <td>milliseconds</td>
-      <td><a href="https://github.com/MALEO-research-group/RandOptGen">https://github.com/MALEO-research-group/RandOptGen</a> | <a href="https://doi.org/10.1145/3712256.3726478">https://doi.org/10.1145/3712256.3726478</a></td>
-      <td>Unified Random Problem Generator for Single- and Multi-Objective Optimization with Mixed-Variable Input Spaces</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="gen_rho_mnk_landscapes">
-      <td>gen_rho_mnk_landscapes</td>
-      <td>ρMNK-Landscapes</td>
-      <td>Generator</td>
-      <td>binary</td>
-      <td>>=1</td>
-      <td>[1, 10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>tunable variable and objective dimensions; tunable multimodality and correlation between objectives</td>
-      <td></td>
-      <td>On the design of multi-objective evolutionary algorithms based on NK-landscapes, <a href="https://doi.org/10.1016/j.ejor.2012.12.019">https://doi.org/10.1016/j.ejor.2012.12.019</a></td>
-      <td>impl_mocobench</td>
-      <td>multimodal</td>
-      <td></td>
-      <td></td>
-      <td>>=1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>mocobench</td>
-      <td>C++</td>
-      <td></td>
-      <td><a href="https://gitlab.com/aliefooghe/mocobench/">https://gitlab.com/aliefooghe/mocobench/</a></td>
-      <td>Multi-objective combinatorial optimization benchmark</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="gen_rho_mtsp">
-      <td>gen_rho_mtsp</td>
-      <td>ρmTSP</td>
-      <td>Generator</td>
-      <td>unknown</td>
-      <td>>=1</td>
-      <td>[1, 10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>tunable variable and objective dimensions; tunable instance type (euclidean/random); tunable correlation between objectives</td>
-      <td></td>
-      <td>On the impact of multi-objective scalability for the ρmTSP, <a href="https://doi.org/10.1007/978-3-319-45823-6_40">https://doi.org/10.1007/978-3-319-45823-6_40</a></td>
-      <td>impl_mocobench</td>
-      <td>["multimodal", "quadratic"]</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>mocobench</td>
-      <td>C++</td>
-      <td></td>
-      <td><a href="https://gitlab.com/aliefooghe/mocobench/">https://gitlab.com/aliefooghe/mocobench/</a></td>
-      <td>Multi-objective combinatorial optimization benchmark</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="gen_wmodel">
-      <td>gen_wmodel</td>
-      <td>W-model</td>
-      <td>Generator</td>
-      <td>binary</td>
-      <td>>=1</td>
-      <td>1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>Tunable generator for binary optimization based on several difficulty features</td>
-      <td></td>
-      <td>W-model, <a href="https://dl.acm.org/doi/abs/10.1145/3205651.3208240">https://dl.acm.org/doi/abs/10.1145/3205651.3208240</a></td>
-      <td>impl_wmodel</td>
-      <td></td>
-      <td></td>
-      <td>artificial</td>
-      <td>>=1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>BBDOB W-Model</td>
-      <td></td>
-      <td></td>
-      <td><a href="https://github.com/thomasWeise/BBDOB_W_Model">https://github.com/thomasWeise/BBDOB_W_Model</a></td>
-      <td>Tunable generator for binary optimization</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_amvop">
-      <td>suite_amvop</td>
-      <td>AMVOP</td>
-      <td>Suite</td>
-      <td>integer | continuous | categorical</td>
-      <td>>=3</td>
-      <td>1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>AMVOP, <a href="https://doi.org/10.1109/TEVC.2013.2281531">https://doi.org/10.1109/TEVC.2013.2281531</a></td>
-      <td></td>
-      <td>multimodal</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>>=1</td>
-      <td>>=1</td>
-      <td>>=1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_bbob">
-      <td>suite_bbob</td>
-      <td>BBOB</td>
-      <td>Suite</td>
-      <td>continuous</td>
-      <td>>=1</td>
-      <td>1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>COCO: a platform for comparing continuous optimizers in a black-box setting, <a href="https://doi.org/10.1080/10556788.2020.1808977">https://doi.org/10.1080/10556788.2020.1808977</a></td>
-      <td>impl_coco</td>
-      <td>multimodal</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>>=1</td>
-      <td></td>
-      <td>COCO framework</td>
-      <td>C/Python</td>
-      <td></td>
-      <td><a href="https://github.com/numbbo/coco">https://github.com/numbbo/coco</a></td>
-      <td>Comparing Continuous Optimizers: black-box optimization benchmarking platform</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_bbob_biobj">
-      <td>suite_bbob_biobj</td>
-      <td>BBOB-biobj</td>
-      <td>Suite</td>
-      <td>continuous</td>
-      <td>2-40</td>
-      <td>2</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>BBOB bi-objective test suite, <a href="https://doi.org/10.48550/arXiv.1604.00359">https://doi.org/10.48550/arXiv.1604.00359</a></td>
-      <td>impl_coco</td>
-      <td>multimodal</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>2-40</td>
-      <td></td>
-      <td>COCO framework</td>
-      <td>C/Python</td>
-      <td></td>
-      <td><a href="https://github.com/numbbo/coco">https://github.com/numbbo/coco</a></td>
-      <td>Comparing Continuous Optimizers: black-box optimization benchmarking platform</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_bbob_biobj_mixint">
-      <td>suite_bbob_biobj_mixint</td>
-      <td>BBOB-biobj-mixint</td>
-      <td>Suite</td>
-      <td>continuous | integer</td>
-      <td>10-320</td>
-      <td>2</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>BBOB bi-objective mixed-integer test suite, <a href="https://doi.org/10.1145/3321707.3321868">https://doi.org/10.1145/3321707.3321868</a></td>
-      <td>impl_coco</td>
-      <td>multimodal</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>5-160</td>
-      <td>5-160</td>
-      <td>COCO framework</td>
-      <td>C/Python</td>
-      <td></td>
-      <td><a href="https://github.com/numbbo/coco">https://github.com/numbbo/coco</a></td>
-      <td>Comparing Continuous Optimizers: black-box optimization benchmarking platform</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_bbob_constrained">
-      <td>suite_bbob_constrained</td>
-      <td>BBOB-constrained</td>
-      <td>Suite</td>
-      <td>continuous</td>
-      <td>2-40</td>
-      <td>1</td>
-      <td></td>
-      <td>unknown</td>
-      <td>>=1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>bbob-constrained documentation, <a href="http://numbbo.github.io/coco-doc/bbob-constrained/">http://numbbo.github.io/coco-doc/bbob-constrained/</a></td>
-      <td>impl_coco</td>
-      <td>multimodal</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>2-40</td>
-      <td></td>
-      <td>COCO framework</td>
-      <td>C/Python</td>
-      <td></td>
-      <td><a href="https://github.com/numbbo/coco">https://github.com/numbbo/coco</a></td>
-      <td>Comparing Continuous Optimizers: black-box optimization benchmarking platform</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_bbob_largescale">
-      <td>suite_bbob_largescale</td>
-      <td>BBOB-largescale</td>
-      <td>Suite</td>
-      <td>continuous</td>
-      <td>20-640</td>
-      <td>1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>BBOB large-scale test suite, <a href="https://doi.org/10.48550/arXiv.1903.06396">https://doi.org/10.48550/arXiv.1903.06396</a></td>
-      <td>impl_coco</td>
-      <td>multimodal</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>20-640</td>
-      <td></td>
-      <td>COCO framework</td>
-      <td>C/Python</td>
-      <td></td>
-      <td><a href="https://github.com/numbbo/coco">https://github.com/numbbo/coco</a></td>
-      <td>Comparing Continuous Optimizers: black-box optimization benchmarking platform</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_bbob_mixint">
-      <td>suite_bbob_mixint</td>
-      <td>BBOB-mixint</td>
-      <td>Suite</td>
-      <td>continuous | integer</td>
-      <td>10-320</td>
-      <td>1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>BBOB mixed-integer test suite, <a href="https://doi.org/10.1145/3321707.3321868">https://doi.org/10.1145/3321707.3321868</a></td>
-      <td>impl_coco</td>
-      <td>multimodal</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>5-160</td>
-      <td>5-160</td>
-      <td>COCO framework</td>
-      <td>C/Python</td>
-      <td></td>
-      <td><a href="https://github.com/numbbo/coco">https://github.com/numbbo/coco</a></td>
-      <td>Comparing Continuous Optimizers: black-box optimization benchmarking platform</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_bbob_noisy">
-      <td>suite_bbob_noisy</td>
-      <td>BBOB-noisy</td>
-      <td>Suite</td>
-      <td>continuous</td>
-      <td>>=1</td>
-      <td>1</td>
-      <td>noisy</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>noisy</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>Real-parameter black-box optimization benchmarking: noisy functions definitions, <a href="https://hal.inria.fr/inria-00369466">https://hal.inria.fr/inria-00369466</a></td>
-      <td>impl_coco_legacy</td>
-      <td>multimodal</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>>=1</td>
-      <td></td>
-      <td>COCO legacy (bbob-noisy)</td>
-      <td>C/Python</td>
-      <td></td>
-      <td><a href="https://web.archive.org/web/20210416065610/https://coco.gforge.inria.fr/doku.php?id=downloads">https://web.archive.org/web/20210416065610/https://coco.gforge.inria.fr/doku.php?id=downloads</a></td>
-      <td>Archived COCO download page that hosted the bbob-noisy suite</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_bp">
-      <td>suite_bp</td>
-      <td>BP</td>
-      <td>Suite</td>
-      <td>continuous</td>
-      <td>>=1</td>
-      <td>[10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
-      <td>noisy</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>unknown</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>BP benchmark, <a href="https://doi.org/10.1109/CEC.2019.8790277">https://doi.org/10.1109/CEC.2019.8790277</a></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>>=1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_brachytherapy">
-      <td>suite_brachytherapy</td>
-      <td>Brachytherapy treatment planning</td>
-      <td>Suite</td>
-      <td>continuous</td>
-      <td>100-500</td>
-      <td>[2, 3]</td>
-      <td>multi-fidelity</td>
-      <td>unknown</td>
-      <td>>=1</td>
-      <td></td>
-      <td></td>
-      <td>yes</td>
-      <td></td>
-      <td>[1, 2]</td>
-      <td>Brachytherapy treatment planning</td>
-      <td>Treatment planning for internal radiation therapy. Multi-objective with aggregated objectives; no public source code.</td>
-      <td></td>
-      <td>Brachytherapy treatment planning, <a href="https://www.sciencedirect.com/science/article/pii/S1538472123016781">https://www.sciencedirect.com/science/article/pii/S1538472123016781</a></td>
-      <td></td>
-      <td>multimodal</td>
-      <td></td>
-      <td>real-world</td>
-      <td></td>
-      <td></td>
-      <td>100-500</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_car_structure">
-      <td>suite_car_structure</td>
-      <td>Car structure</td>
-      <td>Suite</td>
-      <td>integer</td>
-      <td>144-222</td>
-      <td>2</td>
-      <td></td>
-      <td>unknown</td>
-      <td>54</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>54 constraints</td>
-      <td></td>
-      <td>Car structure design benchmark, <a href="https://doi.org/10.1145/3205651.3205702">https://doi.org/10.1145/3205651.3205702</a></td>
-      <td>impl_car_structure</td>
-      <td></td>
-      <td></td>
-      <td>real-world</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>144-222</td>
-      <td>Car-structure benchmark</td>
-      <td></td>
-      <td></td>
-      <td><a href="http://ladse.eng.isas.jaxa.jp/benchmark/">http://ladse.eng.isas.jaxa.jp/benchmark/</a></td>
-      <td>JAXA LADSE benchmark problems</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_cdmp">
-      <td>suite_cdmp</td>
-      <td>CDMP</td>
-      <td>Suite</td>
-      <td>continuous</td>
-      <td>>=1</td>
-      <td>[10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
-      <td>dynamic | noisy</td>
-      <td>unknown</td>
-      <td>>=1</td>
-      <td>unknown</td>
-      <td>unknown</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>CDMP benchmark, <a href="https://doi.org/10.1145/3321707.3321878">https://doi.org/10.1145/3321707.3321878</a></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>>=1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_cec2013">
-      <td>suite_cec2013</td>
-      <td>CEC2013</td>
-      <td>Suite</td>
-      <td>continuous</td>
-      <td>>=1</td>
-      <td>1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>suite used for cec2013 competition. Also in IOH.</td>
-      <td></td>
-      <td>CEC2013 definitions, <a href="https://peerj.com/articles/cs-2671/CEC2013.pdf">https://peerj.com/articles/cs-2671/CEC2013.pdf</a></td>
-      <td>["impl_cec2013", "impl_iohexperimenter"]</td>
-      <td></td>
-      <td></td>
-      <td>artificial</td>
-      <td></td>
-      <td></td>
-      <td>>=1</td>
-      <td></td>
-      <td>IOHexperimenter | CEC2013 reference code</td>
-      <td>C++/Python</td>
-      <td></td>
-      <td><a href="https://github.com/IOHprofiler/IOHexperimenter">https://github.com/IOHprofiler/IOHexperimenter</a> | <a href="https://github.com/P-N-Suganthan/CEC2013">https://github.com/P-N-Suganthan/CEC2013</a></td>
-      <td>IOHprofiler experimenter framework | Suganthan's reference implementation</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_cec2015_dmoo">
-      <td>suite_cec2015_dmoo</td>
-      <td>CEC2015-DMOO</td>
-      <td>Suite</td>
-      <td>continuous</td>
-      <td>0</td>
-      <td>[2, 3]</td>
-      <td>dynamic</td>
-      <td>unknown</td>
-      <td>>=1</td>
-      <td>dynamic</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>Benchmark Functions for CEC 2015 Special Session and Competition on Dynamic Multi-objective Optimization</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>0</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_cec2018_dt">
-      <td>suite_cec2018_dt</td>
-      <td>CEC2018 DT</td>
-      <td>Suite</td>
-      <td>unknown</td>
-      <td>>=1</td>
-      <td>[2, 3]</td>
-      <td>dynamic</td>
-      <td></td>
-      <td></td>
-      <td>dynamic</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>CEC2018 Competition on Dynamic Multiobjective Optimisation</td>
-      <td>14 problems. Time-dependent: Pareto front/Pareto set geometry; irregular Pareto front shapes; variable-linkage; number of disconnected Pareto front segments; etc.</td>
-      <td></td>
-      <td>CEC2018 DMOP Competition TR, <a href="https://www.academia.edu/download/94499025/TR-CEC2018-DMOP-Competition.pdf">https://www.academia.edu/download/94499025/TR-CEC2018-DMOP-Competition.pdf</a></td>
-      <td>impl_pymoo</td>
-      <td></td>
-      <td></td>
-      <td>artificial</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>pymoo</td>
-      <td>Python</td>
-      <td></td>
-      <td><a href="https://github.com/anyoptimization/pymoo">https://github.com/anyoptimization/pymoo</a></td>
-      <td>Multi-objective optimization in Python</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_cec2022">
-      <td>suite_cec2022</td>
-      <td>CEC2022</td>
-      <td>Suite</td>
-      <td>continuous</td>
-      <td>>=1</td>
-      <td>1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>suite used for cec2022 competition. Also in IOH.</td>
-      <td></td>
-      <td>CEC2022 TR, <a href="https://github.com/P-N-Suganthan/2022-SO-BO/blob/main/CEC2022%20TR.pdf">https://github.com/P-N-Suganthan/2022-SO-BO/blob/main/CEC2022%20TR.pdf</a></td>
-      <td>["impl_cec2022", "impl_iohexperimenter"]</td>
-      <td></td>
-      <td></td>
-      <td>artificial</td>
-      <td></td>
-      <td></td>
-      <td>>=1</td>
-      <td></td>
-      <td>IOHexperimenter | CEC2022 reference code</td>
-      <td>C++/Python</td>
-      <td></td>
-      <td><a href="https://github.com/IOHprofiler/IOHexperimenter">https://github.com/IOHprofiler/IOHexperimenter</a> | <a href="https://github.com/P-N-Suganthan/2022-SO-BO">https://github.com/P-N-Suganthan/2022-SO-BO</a></td>
-      <td>IOHprofiler experimenter framework | Suganthan's reference implementation</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_cfd">
-      <td>suite_cfd</td>
-      <td>CFD</td>
-      <td>Suite</td>
-      <td>unknown</td>
-      <td>>=1</td>
-      <td>[1, 2]</td>
-      <td></td>
-      <td>unknown</td>
-      <td>>=1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>expensive evaluations 30s-15m</td>
-      <td></td>
-      <td>CFD test problem suite, <a href="https://doi.org/10.1007/978-3-319-99259-4_24">https://doi.org/10.1007/978-3-319-99259-4_24</a></td>
-      <td>impl_cfd</td>
-      <td></td>
-      <td></td>
-      <td>real-world</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>CFD test problem suite</td>
-      <td></td>
-      <td>30s-15m</td>
-      <td><a href="https://bitbucket.org/arahat/cfd-test-problem-suite">https://bitbucket.org/arahat/cfd-test-problem-suite</a></td>
-      <td>Expensive real-world CFD-based test problems</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_cre">
-      <td>suite_cre</td>
-      <td>CRE</td>
-      <td>Suite</td>
-      <td>integer | continuous</td>
-      <td>6-14</td>
-      <td>[2, 3, 4, 5]</td>
-      <td></td>
-      <td>unknown</td>
-      <td>>=1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>Easy-to-evaluate real-world multi-objective optimization problems, Ryoji Tanabe; Hisao Ishibuchi, <a href="https://doi.org/10.1016/j.asoc.2020.106078">https://doi.org/10.1016/j.asoc.2020.106078</a></td>
-      <td>impl_reproblems</td>
-      <td></td>
-      <td></td>
-      <td>real-world-like</td>
-      <td></td>
-      <td></td>
-      <td>3-7</td>
-      <td>3-7</td>
-      <td>reproblems</td>
-      <td>Python</td>
-      <td></td>
-      <td><a href="https://github.com/ryojitanabe/reproblems">https://github.com/ryojitanabe/reproblems</a></td>
-      <td>Real-world inspired multi-objective optimization problem suite</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_cuter">
-      <td>suite_cuter</td>
-      <td>CUTEr</td>
-      <td>Suite</td>
-      <td>binary | continuous | integer</td>
-      <td>>=3</td>
-      <td>1</td>
-      <td></td>
-      <td>unknown</td>
-      <td>>=1</td>
-      <td></td>
-      <td></td>
-      <td>no</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>A constrained and unconstrained testing environment.</td>
-      <td></td>
-      <td>CUTEr, <a href="https://dl.acm.org/doi/10.1145/962437.962439">https://dl.acm.org/doi/10.1145/962437.962439</a></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>artificial</td>
-      <td>>=1</td>
-      <td></td>
-      <td>>=1</td>
-      <td>>=1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_cutest">
-      <td>suite_cutest</td>
-      <td>CUTEst</td>
-      <td>Suite</td>
-      <td>binary | continuous | integer</td>
-      <td>>=3</td>
-      <td>1</td>
-      <td></td>
-      <td>unknown | box</td>
-      <td>>=2</td>
-      <td></td>
-      <td></td>
-      <td>no</td>
-      <td></td>
-      <td></td>
-      <td>Constrained and Unconstrained Testing Environment with safe threads</td>
-      <td>CUTEst for optimization software</td>
-      <td></td>
-      <td>CUTEst, <a href="https://link.springer.com/article/10.1007/s10589-014-9687-3">https://link.springer.com/article/10.1007/s10589-014-9687-3</a></td>
-      <td>impl_pycutest</td>
-      <td>multimodal</td>
-      <td></td>
-      <td>artificial</td>
-      <td>>=1</td>
-      <td></td>
-      <td>>=1</td>
-      <td>>=1</td>
-      <td>pycutest</td>
-      <td>Python / C++ / Fortran</td>
-      <td></td>
-      <td><a href="https://github.com/jfowkes/pycutest">https://github.com/jfowkes/pycutest</a></td>
-      <td>Python interface to CUTEst</td>
-      <td></td>
-      <td>>=1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_dtlz">
-      <td>suite_dtlz</td>
-      <td>DTLZ</td>
-      <td>Suite</td>
-      <td>continuous</td>
-      <td>>=1</td>
-      <td>[10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>Scalable multi-objective optimization test problems, Kalyanmoy Deb; Lothar Thiele; Marco Laumanns; Eckart Zitzler, <a href="https://doi.org/10.1109/CEC.2002.1007032">https://doi.org/10.1109/CEC.2002.1007032</a></td>
-      <td>impl_pymoo</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>>=1</td>
-      <td></td>
-      <td>pymoo</td>
-      <td>Python</td>
-      <td></td>
-      <td><a href="https://github.com/anyoptimization/pymoo">https://github.com/anyoptimization/pymoo</a></td>
-      <td>Multi-objective optimization in Python</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_dynamicbinval">
-      <td>suite_dynamicbinval</td>
-      <td>DynamicBinVal</td>
-      <td>Suite</td>
-      <td>binary</td>
-      <td>>=1</td>
-      <td>1</td>
-      <td>dynamic</td>
-      <td></td>
-      <td></td>
-      <td>dynamic</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>Four versions of the dynamic binary value problem</td>
-      <td></td>
-      <td>DynamicBinVal, <a href="https://arxiv.org/pdf/2404.15837">https://arxiv.org/pdf/2404.15837</a></td>
-      <td>impl_iohexperimenter</td>
-      <td></td>
-      <td></td>
-      <td>artificial</td>
-      <td>>=1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>IOHexperimenter</td>
-      <td>C++/Python</td>
-      <td></td>
-      <td><a href="https://github.com/IOHprofiler/IOHexperimenter">https://github.com/IOHprofiler/IOHexperimenter</a></td>
-      <td>IOHprofiler experimenter framework</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_emo2017">
-      <td>suite_emo2017</td>
-      <td>EMO2017</td>
-      <td>Suite</td>
-      <td>continuous</td>
-      <td>4-24</td>
-      <td>2</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>BBComp EMO 2017, <a href="https://www.ini.rub.de/PEOPLE/glasmtbl/projects/bbcomp/">https://www.ini.rub.de/PEOPLE/glasmtbl/projects/bbcomp/</a></td>
-      <td>impl_emo2017</td>
-      <td></td>
-      <td></td>
-      <td>real-world</td>
-      <td></td>
-      <td></td>
-      <td>4-24</td>
-      <td></td>
-      <td>EMO 2017 real-world problems</td>
-      <td></td>
-      <td></td>
-      <td><a href="https://www.ini.rub.de/PEOPLE/glasmtbl/projects/bbcomp/downloads/realworld-problems-bbcomp-EMO-2017.zip">https://www.ini.rub.de/PEOPLE/glasmtbl/projects/bbcomp/downloads/realworld-problems-bbcomp-EMO-2017.zip</a></td>
-      <td>BBComp EMO-2017 real-world problem archive</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_etmof">
-      <td>suite_etmof</td>
-      <td>ETMOF</td>
-      <td>Suite</td>
-      <td>continuous</td>
-      <td>25-10000</td>
-      <td>[10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 2, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 3, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 4, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 5, 50, 6, 7, 8, 9]</td>
-      <td>dynamic</td>
-      <td></td>
-      <td></td>
-      <td>dynamic</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>Evolutionary many-task optimization framework, <a href="https://doi.org/10.48550/arXiv.2110.08033">https://doi.org/10.48550/arXiv.2110.08033</a></td>
-      <td>impl_etmof</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>25-10000</td>
-      <td></td>
-      <td>ETMOF</td>
-      <td></td>
-      <td></td>
-      <td><a href="https://github.com/songbai-liu/etmo">https://github.com/songbai-liu/etmo</a></td>
-      <td>Evolutionary many-task optimization framework</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_expobench">
-      <td>suite_expobench</td>
-      <td>EXPObench</td>
-      <td>Suite</td>
-      <td>categorical | continuous | integer</td>
-      <td>30-405</td>
-      <td>1</td>
-      <td>noisy</td>
-      <td>unknown | box</td>
-      <td>>=2</td>
-      <td></td>
-      <td>["observational", "real-life"]</td>
-      <td>no</td>
-      <td></td>
-      <td></td>
-      <td>EXPensive Optimization benchmark library</td>
-      <td>Wind farm layout optimization, gas filter design, pipe shape optimization, hyperparameter tuning, and hospital simulation</td>
-      <td></td>
-      <td>EXPObench, <a href="https://doi.org/10.1016/j.asoc.2023.110744">https://doi.org/10.1016/j.asoc.2023.110744</a></td>
-      <td>impl_expobench</td>
-      <td></td>
-      <td></td>
-      <td>real-world</td>
-      <td></td>
-      <td>10-135</td>
-      <td>10-135</td>
-      <td>10-135</td>
-      <td>EXPObench</td>
-      <td>Python</td>
-      <td>2 to 80 seconds</td>
-      <td><a href="https://github.com/AlgTUDelft/ExpensiveOptimBenchmark">https://github.com/AlgTUDelft/ExpensiveOptimBenchmark</a></td>
-      <td>EXPensive Optimization benchmark library (wind farm layout, gas filter design, pipe shape, hyperparameter tuning, hospital simulation)</td>
-      <td></td>
-      <td>>=1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_gbea">
-      <td>suite_gbea</td>
-      <td>GBEA</td>
-      <td>Suite</td>
-      <td>continuous</td>
-      <td>>=1</td>
-      <td>[1, 2]</td>
-      <td>noisy</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>noisy</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>expensive evaluations 5s-35s, RW-GAN-Mario and TopTrumps are part of GBEA</td>
-      <td></td>
-      <td>Game benchmark for evolutionary algorithms, <a href="https://doi.org/10.1145/3321707.3321805">https://doi.org/10.1145/3321707.3321805</a></td>
-      <td>impl_gbea</td>
-      <td>multimodal</td>
-      <td></td>
-      <td>real-world</td>
-      <td></td>
-      <td></td>
-      <td>>=1</td>
-      <td></td>
-      <td>coco-gbea</td>
-      <td></td>
-      <td>5s-35s</td>
-      <td><a href="https://github.com/ttusar/coco-gbea">https://github.com/ttusar/coco-gbea</a></td>
-      <td>Game-Benchmark for Evolutionary Algorithms (COCO fork)</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_gnbg">
-      <td>suite_gnbg</td>
-      <td>GNBG</td>
-      <td>Suite</td>
-      <td>continuous</td>
-      <td>>=1</td>
-      <td>1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>Generalized Numerical Benchmark Generator</td>
-      <td></td>
-      <td>GNBG, <a href="https://arxiv.org/abs/2312.07083">https://arxiv.org/abs/2312.07083</a></td>
-      <td>impl_gnbg</td>
-      <td></td>
-      <td></td>
-      <td>artificial</td>
-      <td></td>
-      <td></td>
-      <td>>=1</td>
-      <td></td>
-      <td>GNBG Generator</td>
-      <td></td>
-      <td></td>
-      <td><a href="https://github.com/Danial-Yazdani/GNBG-Generator">https://github.com/Danial-Yazdani/GNBG-Generator</a></td>
-      <td>Generalized Numerical Benchmark Generator</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_gnbg_ii">
-      <td>suite_gnbg_ii</td>
-      <td>GNBG-II</td>
-      <td>Suite</td>
-      <td>continuous</td>
-      <td>>=1</td>
-      <td>1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>Generalized Numerical Benchmark Generator (version 2). Also available in IOH.</td>
-      <td></td>
-      <td>GNBG-II, <a href="https://dl.acm.org/doi/pdf/10.1145/3712255.3734271">https://dl.acm.org/doi/pdf/10.1145/3712255.3734271</a></td>
-      <td>["impl_gnbg_ii", "impl_iohgnbg"]</td>
-      <td></td>
-      <td></td>
-      <td>artificial</td>
-      <td></td>
-      <td></td>
-      <td>>=1</td>
-      <td></td>
-      <td>GNBG-II | IOHGNBG</td>
-      <td></td>
-      <td></td>
-      <td><a href="https://github.com/rohitsalgotra/GNBG-II">https://github.com/rohitsalgotra/GNBG-II</a> | <a href="https://github.com/IOHprofiler/IOHGNBG">https://github.com/IOHprofiler/IOHGNBG</a></td>
-      <td>Generalized Numerical Benchmark Generator version 2 | IOHprofiler version of GNBG</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_iohclustering">
-      <td>suite_iohclustering</td>
-      <td>IOHClustering</td>
-      <td>Suite</td>
-      <td>continuous</td>
-      <td>>=1</td>
-      <td>1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>Set of benchmark problems from clustering: optimization task is selecting cluster centers for a given set of data.</td>
-      <td></td>
-      <td>IOHClustering, <a href="https://arxiv.org/pdf/2505.09233">https://arxiv.org/pdf/2505.09233</a></td>
-      <td>impl_iohclustering</td>
-      <td>multimodal</td>
-      <td></td>
-      <td>artificial-from-real-data</td>
-      <td></td>
-      <td></td>
-      <td>>=1</td>
-      <td></td>
-      <td>IOHClustering</td>
-      <td></td>
-      <td></td>
-      <td><a href="https://github.com/IOHprofiler/IOHClustering">https://github.com/IOHprofiler/IOHClustering</a></td>
-      <td>Clustering-based optimization benchmark built on ML datasets</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_kinematics_robotarm">
-      <td>suite_kinematics_robotarm</td>
-      <td>KinematicsRobotArm</td>
-      <td>Suite</td>
-      <td>continuous</td>
-      <td>21</td>
-      <td>1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>Kinematics of a robot arm, <a href="https://doi.org/10.1023/A:1013258808932">https://doi.org/10.1023/A:1013258808932</a></td>
-      <td>impl_transfer_rf_bbob_rw</td>
-      <td>unimodal</td>
-      <td></td>
-      <td>real-world</td>
-      <td></td>
-      <td></td>
-      <td>21</td>
-      <td></td>
-      <td>Transfer Random Forests BBOB Real-world</td>
-      <td></td>
-      <td></td>
-      <td><a href="https://github.com/ShuaiqunPan/Transfer_Random_forests_BBOB_Real_world">https://github.com/ShuaiqunPan/Transfer_Random_forests_BBOB_Real_world</a></td>
-      <td>Real-world BBOB-like problem implementations (Porkchop, KinematicsRobotArm)</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_l1_zdt">
-      <td>suite_l1_zdt</td>
-      <td>L1-ZDT</td>
-      <td>Suite</td>
-      <td>binary | continuous</td>
-      <td>>=2</td>
-      <td>2</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>Variant of ZDT with linkages between variables within groups</td>
-      <td></td>
-      <td>Linkage ZDT/DTLZ variants, <a href="https://doi.org/10.1145/1143997.1144179">https://doi.org/10.1145/1143997.1144179</a></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>>=1</td>
-      <td></td>
-      <td>>=1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_l2_dtlz">
-      <td>suite_l2_dtlz</td>
-      <td>L2-DTLZ</td>
-      <td>Suite</td>
-      <td>continuous</td>
-      <td>>=1</td>
-      <td>[10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>Variant of DTLZ2/DTLZ3 with linkages between all variables</td>
-      <td></td>
-      <td>Linkage ZDT/DTLZ variants, <a href="https://doi.org/10.1145/1143997.1144179">https://doi.org/10.1145/1143997.1144179</a></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>>=1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_l2_zdt">
-      <td>suite_l2_zdt</td>
-      <td>L2-ZDT</td>
-      <td>Suite</td>
-      <td>binary | continuous</td>
-      <td>>=2</td>
-      <td>2</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>Variant of ZDT with linkages between all variables</td>
-      <td></td>
-      <td>Linkage ZDT/DTLZ variants, <a href="https://doi.org/10.1145/1143997.1144179">https://doi.org/10.1145/1143997.1144179</a></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>>=1</td>
-      <td></td>
-      <td>>=1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_l3_dtlz">
-      <td>suite_l3_dtlz</td>
-      <td>L3-DTLZ</td>
-      <td>Suite</td>
-      <td>continuous</td>
-      <td>>=1</td>
-      <td>[10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>Variant of L2-DTLZ with anti-linkage mapping</td>
-      <td></td>
-      <td>Linkage ZDT/DTLZ variants, <a href="https://doi.org/10.1145/1143997.1144179">https://doi.org/10.1145/1143997.1144179</a></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>>=1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_l3_zdt">
-      <td>suite_l3_zdt</td>
-      <td>L3-ZDT</td>
-      <td>Suite</td>
-      <td>binary | continuous</td>
-      <td>>=2</td>
-      <td>2</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>Variant of L2-ZDT with anti-linkage mapping</td>
-      <td></td>
-      <td>Linkage ZDT/DTLZ variants, <a href="https://doi.org/10.1145/1143997.1144179">https://doi.org/10.1145/1143997.1144179</a></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>>=1</td>
-      <td></td>
-      <td>>=1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_maop">
-      <td>suite_maop</td>
-      <td>MaOP</td>
-      <td>Suite</td>
-      <td>continuous</td>
-      <td>>=1</td>
-      <td>[10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
-      <td>noisy</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>unknown</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>MaOP benchmark, <a href="https://doi.org/10.1016/j.swevo.2019.02.003">https://doi.org/10.1016/j.swevo.2019.02.003</a></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>>=1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_mechbench">
-      <td>suite_mechbench</td>
-      <td>MECHBench</td>
-      <td>Suite</td>
-      <td>continuous</td>
-      <td>>=1</td>
-      <td>1</td>
-      <td></td>
-      <td>unknown</td>
-      <td>{1, 2}</td>
-      <td></td>
-      <td></td>
-      <td>no</td>
-      <td></td>
-      <td></td>
-      <td>MECHBench</td>
-      <td>Set of problems inspired by Structural Mechanics Design Optimization. Embeds physical simulations (plasticity only, no fracture/damage). Unstructured/non-isotropic multimodality.</td>
-      <td></td>
-      <td>MECHBench, <a href="https://arxiv.org/abs/2511.10821">https://arxiv.org/abs/2511.10821</a></td>
-      <td>impl_mechbench</td>
-      <td>multimodal</td>
-      <td></td>
-      <td>real-world</td>
-      <td></td>
-      <td></td>
-      <td>>=1</td>
-      <td></td>
-      <td>MECHBench</td>
-      <td>Python</td>
-      <td>1-7 minutes</td>
-      <td><a href="https://github.com/BayesOptApp/MECHBench">https://github.com/BayesOptApp/MECHBench</a></td>
-      <td>Structural mechanics design optimization benchmark</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_mf2">
-      <td>suite_mf2</td>
-      <td>MF2</td>
-      <td>Suite</td>
-      <td>continuous</td>
-      <td>>=1</td>
-      <td>1</td>
-      <td>multi-fidelity</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>[1, 2]</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>mf2: a collection of multi-fidelity benchmark functions in Python, <a href="https://doi.org/10.21105/joss.02049">https://doi.org/10.21105/joss.02049</a></td>
-      <td>impl_mf2</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>>=1</td>
-      <td></td>
-      <td>mf2</td>
-      <td>Python</td>
-      <td></td>
-      <td><a href="https://github.com/sjvrijn/mf2">https://github.com/sjvrijn/mf2</a></td>
-      <td>Multi-fidelity test function collection</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_minus_dtlz">
-      <td>suite_minus_dtlz</td>
-      <td>Minus DTLZ</td>
-      <td>Suite</td>
-      <td>continuous</td>
-      <td>>=1</td>
-      <td>[10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>Variant of DTLZ that minimises the inverse of the base DTLZ functions</td>
-      <td></td>
-      <td>Minus DTLZ / Minus WFG, <a href="https://doi.org/10.1109/TEVC.2016.2587749">https://doi.org/10.1109/TEVC.2016.2587749</a></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>>=1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_minus_wfg">
-      <td>suite_minus_wfg</td>
-      <td>Minus WFG</td>
-      <td>Suite</td>
-      <td>continuous</td>
-      <td>>=1</td>
-      <td>[10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>Variant of WFG that minimises the inverse of the base WFG functions</td>
-      <td></td>
-      <td>Minus DTLZ / Minus WFG, <a href="https://doi.org/10.1109/TEVC.2016.2587749">https://doi.org/10.1109/TEVC.2016.2587749</a></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>>=1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_mmopp">
-      <td>suite_mmopp</td>
-      <td>MMOPP</td>
-      <td>Suite</td>
-      <td>unknown</td>
-      <td>0</td>
-      <td>[2, 3, 4, 5, 6, 7]</td>
-      <td></td>
-      <td>unknown</td>
-      <td>>=1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>MMOPP technical report, <a href="http://www5.zzu.edu.cn/system/_content/download.jsp?urltype=news.DownloadAttachUrl&owner=1327567121&wbfileid=4764412">http://www5.zzu.edu.cn/system/_content/download.jsp?urltype=news.DownloadAttachUrl&owner=1327567121&wbfileid=4764412</a></td>
-      <td>impl_mmopp</td>
-      <td>multimodal</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>MMOPP</td>
-      <td></td>
-      <td></td>
-      <td><a href="http://www5.zzu.edu.cn/ecilab/info/1036/1251.htm">http://www5.zzu.edu.cn/ecilab/info/1036/1251.htm</a></td>
-      <td>ECI lab distribution page for MMOPP</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_modact">
-      <td>suite_modact</td>
-      <td>MODAct</td>
-      <td>Suite</td>
-      <td>continuous | integer</td>
-      <td>40</td>
-      <td>[2, 3, 4, 5]</td>
-      <td></td>
-      <td>unknown</td>
-      <td>>=1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>multiobjective design of actuators</td>
-      <td>Realistic Constrained Multi-Objective Optimization Benchmark Problems from Design.</td>
-      <td></td>
-      <td>MODAct, <a href="https://doi.org/10.1109/TEVC.2020.3020046">https://doi.org/10.1109/TEVC.2020.3020046</a></td>
-      <td>["impl_modact", "impl_pymoo"]</td>
-      <td></td>
-      <td></td>
-      <td>real-world</td>
-      <td></td>
-      <td></td>
-      <td>20</td>
-      <td>20</td>
-      <td>modact | pymoo</td>
-      <td>Python</td>
-      <td>20ms</td>
-      <td><a href="https://github.com/epfl-lamd/modact">https://github.com/epfl-lamd/modact</a> | <a href="https://github.com/anyoptimization/pymoo">https://github.com/anyoptimization/pymoo</a></td>
-      <td>EPFL-LAMD modact package | Multi-objective optimization in Python</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_morepo">
-      <td>suite_morepo</td>
-      <td>MOrepo</td>
-      <td>Suite</td>
-      <td>unknown</td>
-      <td>0</td>
-      <td>2</td>
-      <td>dynamic | noisy</td>
-      <td>unknown</td>
-      <td>>=1</td>
-      <td>unknown</td>
-      <td>unknown</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>impl_morepo</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>MOrepo</td>
-      <td></td>
-      <td></td>
-      <td><a href="https://github.com/MCDMSociety/MOrepo">https://github.com/MCDMSociety/MOrepo</a></td>
-      <td>Multi-objective optimisation problem repository</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_pbo">
-      <td>suite_pbo</td>
-      <td>PBO</td>
-      <td>Suite</td>
-      <td>binary</td>
-      <td>>=1</td>
-      <td>1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>Suite of 25 binary optimization problems</td>
-      <td></td>
-      <td>PBO benchmarks, <a href="https://dl.acm.org/doi/pdf/10.1145/3319619.3326810">https://dl.acm.org/doi/pdf/10.1145/3319619.3326810</a></td>
-      <td>impl_iohexperimenter</td>
-      <td></td>
-      <td></td>
-      <td>artificial</td>
-      <td>>=1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>IOHexperimenter</td>
-      <td>C++/Python</td>
-      <td></td>
-      <td><a href="https://github.com/IOHprofiler/IOHexperimenter">https://github.com/IOHprofiler/IOHexperimenter</a></td>
-      <td>IOHprofiler experimenter framework</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_porkchop">
-      <td>suite_porkchop</td>
-      <td>PorkchopPlotInterplanetaryTrajectory</td>
-      <td>Suite</td>
-      <td>continuous</td>
-      <td>2</td>
-      <td>1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>Porkchop plot interplanetary trajectory benchmark, <a href="https://doi.org/10.1109/CEC65147.2025.11042973">https://doi.org/10.1109/CEC65147.2025.11042973</a></td>
-      <td>impl_transfer_rf_bbob_rw</td>
-      <td>multimodal</td>
-      <td></td>
-      <td>real-world</td>
-      <td></td>
-      <td></td>
-      <td>2</td>
-      <td></td>
-      <td>Transfer Random Forests BBOB Real-world</td>
-      <td></td>
-      <td></td>
-      <td><a href="https://github.com/ShuaiqunPan/Transfer_Random_forests_BBOB_Real_world">https://github.com/ShuaiqunPan/Transfer_Random_forests_BBOB_Real_world</a></td>
-      <td>Real-world BBOB-like problem implementations (Porkchop, KinematicsRobotArm)</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_re">
-      <td>suite_re</td>
-      <td>RE</td>
-      <td>Suite</td>
-      <td>continuous | integer</td>
-      <td>4-14</td>
-      <td>[2, 3, 4, 5, 6, 7, 8, 9]</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>Easy-to-evaluate real-world multi-objective optimization problems, Ryoji Tanabe; Hisao Ishibuchi, <a href="https://doi.org/10.1016/j.asoc.2020.106078">https://doi.org/10.1016/j.asoc.2020.106078</a></td>
-      <td>impl_reproblems</td>
-      <td></td>
-      <td></td>
-      <td>real-world-like</td>
-      <td></td>
-      <td></td>
-      <td>2-7</td>
-      <td>2-7</td>
-      <td>reproblems</td>
-      <td>Python</td>
-      <td></td>
-      <td><a href="https://github.com/ryojitanabe/reproblems">https://github.com/ryojitanabe/reproblems</a></td>
-      <td>Real-world inspired multi-objective optimization problem suite</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_rwmvop">
-      <td>suite_rwmvop</td>
-      <td>RWMVOP</td>
-      <td>Suite</td>
-      <td>integer | continuous | categorical</td>
-      <td>>=3</td>
-      <td>1</td>
-      <td></td>
-      <td>unknown</td>
-      <td>>=1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>RWMVOP, <a href="https://doi.org/10.1109/TEVC.2013.2281531">https://doi.org/10.1109/TEVC.2013.2281531</a></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>real-world</td>
-      <td></td>
-      <td>>=1</td>
-      <td>>=1</td>
-      <td>>=1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_sbox_cost">
-      <td>suite_sbox_cost</td>
-      <td>SBOX-COST</td>
-      <td>Suite</td>
-      <td>continuous</td>
-      <td>>=1</td>
-      <td>1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>problems from BBOB but allows instances with the optimum close to the boundary</td>
-      <td></td>
-      <td>SBOX-COST, <a href="https://doi.org/10.48550/arXiv.2305.12221">https://doi.org/10.48550/arXiv.2305.12221</a></td>
-      <td>impl_iohexperimenter</td>
-      <td>multimodal</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>>=1</td>
-      <td></td>
-      <td>IOHexperimenter</td>
-      <td>C++/Python</td>
-      <td></td>
-      <td><a href="https://github.com/IOHprofiler/IOHexperimenter">https://github.com/IOHprofiler/IOHexperimenter</a></td>
-      <td>IOHprofiler experimenter framework</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_sdp">
-      <td>suite_sdp</td>
-      <td>SDP</td>
-      <td>Suite</td>
-      <td>continuous</td>
-      <td>>=1</td>
-      <td>[10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
-      <td>dynamic | noisy</td>
-      <td></td>
-      <td></td>
-      <td>dynamic</td>
-      <td>unknown</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>SDP dynamic multi-objective benchmark, <a href="https://doi.org/10.1109/TCYB.2019.2896021">https://doi.org/10.1109/TCYB.2019.2896021</a></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>>=1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_submodular">
-      <td>suite_submodular</td>
-      <td>Submodular Optimization</td>
-      <td>Suite</td>
-      <td>binary</td>
-      <td>>=1</td>
-      <td>1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>set of graph-based submodular optimization problems from 4 problem types</td>
-      <td></td>
-      <td>Submodular optimization benchmark, <a href="https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10254181">https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10254181</a></td>
-      <td>impl_iohexperimenter</td>
-      <td></td>
-      <td></td>
-      <td>artificial</td>
-      <td>>=1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>IOHexperimenter</td>
-      <td>C++/Python</td>
-      <td></td>
-      <td><a href="https://github.com/IOHprofiler/IOHexperimenter">https://github.com/IOHprofiler/IOHexperimenter</a></td>
-      <td>IOHprofiler experimenter framework</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_tulipa_energy">
-      <td>suite_tulipa_energy</td>
-      <td>TulipaEnergy</td>
-      <td>Suite</td>
-      <td>continuous</td>
-      <td>>=1</td>
-      <td>1</td>
-      <td>noisy | multi-fidelity</td>
-      <td>unknown</td>
-      <td>>=2</td>
-      <td></td>
-      <td>parameter</td>
-      <td></td>
-      <td></td>
-      <td>[1, 2]</td>
-      <td>TulipaEnergyModel.jl</td>
-      <td>Determine the optimal investment and operation decisions for different assets in the energy system (production, consumption, conversion, storage, transport) while minimizing loss of load. Modelled as a potentially very large linear program with multiple fidelity levels.</td>
-      <td></td>
-      <td>TulipaEnergyModel.jl scientific references, <a href="https://tulipaenergy.github.io/TulipaEnergyModel.jl/stable/40-scientific-foundation/45-scientific-references">https://tulipaenergy.github.io/TulipaEnergyModel.jl/stable/40-scientific-foundation/45-scientific-references</a></td>
-      <td>impl_tulipa</td>
-      <td>unimodal</td>
-      <td></td>
-      <td>real-world</td>
-      <td></td>
-      <td></td>
-      <td>>=1</td>
-      <td></td>
-      <td>TulipaEnergyModel.jl</td>
-      <td>Julia / JuMP</td>
-      <td>minutes to hours</td>
-      <td><a href="https://tulipaenergy.github.io/TulipaEnergyModel.jl/stable/">https://tulipaenergy.github.io/TulipaEnergyModel.jl/stable/</a> | <a href="https://github.com/TulipaEnergy/Tulipa-OBZ-CaseStudy">https://github.com/TulipaEnergy/Tulipa-OBZ-CaseStudy</a></td>
-      <td>Large linear program for optimal investment and operation of energy systems</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_vehicle_dynamics">
-      <td>suite_vehicle_dynamics</td>
-      <td>VehicleDynamics</td>
-      <td>Suite</td>
-      <td>continuous</td>
-      <td>2</td>
-      <td>1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>VehicleDynamics benchmark, <a href="https://www.scitepress.org/Papers/2023/121580/121580.pdf">https://www.scitepress.org/Papers/2023/121580/121580.pdf</a></td>
-      <td>impl_vehicle_dynamics</td>
-      <td>multimodal</td>
-      <td></td>
-      <td>real-world</td>
-      <td></td>
-      <td></td>
-      <td>2</td>
-      <td></td>
-      <td>VehicleDynamics (Zenodo)</td>
-      <td></td>
-      <td></td>
-      <td><a href="https://zenodo.org/records/8307853">https://zenodo.org/records/8307853</a></td>
-      <td>Zenodo archive for the vehicle dynamics benchmark</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_wfg">
-      <td>suite_wfg</td>
-      <td>WFG</td>
-      <td>Suite</td>
-      <td>continuous</td>
-      <td>>=1</td>
-      <td>[10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>A review of multiobjective test problems and a scalable test problem toolkit, Simon Huband; Philip Hingston; Luigi Barone; Lyndon While, <a href="https://doi.org/10.1109/TEVC.2005.861417">https://doi.org/10.1109/TEVC.2005.861417</a></td>
-      <td>impl_pymoo</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>>=1</td>
-      <td></td>
-      <td>pymoo</td>
-      <td>Python</td>
-      <td></td>
-      <td><a href="https://github.com/anyoptimization/pymoo">https://github.com/anyoptimization/pymoo</a></td>
-      <td>Multi-objective optimization in Python</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_zdt">
-      <td>suite_zdt</td>
-      <td>ZDT</td>
-      <td>Suite</td>
-      <td>binary | continuous</td>
-      <td>>=2</td>
-      <td>2</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>Comparison of multiobjective evolutionary algorithms: empirical results, Eckart Zitzler; Kalyanmoy Deb; Lothar Thiele, <a href="https://doi.org/10.1162/106365600568202">https://doi.org/10.1162/106365600568202</a></td>
-      <td>impl_pymoo</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>>=1</td>
-      <td></td>
-      <td>>=1</td>
-      <td></td>
-      <td>pymoo</td>
-      <td>Python</td>
-      <td></td>
-      <td><a href="https://github.com/anyoptimization/pymoo">https://github.com/anyoptimization/pymoo</a></td>
-      <td>Multi-objective optimization in Python</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-  </tbody>
-<tfoot><tr><th>ID</th> <th>Name</th> <th>Type</th> <th>Variable Types</th> <th>Total Variables</th> <th>Objectives</th> <th>Properties</th> <th>Constraint Types</th> <th>Total Constraints</th> <th>Dynamics</th> <th>Noise</th> <th>Partial Evaluations</th> <th>Independent Objectives</th> <th>Fidelity Levels</th> <th>Full Name</th> <th>Description</th> <th>Tags</th> <th>References</th> <th>Implementations</th> <th>Modality</th> <th>Examples</th> <th>Source</th> <th>Binary Vars</th> <th>Categorical Vars</th> <th>Continuous Vars</th> <th>Integer Vars</th> <th>Implementation Names</th> <th>Implementation Languages</th> <th>Implementation Evaluation Times</th> <th>Implementation Links</th> <th>Implementation Descriptions</th> <th>Implementation Requirements</th> <th>Hard Box Constraints</th> <th>Soft Box Constraints</th> <th>Hard Linear Constraints</th> <th>Soft Linear Constraints</th> <th>Hard Function Constraints</th> <th>Soft Function Constraints</th></tr></tfoot></table>
-    </div>
-</section>
-
-<section class="details-shell" id="problem-details" aria-live="polite">
-    <h3 class="details-title">Problem details</h3>
-    <p class="details-hint" id="problem-details-hint">Click a table row to inspect full details.</p>
-    <dl class="details-grid" id="problem-details-content"></dl>
-    <div class="snippet-shell" id="problem-snippet-shell">
-        <h4 class="snippet-title">README.md</h4>
-        <p class="snippet-hint" id="problem-readme-hint">README content for this snippet folder will appear here.</p>
-        <div class="snippet-readme markdown-body" id="problem-readme-content"></div>
-        <h4 class="snippet-title">Snippet: <code id="problem-snippet-path">call_{problem_id}.py</code></h4>
-        <p class="snippet-meta"><a id="problem-snippet-folder-link" href="#" target="_blank" rel="noopener noreferrer">Open snippet folder</a></p>
-        <p class="snippet-hint" id="problem-snippet-hint">Select a problem to check for an available code snippet.</p>
-        <pre class="snippet-code"><code id="problem-snippet-content"></code></pre>
-    </div>
-</section>
+<section class="table-shell">
+    <div class="table-toolbar">
+        <details class="column-toolbar-disclosure">
+            <summary class="toolbar-title">Toggle Visible Columns</summary>
+            <div class="toolbar-actions">
+                <button type="button" class="toolbar-btn" id="show-all-columns">Show all</button>
+                <button type="button" class="toolbar-btn" id="hide-all-columns">Hide all</button>
+            </div>
+            <div class="column-controls">
+                <label class="column-chip"><input class="col-toggle" type="checkbox" data-column="0"><span>ID</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="1" checked><span>Name</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="2" checked><span>Type</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="3" checked><span>Variable Types</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="4" checked><span>Total Variables</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="5" checked><span>Objectives</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="6" checked><span>Properties</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="7" checked><span>Constraint Types</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="8" checked><span>Total Constraints</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="9" checked><span>Dynamics</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="10" checked><span>Noise</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="11" checked><span>Partial Evaluations</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="12" checked><span>Independent Objectives</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="13" checked><span>Fidelity Levels</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="14"><span>Full Name</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="15"><span>Description</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="16"><span>Tags</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="17"><span>References</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="18"><span>Implementations</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="19"><span>Modality</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="20"><span>Examples</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="21"><span>Source</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="22"><span>Binary Vars</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="23"><span>Categorical Vars</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="24"><span>Continuous Vars</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="25"><span>Integer Vars</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="26"><span>Implementation Names</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="27"><span>Implementation Languages</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="28"><span>Implementation Evaluation Times</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="29"><span>Implementation Links</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="30"><span>Implementation Descriptions</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="31"><span>Implementation Requirements</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="32"><span>Hard Box Constraints</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="33"><span>Soft Box Constraints</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="34"><span>Hard Linear Constraints</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="35"><span>Soft Linear Constraints</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="36"><span>Hard Function Constraints</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="37"><span>Soft Function Constraints</span></label>
+            </div>
+        </details>
+    </div>
+    <div class="table-wrap">
+        <table class="dataframe display compact display styled-table" id="problems">
+  <thead>
+    <tr style="text-align: right;">
+      <th>ID</th>
+      <th>Name</th>
+      <th>Type</th>
+      <th>Variable Types</th>
+      <th>Total Variables</th>
+      <th>Objectives</th>
+      <th>Properties</th>
+      <th>Constraint Types</th>
+      <th>Total Constraints</th>
+      <th>Dynamics</th>
+      <th>Noise</th>
+      <th>Partial Evaluations</th>
+      <th>Independent Objectives</th>
+      <th>Fidelity Levels</th>
+      <th>Full Name</th>
+      <th>Description</th>
+      <th>Tags</th>
+      <th>References</th>
+      <th>Implementations</th>
+      <th>Modality</th>
+      <th>Examples</th>
+      <th>Source</th>
+      <th>Binary Vars</th>
+      <th>Categorical Vars</th>
+      <th>Continuous Vars</th>
+      <th>Integer Vars</th>
+      <th>Implementation Names</th>
+      <th>Implementation Languages</th>
+      <th>Implementation Evaluation Times</th>
+      <th>Implementation Links</th>
+      <th>Implementation Descriptions</th>
+      <th>Implementation Requirements</th>
+      <th>Hard Box Constraints</th>
+      <th>Soft Box Constraints</th>
+      <th>Hard Linear Constraints</th>
+      <th>Soft Linear Constraints</th>
+      <th>Hard Function Constraints</th>
+      <th>Soft Function Constraints</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr data-problem-id="fn_ato">
+      <td>fn_ato</td>
+      <td>ATO</td>
+      <td>Problem</td>
+      <td>continuous</td>
+      <td>10</td>
+      <td>2</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>no</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Parameters of the Modules of the Automatic Train Operation are optimized; two objectives: minimizing energy consumption and minimizing driving duration.</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>unimodal</td>
+      <td></td>
+      <td>real-world</td>
+      <td></td>
+      <td></td>
+      <td>10</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="fn_building_spatial">
+      <td>fn_building_spatial</td>
+      <td>Building spatial design</td>
+      <td>Problem</td>
+      <td>continuous | binary</td>
+      <td>>=2</td>
+      <td>2</td>
+      <td></td>
+      <td>box | unknown</td>
+      <td>>=2</td>
+      <td></td>
+      <td></td>
+      <td>no</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Optimise the spatial layout of a building to minimise energy consumption for climate control and minimise the strain on the structure. Many hard constraints; mixed-variable (continuous+binary); expensive evaluations.</td>
+      <td></td>
+      <td>Building spatial design, <a href="https://hdl.handle.net/1887/81789">https://hdl.handle.net/1887/81789</a></td>
+      <td>impl_bso_toolbox</td>
+      <td></td>
+      <td></td>
+      <td>real-world</td>
+      <td>>=1</td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>BSO-toolbox</td>
+      <td>C++</td>
+      <td>~1s (smallest) to ~40s (larger)</td>
+      <td><a href="https://github.com/TUe-excellent-buildings/BSO-toolbox">https://github.com/TUe-excellent-buildings/BSO-toolbox</a></td>
+      <td>Building Spatial Design toolbox (TU/e)</td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="fn_convex_dtlz2">
+      <td>fn_convex_dtlz2</td>
+      <td>Convex DTLZ2</td>
+      <td>Problem</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>[10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Variant of DTLZ2 with a convex Pareto front (instead of concave)</td>
+      <td></td>
+      <td>Convex DTLZ2, <a href="https://doi.org/10.1109/TEVC.2013.2281535">https://doi.org/10.1109/TEVC.2013.2281535</a></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="fn_emdo">
+      <td>fn_emdo</td>
+      <td>Electric Motor Design Optimization</td>
+      <td>Problem</td>
+      <td>continuous | integer</td>
+      <td>26</td>
+      <td>1</td>
+      <td>noisy</td>
+      <td>unknown | box</td>
+      <td>>=14</td>
+      <td></td>
+      <td>noisy</td>
+      <td>no</td>
+      <td></td>
+      <td></td>
+      <td>Electric Motor Design Optimization</td>
+      <td># Goal\nFind a design of a synchronous electric motor for power steering systems that minimizes costs and satisfies all constraints.\n\n# Motivation\nChallenging to find good solutions in a limited time.\n\n# Key Challenges\n* Time-consuming solution evaluation\n* Highly-constrained problem\n* Constraints are multimodal\n\nThis is not an available problem, but could be interesting to show to researchers which difficulties appear in real-world problems.</td>
+      <td></td>
+      <td>A Multi-Step Evaluation Process in Electric Motor Design, Tea Tušar; Peter Korošec; Bogdan Filipič, <a href="https://dis.ijs.si/tea/Publications/Tusar23Multistep.pdf">https://dis.ijs.si/tea/Publications/Tusar23Multistep.pdf</a></td>
+      <td>impl_emdo</td>
+      <td>multimodal</td>
+      <td></td>
+      <td>real-world</td>
+      <td></td>
+      <td></td>
+      <td>13</td>
+      <td>13</td>
+      <td>Electric Motor Design Optimization</td>
+      <td>Python</td>
+      <td>8 minutes</td>
+      <td></td>
+      <td>Not publicly available</td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="fn_fleetopt">
+      <td>fn_fleetopt</td>
+      <td>FleetOpt</td>
+      <td>Problem</td>
+      <td>integer</td>
+      <td>{54, 13208}</td>
+      <td>1</td>
+      <td></td>
+      <td>unknown</td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td>yes</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>UK healthcare organisation fleet optimisation: reduce the fleet of non-emergency healthcare trip vehicles while still ensuring all trips can be covered. Bilevel: upper level 54 vars, lower level 13208 vars.</td>
+      <td></td>
+      <td>FleetOpt, <a href="https://dl.acm.org/doi/abs/10.1145/3638530.3664137">https://dl.acm.org/doi/abs/10.1145/3638530.3664137</a></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>real-world</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>{54, 13208}</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="fn_gasoline">
+      <td>fn_gasoline</td>
+      <td>Gasoline direct injection engine design</td>
+      <td>Problem</td>
+      <td>integer | continuous</td>
+      <td>14</td>
+      <td>2</td>
+      <td>multi-fidelity</td>
+      <td>unknown</td>
+      <td>5</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>[1, 2]</td>
+      <td></td>
+      <td>Multi-objective optimization to minimize fuel consumption and NOx emissions over a two-minute dynamic duty cycle, subject to five constraints (turbine inlet temperature, knock occurrences, peak cylinder pressure, peak cylinder pressure rise, total work). Seven decision variables cover hardware choices and engine control parameters.</td>
+      <td></td>
+      <td>Gasoline direct injection engine design, <a href="https://doi.org/10.1016/j.ejor.2022.08.032">https://doi.org/10.1016/j.ejor.2022.08.032</a></td>
+      <td>impl_gasoline</td>
+      <td></td>
+      <td></td>
+      <td>real-world</td>
+      <td></td>
+      <td></td>
+      <td>7</td>
+      <td>7</td>
+      <td>Gasoline direct injection engine design</td>
+      <td>Matlab Simulink / Wave RT</td>
+      <td></td>
+      <td><a href="https://doi.org/10.1016/j.ejor.2022.08.032">https://doi.org/10.1016/j.ejor.2022.08.032</a></td>
+      <td>Proprietary Matlab Simulink + Wave RT co-simulation</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="fn_invdeceptive_deceptive_rotell">
+      <td>fn_invdeceptive_deceptive_rotell</td>
+      <td>InverseDeceptiveTrap+RotatedEllipsoid / DeceptiveTrap+RotatedEllipsoid</td>
+      <td>Problem</td>
+      <td>continuous | binary</td>
+      <td>>=2</td>
+      <td>2</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Mixed-variable multi-objective test problems, <a href="https://doi.org/10.1145/3449726.3459521">https://doi.org/10.1145/3449726.3459521</a></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>artificial</td>
+      <td>>=1</td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="fn_inverted_dtlz1">
+      <td>fn_inverted_dtlz1</td>
+      <td>Inverted DTLZ1</td>
+      <td>Problem</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>[10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Variant of DTLZ1 with an inverted Pareto front</td>
+      <td></td>
+      <td>Inverted DTLZ1, <a href="https://doi.org/10.1109/TEVC.2013.2281534">https://doi.org/10.1109/TEVC.2013.2281534</a></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="fn_jsec2019">
+      <td>fn_jsec2019</td>
+      <td>JSEC2019</td>
+      <td>Problem</td>
+      <td>continuous</td>
+      <td>32</td>
+      <td>[1, 2, 3, 4, 5]</td>
+      <td></td>
+      <td>unknown</td>
+      <td>22</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>expensive evaluations 3s; 22 constraints</td>
+      <td></td>
+      <td>JPNSEC EC-Symposium 2019 competition, <a href="http://www.jpnsec.org/files/competition2019/EC-Symposium-2019-Competition-English.html">http://www.jpnsec.org/files/competition2019/EC-Symposium-2019-Competition-English.html</a></td>
+      <td>impl_jsec2019</td>
+      <td></td>
+      <td></td>
+      <td>real-world</td>
+      <td></td>
+      <td></td>
+      <td>32</td>
+      <td></td>
+      <td>JSEC 2019 competition</td>
+      <td></td>
+      <td>3s</td>
+      <td><a href="http://www.jpnsec.org/files/competition2019/EC-Symposium-2019-Competition-English.html">http://www.jpnsec.org/files/competition2019/EC-Symposium-2019-Competition-English.html</a></td>
+      <td>JPNSEC EC-Symposium 2019 competition problem</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="fn_onemax_sphere_deceptive_rotell">
+      <td>fn_onemax_sphere_deceptive_rotell</td>
+      <td>Onemax+Sphere / DeceptiveTrap+RotatedEllipsoid</td>
+      <td>Problem</td>
+      <td>continuous | binary</td>
+      <td>>=2</td>
+      <td>2</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Mixed-variable multi-objective test problems, <a href="https://doi.org/10.1145/3449726.3459521">https://doi.org/10.1145/3449726.3459521</a></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>artificial</td>
+      <td>>=1</td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="fn_onemax_sphere_zeromax_sphere">
+      <td>fn_onemax_sphere_zeromax_sphere</td>
+      <td>Onemax+Sphere / Zeromax+Sphere</td>
+      <td>Problem</td>
+      <td>continuous | binary</td>
+      <td>>=2</td>
+      <td>2</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Onemax+Sphere / Zeromax+Sphere, <a href="https://doi.org/10.1145/3449726.3459521">https://doi.org/10.1145/3449726.3459521</a></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>artificial</td>
+      <td>>=1</td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="fn_radar_waveform">
+      <td>fn_radar_waveform</td>
+      <td>Radar waveform</td>
+      <td>Problem</td>
+      <td>integer</td>
+      <td>4-12</td>
+      <td>9</td>
+      <td></td>
+      <td>unknown</td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Radar waveform design, <a href="https://doi.org/10.1007/978-3-540-70928-2_53">https://doi.org/10.1007/978-3-540-70928-2_53</a></td>
+      <td>impl_radar_waveform</td>
+      <td></td>
+      <td></td>
+      <td>real-world</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>4-12</td>
+      <td>Evan Hughes radar waveform code</td>
+      <td></td>
+      <td></td>
+      <td><a href="http://code.evanhughes.org/">http://code.evanhughes.org/</a></td>
+      <td>Radar waveform design reference implementation</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="gen_beacon">
+      <td>gen_beacon</td>
+      <td>BEACON</td>
+      <td>Generator</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>2</td>
+      <td></td>
+      <td>box</td>
+      <td>0</td>
+      <td></td>
+      <td></td>
+      <td>no</td>
+      <td></td>
+      <td></td>
+      <td>Continuous Bi-objective Benchmark problems with Explicit Adjustable COrrelatioN control</td>
+      <td>Generator for bi-objective benchmark problems with explicitly controlled correlations in continuous spaces. Multimodal with random structure.</td>
+      <td></td>
+      <td>BEACON, <a href="https://dl.acm.org/doi/10.1145/3712255.3734303">https://dl.acm.org/doi/10.1145/3712255.3734303</a></td>
+      <td>impl_beacon</td>
+      <td>multimodal</td>
+      <td></td>
+      <td>artificial</td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>BEACON</td>
+      <td>Python</td>
+      <td>negligible</td>
+      <td><a href="https://github.com/Stebbet/BEACON/">https://github.com/Stebbet/BEACON/</a></td>
+      <td>Continuous Bi-objective Benchmark with Explicit Adjustable COrrelatioN control</td>
+      <td></td>
+      <td>0</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="gen_bono_bench">
+      <td>gen_bono_bench</td>
+      <td>BONO-Bench</td>
+      <td>Generator</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>2</td>
+      <td></td>
+      <td>box</td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td>no</td>
+      <td></td>
+      <td></td>
+      <td>Bi-objective Numerical Optimization Benchmark</td>
+      <td>Bi-objective problem generator and suite with scalable continuous decision space. Features complex problem properties and Pareto front approximations with error guarantees for the hypervolume and exact R2 indicators.</td>
+      <td></td>
+      <td></td>
+      <td>impl_bonobench</td>
+      <td>multimodal</td>
+      <td></td>
+      <td>artificial</td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>BONO-Bench</td>
+      <td>Python</td>
+      <td></td>
+      <td><a href="https://github.com/schaepermeier/bonobench">https://github.com/schaepermeier/bonobench</a></td>
+      <td>Bi-objective Numerical Optimization Benchmark (BONO-Bench)</td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="gen_ealain">
+      <td>gen_ealain</td>
+      <td>Ealain</td>
+      <td>Generator</td>
+      <td>continuous | integer | binary</td>
+      <td>>=3</td>
+      <td>[1, 10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
+      <td>dynamic | multi-fidelity</td>
+      <td>unknown</td>
+      <td>>=1</td>
+      <td>optional</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>[1, 2]</td>
+      <td></td>
+      <td>Real-world-like, easily extensible to increase complexity</td>
+      <td></td>
+      <td>Ealain, <a href="https://doi.org/10.1145/3638530.3654299">https://doi.org/10.1145/3638530.3654299</a></td>
+      <td>impl_ealain</td>
+      <td></td>
+      <td></td>
+      <td>real-world-like</td>
+      <td>>=1</td>
+      <td></td>
+      <td>>=1</td>
+      <td>>=1</td>
+      <td>Ealain</td>
+      <td></td>
+      <td></td>
+      <td><a href="https://github.com/qrenau/Ealain">https://github.com/qrenau/Ealain</a></td>
+      <td>Real-world-like extensible benchmark problem generator</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="gen_gnbg">
+      <td>gen_gnbg</td>
+      <td>GNBG</td>
+      <td>Generator</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Generator counterpart of GNBG.</td>
+      <td></td>
+      <td>GNBG, <a href="https://arxiv.org/abs/2312.07083">https://arxiv.org/abs/2312.07083</a></td>
+      <td>impl_gnbg</td>
+      <td></td>
+      <td></td>
+      <td>artificial</td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>GNBG Generator</td>
+      <td></td>
+      <td></td>
+      <td><a href="https://github.com/Danial-Yazdani/GNBG-Generator">https://github.com/Danial-Yazdani/GNBG-Generator</a></td>
+      <td>Generalized Numerical Benchmark Generator</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="gen_gnbg_ii">
+      <td>gen_gnbg_ii</td>
+      <td>GNBG-II</td>
+      <td>Generator</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Generator counterpart of GNBG-II.</td>
+      <td></td>
+      <td>GNBG-II, <a href="https://dl.acm.org/doi/pdf/10.1145/3712255.3734271">https://dl.acm.org/doi/pdf/10.1145/3712255.3734271</a></td>
+      <td>["impl_gnbg_ii", "impl_iohgnbg"]</td>
+      <td></td>
+      <td></td>
+      <td>artificial</td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>IOHGNBG | GNBG-II</td>
+      <td></td>
+      <td></td>
+      <td><a href="https://github.com/IOHprofiler/IOHGNBG">https://github.com/IOHprofiler/IOHGNBG</a> | <a href="https://github.com/rohitsalgotra/GNBG-II">https://github.com/rohitsalgotra/GNBG-II</a></td>
+      <td>IOHprofiler version of GNBG | Generalized Numerical Benchmark Generator version 2</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="gen_gpd">
+      <td>gen_gpd</td>
+      <td>GPD</td>
+      <td>Generator</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>[10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
+      <td>noisy</td>
+      <td>unknown</td>
+      <td>>=1</td>
+      <td></td>
+      <td>optional</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>GPD generator, <a href="https://doi.org/10.1016/j.asoc.2020.106139">https://doi.org/10.1016/j.asoc.2020.106139</a></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="gen_iohclustering">
+      <td>gen_iohclustering</td>
+      <td>IOHClustering</td>
+      <td>Generator</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Generator counterpart of the IOHClustering suite.</td>
+      <td></td>
+      <td>IOHClustering, <a href="https://arxiv.org/pdf/2505.09233">https://arxiv.org/pdf/2505.09233</a></td>
+      <td>impl_iohclustering</td>
+      <td>multimodal</td>
+      <td></td>
+      <td>artificial-from-real-data</td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>IOHClustering</td>
+      <td></td>
+      <td></td>
+      <td><a href="https://github.com/IOHprofiler/IOHClustering">https://github.com/IOHprofiler/IOHClustering</a></td>
+      <td>Clustering-based optimization benchmark built on ML datasets</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="gen_ma_bbob">
+      <td>gen_ma_bbob</td>
+      <td>MA-BBOB</td>
+      <td>Generator</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Generator that creates affine combinations of BBOB functions</td>
+      <td></td>
+      <td>MA-BBOB, <a href="https://doi.org/10.1145/3673908">https://doi.org/10.1145/3673908</a></td>
+      <td>["impl_iohexperimenter", "impl_ma_bbob"]</td>
+      <td>multimodal</td>
+      <td></td>
+      <td>artificial</td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>MA-BBOB (IOHexperimenter) | IOHexperimenter</td>
+      <td>C++/Python</td>
+      <td></td>
+      <td><a href="https://github.com/IOHprofiler/IOHexperimenter/blob/master/example/Competitions/MA-BBOB/Example_MABBOB.ipynb">https://github.com/IOHprofiler/IOHexperimenter/blob/master/example/Competitions/MA-BBOB/Example_MABBOB.ipynb</a> | <a href="https://github.com/IOHprofiler/IOHexperimenter">https://github.com/IOHprofiler/IOHexperimenter</a></td>
+      <td>Example notebook for MA-BBOB in IOHexperimenter | IOHprofiler experimenter framework</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="gen_mpm2">
+      <td>gen_mpm2</td>
+      <td>MPM2</td>
+      <td>Generator</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>nonlinear nonseparable nonsymmetric; scalable in terms of time to evaluate the objective function</td>
+      <td></td>
+      <td>MPM2 technical report TR15-01, <a href="https://ls11-www.cs.tu-dortmund.de/_media/techreports/tr15-01.pdf">https://ls11-www.cs.tu-dortmund.de/_media/techreports/tr15-01.pdf</a></td>
+      <td>impl_mpm2</td>
+      <td>multimodal</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>MPM2 (smoof)</td>
+      <td>Python</td>
+      <td></td>
+      <td><a href="https://github.com/jakobbossek/smoof/blob/master/inst/mpm2.py">https://github.com/jakobbossek/smoof/blob/master/inst/mpm2.py</a></td>
+      <td>Python implementation of MPM2 distributed with smoof</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="gen_mubqp">
+      <td>gen_mubqp</td>
+      <td>mUBQP</td>
+      <td>Generator</td>
+      <td>binary</td>
+      <td>>=1</td>
+      <td>[1, 10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>tunable variable and objective dimensions; tunable density and correlation between objectives</td>
+      <td></td>
+      <td>mUBQP benchmark, <a href="https://doi.org/10.1016/j.asoc.2013.11.008">https://doi.org/10.1016/j.asoc.2013.11.008</a></td>
+      <td>impl_mocobench</td>
+      <td>["multimodal", "quadratic"]</td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>mocobench</td>
+      <td>C++</td>
+      <td></td>
+      <td><a href="https://gitlab.com/aliefooghe/mocobench/">https://gitlab.com/aliefooghe/mocobench/</a></td>
+      <td>Multi-objective combinatorial optimization benchmark</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="gen_puboi">
+      <td>gen_puboi</td>
+      <td>PUBOi</td>
+      <td>Generator</td>
+      <td>binary</td>
+      <td>>=1</td>
+      <td>1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>no</td>
+      <td></td>
+      <td></td>
+      <td>Polynomial Unconstrained Binary Optimization with tunable importance</td>
+      <td>A benchmark in which variable importance is tunable, based on the Walsh function.</td>
+      <td></td>
+      <td>PUBOi, <a href="https://link.springer.com/chapter/10.1007/978-3-031-04148-8_12">https://link.springer.com/chapter/10.1007/978-3-031-04148-8_12</a></td>
+      <td>impl_puboi</td>
+      <td></td>
+      <td></td>
+      <td>artificial</td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>PUBO Importance Benchmark</td>
+      <td>Python / C++</td>
+      <td></td>
+      <td><a href="https://gitlab.com/verel/pubo-importance-benchmark">https://gitlab.com/verel/pubo-importance-benchmark</a></td>
+      <td>A benchmark in which variable importance is tunable, based on the Walsh function</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="gen_randoptgen">
+      <td>gen_randoptgen</td>
+      <td>RandOptGen</td>
+      <td>Generator</td>
+      <td>continuous | integer | binary</td>
+      <td>>=3</td>
+      <td>[1, 10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>no</td>
+      <td></td>
+      <td></td>
+      <td>RandOptGen</td>
+      <td>A Unified Random Problem Generator for Single- and Multi-Objective Optimization Problems with Mixed-Variable Input Spaces.</td>
+      <td></td>
+      <td></td>
+      <td>impl_randoptgen</td>
+      <td>multimodal</td>
+      <td></td>
+      <td>artificial</td>
+      <td>>=1</td>
+      <td></td>
+      <td>>=1</td>
+      <td>>=1</td>
+      <td>RandOptGen</td>
+      <td>Python</td>
+      <td>milliseconds</td>
+      <td><a href="https://github.com/MALEO-research-group/RandOptGen">https://github.com/MALEO-research-group/RandOptGen</a> | <a href="https://doi.org/10.1145/3712256.3726478">https://doi.org/10.1145/3712256.3726478</a></td>
+      <td>Unified Random Problem Generator for Single- and Multi-Objective Optimization with Mixed-Variable Input Spaces</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="gen_rho_mnk_landscapes">
+      <td>gen_rho_mnk_landscapes</td>
+      <td>ρMNK-Landscapes</td>
+      <td>Generator</td>
+      <td>binary</td>
+      <td>>=1</td>
+      <td>[1, 10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>tunable variable and objective dimensions; tunable multimodality and correlation between objectives</td>
+      <td></td>
+      <td>On the design of multi-objective evolutionary algorithms based on NK-landscapes, <a href="https://doi.org/10.1016/j.ejor.2012.12.019">https://doi.org/10.1016/j.ejor.2012.12.019</a></td>
+      <td>impl_mocobench</td>
+      <td>multimodal</td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>mocobench</td>
+      <td>C++</td>
+      <td></td>
+      <td><a href="https://gitlab.com/aliefooghe/mocobench/">https://gitlab.com/aliefooghe/mocobench/</a></td>
+      <td>Multi-objective combinatorial optimization benchmark</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="gen_rho_mtsp">
+      <td>gen_rho_mtsp</td>
+      <td>ρmTSP</td>
+      <td>Generator</td>
+      <td>unknown</td>
+      <td>>=1</td>
+      <td>[1, 10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>tunable variable and objective dimensions; tunable instance type (euclidean/random); tunable correlation between objectives</td>
+      <td></td>
+      <td>On the impact of multi-objective scalability for the ρmTSP, <a href="https://doi.org/10.1007/978-3-319-45823-6_40">https://doi.org/10.1007/978-3-319-45823-6_40</a></td>
+      <td>impl_mocobench</td>
+      <td>["multimodal", "quadratic"]</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>mocobench</td>
+      <td>C++</td>
+      <td></td>
+      <td><a href="https://gitlab.com/aliefooghe/mocobench/">https://gitlab.com/aliefooghe/mocobench/</a></td>
+      <td>Multi-objective combinatorial optimization benchmark</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="gen_wmodel">
+      <td>gen_wmodel</td>
+      <td>W-model</td>
+      <td>Generator</td>
+      <td>binary</td>
+      <td>>=1</td>
+      <td>1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Tunable generator for binary optimization based on several difficulty features</td>
+      <td></td>
+      <td>W-model, <a href="https://dl.acm.org/doi/abs/10.1145/3205651.3208240">https://dl.acm.org/doi/abs/10.1145/3205651.3208240</a></td>
+      <td>impl_wmodel</td>
+      <td></td>
+      <td></td>
+      <td>artificial</td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>BBDOB W-Model</td>
+      <td></td>
+      <td></td>
+      <td><a href="https://github.com/thomasWeise/BBDOB_W_Model">https://github.com/thomasWeise/BBDOB_W_Model</a></td>
+      <td>Tunable generator for binary optimization</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_amvop">
+      <td>suite_amvop</td>
+      <td>AMVOP</td>
+      <td>Suite</td>
+      <td>continuous | integer | categorical</td>
+      <td>>=3</td>
+      <td>1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>AMVOP, <a href="https://doi.org/10.1109/TEVC.2013.2281531">https://doi.org/10.1109/TEVC.2013.2281531</a></td>
+      <td></td>
+      <td>multimodal</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td>>=1</td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_bbob">
+      <td>suite_bbob</td>
+      <td>BBOB</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>COCO: a platform for comparing continuous optimizers in a black-box setting, <a href="https://doi.org/10.1080/10556788.2020.1808977">https://doi.org/10.1080/10556788.2020.1808977</a></td>
+      <td>impl_coco</td>
+      <td>multimodal</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>COCO framework</td>
+      <td>C/Python</td>
+      <td></td>
+      <td><a href="https://github.com/numbbo/coco">https://github.com/numbbo/coco</a></td>
+      <td>Comparing Continuous Optimizers: black-box optimization benchmarking platform</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_bbob_biobj">
+      <td>suite_bbob_biobj</td>
+      <td>BBOB-biobj</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>2-40</td>
+      <td>2</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>BBOB bi-objective test suite, <a href="https://doi.org/10.48550/arXiv.1604.00359">https://doi.org/10.48550/arXiv.1604.00359</a></td>
+      <td>impl_coco</td>
+      <td>multimodal</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>2-40</td>
+      <td></td>
+      <td>COCO framework</td>
+      <td>C/Python</td>
+      <td></td>
+      <td><a href="https://github.com/numbbo/coco">https://github.com/numbbo/coco</a></td>
+      <td>Comparing Continuous Optimizers: black-box optimization benchmarking platform</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_bbob_biobj_mixint">
+      <td>suite_bbob_biobj_mixint</td>
+      <td>BBOB-biobj-mixint</td>
+      <td>Suite</td>
+      <td>continuous | integer</td>
+      <td>10-320</td>
+      <td>2</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>BBOB bi-objective mixed-integer test suite, <a href="https://doi.org/10.1145/3321707.3321868">https://doi.org/10.1145/3321707.3321868</a></td>
+      <td>impl_coco</td>
+      <td>multimodal</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>5-160</td>
+      <td>5-160</td>
+      <td>COCO framework</td>
+      <td>C/Python</td>
+      <td></td>
+      <td><a href="https://github.com/numbbo/coco">https://github.com/numbbo/coco</a></td>
+      <td>Comparing Continuous Optimizers: black-box optimization benchmarking platform</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_bbob_constrained">
+      <td>suite_bbob_constrained</td>
+      <td>BBOB-constrained</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>2-40</td>
+      <td>1</td>
+      <td></td>
+      <td>unknown</td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>bbob-constrained documentation, <a href="http://numbbo.github.io/coco-doc/bbob-constrained/">http://numbbo.github.io/coco-doc/bbob-constrained/</a></td>
+      <td>impl_coco</td>
+      <td>multimodal</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>2-40</td>
+      <td></td>
+      <td>COCO framework</td>
+      <td>C/Python</td>
+      <td></td>
+      <td><a href="https://github.com/numbbo/coco">https://github.com/numbbo/coco</a></td>
+      <td>Comparing Continuous Optimizers: black-box optimization benchmarking platform</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_bbob_largescale">
+      <td>suite_bbob_largescale</td>
+      <td>BBOB-largescale</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>20-640</td>
+      <td>1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>BBOB large-scale test suite, <a href="https://doi.org/10.48550/arXiv.1903.06396">https://doi.org/10.48550/arXiv.1903.06396</a></td>
+      <td>impl_coco</td>
+      <td>multimodal</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>20-640</td>
+      <td></td>
+      <td>COCO framework</td>
+      <td>C/Python</td>
+      <td></td>
+      <td><a href="https://github.com/numbbo/coco">https://github.com/numbbo/coco</a></td>
+      <td>Comparing Continuous Optimizers: black-box optimization benchmarking platform</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_bbob_mixint">
+      <td>suite_bbob_mixint</td>
+      <td>BBOB-mixint</td>
+      <td>Suite</td>
+      <td>continuous | integer</td>
+      <td>10-320</td>
+      <td>1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>BBOB mixed-integer test suite, <a href="https://doi.org/10.1145/3321707.3321868">https://doi.org/10.1145/3321707.3321868</a></td>
+      <td>impl_coco</td>
+      <td>multimodal</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>5-160</td>
+      <td>5-160</td>
+      <td>COCO framework</td>
+      <td>C/Python</td>
+      <td></td>
+      <td><a href="https://github.com/numbbo/coco">https://github.com/numbbo/coco</a></td>
+      <td>Comparing Continuous Optimizers: black-box optimization benchmarking platform</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_bbob_noisy">
+      <td>suite_bbob_noisy</td>
+      <td>BBOB-noisy</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>1</td>
+      <td>noisy</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>noisy</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Real-parameter black-box optimization benchmarking: noisy functions definitions, <a href="https://hal.inria.fr/inria-00369466">https://hal.inria.fr/inria-00369466</a></td>
+      <td>impl_coco_legacy</td>
+      <td>multimodal</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>COCO legacy (bbob-noisy)</td>
+      <td>C/Python</td>
+      <td></td>
+      <td><a href="https://web.archive.org/web/20210416065610/https://coco.gforge.inria.fr/doku.php?id=downloads">https://web.archive.org/web/20210416065610/https://coco.gforge.inria.fr/doku.php?id=downloads</a></td>
+      <td>Archived COCO download page that hosted the bbob-noisy suite</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_bp">
+      <td>suite_bp</td>
+      <td>BP</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>[10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
+      <td>noisy</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>unknown</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>BP benchmark, <a href="https://doi.org/10.1109/CEC.2019.8790277">https://doi.org/10.1109/CEC.2019.8790277</a></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_brachytherapy">
+      <td>suite_brachytherapy</td>
+      <td>Brachytherapy treatment planning</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>100-500</td>
+      <td>[2, 3]</td>
+      <td>multi-fidelity</td>
+      <td>unknown</td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td>yes</td>
+      <td></td>
+      <td>[1, 2]</td>
+      <td>Brachytherapy treatment planning</td>
+      <td>Treatment planning for internal radiation therapy. Multi-objective with aggregated objectives; no public source code.</td>
+      <td></td>
+      <td>Brachytherapy treatment planning, <a href="https://www.sciencedirect.com/science/article/pii/S1538472123016781">https://www.sciencedirect.com/science/article/pii/S1538472123016781</a></td>
+      <td></td>
+      <td>multimodal</td>
+      <td></td>
+      <td>real-world</td>
+      <td></td>
+      <td></td>
+      <td>100-500</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_car_structure">
+      <td>suite_car_structure</td>
+      <td>Car structure</td>
+      <td>Suite</td>
+      <td>integer</td>
+      <td>144-222</td>
+      <td>2</td>
+      <td></td>
+      <td>unknown</td>
+      <td>54</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>54 constraints</td>
+      <td></td>
+      <td>Car structure design benchmark, <a href="https://doi.org/10.1145/3205651.3205702">https://doi.org/10.1145/3205651.3205702</a></td>
+      <td>impl_car_structure</td>
+      <td></td>
+      <td></td>
+      <td>real-world</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>144-222</td>
+      <td>Car-structure benchmark</td>
+      <td></td>
+      <td></td>
+      <td><a href="http://ladse.eng.isas.jaxa.jp/benchmark/">http://ladse.eng.isas.jaxa.jp/benchmark/</a></td>
+      <td>JAXA LADSE benchmark problems</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_cdmp">
+      <td>suite_cdmp</td>
+      <td>CDMP</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>[10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
+      <td>dynamic | noisy</td>
+      <td>unknown</td>
+      <td>>=1</td>
+      <td>unknown</td>
+      <td>unknown</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>CDMP benchmark, <a href="https://doi.org/10.1145/3321707.3321878">https://doi.org/10.1145/3321707.3321878</a></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_cec2013">
+      <td>suite_cec2013</td>
+      <td>CEC2013</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>suite used for cec2013 competition. Also in IOH.</td>
+      <td></td>
+      <td>CEC2013 definitions, <a href="https://peerj.com/articles/cs-2671/CEC2013.pdf">https://peerj.com/articles/cs-2671/CEC2013.pdf</a></td>
+      <td>["impl_cec2013", "impl_iohexperimenter"]</td>
+      <td></td>
+      <td></td>
+      <td>artificial</td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>CEC2013 reference code | IOHexperimenter</td>
+      <td>C++/Python</td>
+      <td></td>
+      <td><a href="https://github.com/P-N-Suganthan/CEC2013">https://github.com/P-N-Suganthan/CEC2013</a> | <a href="https://github.com/IOHprofiler/IOHexperimenter">https://github.com/IOHprofiler/IOHexperimenter</a></td>
+      <td>Suganthan's reference implementation | IOHprofiler experimenter framework</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_cec2015_dmoo">
+      <td>suite_cec2015_dmoo</td>
+      <td>CEC2015-DMOO</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>0</td>
+      <td>[2, 3]</td>
+      <td>dynamic</td>
+      <td>unknown</td>
+      <td>>=1</td>
+      <td>dynamic</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Benchmark Functions for CEC 2015 Special Session and Competition on Dynamic Multi-objective Optimization</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>0</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_cec2018_dt">
+      <td>suite_cec2018_dt</td>
+      <td>CEC2018 DT</td>
+      <td>Suite</td>
+      <td>unknown</td>
+      <td>>=1</td>
+      <td>[2, 3]</td>
+      <td>dynamic</td>
+      <td></td>
+      <td></td>
+      <td>dynamic</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>CEC2018 Competition on Dynamic Multiobjective Optimisation</td>
+      <td>14 problems. Time-dependent: Pareto front/Pareto set geometry; irregular Pareto front shapes; variable-linkage; number of disconnected Pareto front segments; etc.</td>
+      <td></td>
+      <td>CEC2018 DMOP Competition TR, <a href="https://www.academia.edu/download/94499025/TR-CEC2018-DMOP-Competition.pdf">https://www.academia.edu/download/94499025/TR-CEC2018-DMOP-Competition.pdf</a></td>
+      <td>impl_pymoo</td>
+      <td></td>
+      <td></td>
+      <td>artificial</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>pymoo</td>
+      <td>Python</td>
+      <td></td>
+      <td><a href="https://github.com/anyoptimization/pymoo">https://github.com/anyoptimization/pymoo</a></td>
+      <td>Multi-objective optimization in Python</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_cec2022">
+      <td>suite_cec2022</td>
+      <td>CEC2022</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>suite used for cec2022 competition. Also in IOH.</td>
+      <td></td>
+      <td>CEC2022 TR, <a href="https://github.com/P-N-Suganthan/2022-SO-BO/blob/main/CEC2022%20TR.pdf">https://github.com/P-N-Suganthan/2022-SO-BO/blob/main/CEC2022%20TR.pdf</a></td>
+      <td>["impl_cec2022", "impl_iohexperimenter"]</td>
+      <td></td>
+      <td></td>
+      <td>artificial</td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>IOHexperimenter | CEC2022 reference code</td>
+      <td>C++/Python</td>
+      <td></td>
+      <td><a href="https://github.com/IOHprofiler/IOHexperimenter">https://github.com/IOHprofiler/IOHexperimenter</a> | <a href="https://github.com/P-N-Suganthan/2022-SO-BO">https://github.com/P-N-Suganthan/2022-SO-BO</a></td>
+      <td>IOHprofiler experimenter framework | Suganthan's reference implementation</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_cfd">
+      <td>suite_cfd</td>
+      <td>CFD</td>
+      <td>Suite</td>
+      <td>unknown</td>
+      <td>>=1</td>
+      <td>[1, 2]</td>
+      <td></td>
+      <td>unknown</td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>expensive evaluations 30s-15m</td>
+      <td></td>
+      <td>CFD test problem suite, <a href="https://doi.org/10.1007/978-3-319-99259-4_24">https://doi.org/10.1007/978-3-319-99259-4_24</a></td>
+      <td>impl_cfd</td>
+      <td></td>
+      <td></td>
+      <td>real-world</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>CFD test problem suite</td>
+      <td></td>
+      <td>30s-15m</td>
+      <td><a href="https://bitbucket.org/arahat/cfd-test-problem-suite">https://bitbucket.org/arahat/cfd-test-problem-suite</a></td>
+      <td>Expensive real-world CFD-based test problems</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_cre">
+      <td>suite_cre</td>
+      <td>CRE</td>
+      <td>Suite</td>
+      <td>continuous | integer</td>
+      <td>6-14</td>
+      <td>[2, 3, 4, 5]</td>
+      <td></td>
+      <td>unknown</td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Easy-to-evaluate real-world multi-objective optimization problems, Ryoji Tanabe; Hisao Ishibuchi, <a href="https://doi.org/10.1016/j.asoc.2020.106078">https://doi.org/10.1016/j.asoc.2020.106078</a></td>
+      <td>impl_reproblems</td>
+      <td></td>
+      <td></td>
+      <td>real-world-like</td>
+      <td></td>
+      <td></td>
+      <td>3-7</td>
+      <td>3-7</td>
+      <td>reproblems</td>
+      <td>Python</td>
+      <td></td>
+      <td><a href="https://github.com/ryojitanabe/reproblems">https://github.com/ryojitanabe/reproblems</a></td>
+      <td>Real-world inspired multi-objective optimization problem suite</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_cuter">
+      <td>suite_cuter</td>
+      <td>CUTEr</td>
+      <td>Suite</td>
+      <td>continuous | integer | binary</td>
+      <td>>=3</td>
+      <td>1</td>
+      <td></td>
+      <td>unknown</td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td>no</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>A constrained and unconstrained testing environment.</td>
+      <td></td>
+      <td>CUTEr, <a href="https://dl.acm.org/doi/10.1145/962437.962439">https://dl.acm.org/doi/10.1145/962437.962439</a></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>artificial</td>
+      <td>>=1</td>
+      <td></td>
+      <td>>=1</td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_cutest">
+      <td>suite_cutest</td>
+      <td>CUTEst</td>
+      <td>Suite</td>
+      <td>continuous | integer | binary</td>
+      <td>>=3</td>
+      <td>1</td>
+      <td></td>
+      <td>box | unknown</td>
+      <td>>=2</td>
+      <td></td>
+      <td></td>
+      <td>no</td>
+      <td></td>
+      <td></td>
+      <td>Constrained and Unconstrained Testing Environment with safe threads</td>
+      <td>CUTEst for optimization software</td>
+      <td></td>
+      <td>CUTEst, <a href="https://link.springer.com/article/10.1007/s10589-014-9687-3">https://link.springer.com/article/10.1007/s10589-014-9687-3</a></td>
+      <td>impl_pycutest</td>
+      <td>multimodal</td>
+      <td></td>
+      <td>artificial</td>
+      <td>>=1</td>
+      <td></td>
+      <td>>=1</td>
+      <td>>=1</td>
+      <td>pycutest</td>
+      <td>Python / C++ / Fortran</td>
+      <td></td>
+      <td><a href="https://github.com/jfowkes/pycutest">https://github.com/jfowkes/pycutest</a></td>
+      <td>Python interface to CUTEst</td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_dtlz">
+      <td>suite_dtlz</td>
+      <td>DTLZ</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>[10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Scalable multi-objective optimization test problems, Kalyanmoy Deb; Lothar Thiele; Marco Laumanns; Eckart Zitzler, <a href="https://doi.org/10.1109/CEC.2002.1007032">https://doi.org/10.1109/CEC.2002.1007032</a></td>
+      <td>impl_pymoo</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>pymoo</td>
+      <td>Python</td>
+      <td></td>
+      <td><a href="https://github.com/anyoptimization/pymoo">https://github.com/anyoptimization/pymoo</a></td>
+      <td>Multi-objective optimization in Python</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_dynamicbinval">
+      <td>suite_dynamicbinval</td>
+      <td>DynamicBinVal</td>
+      <td>Suite</td>
+      <td>binary</td>
+      <td>>=1</td>
+      <td>1</td>
+      <td>dynamic</td>
+      <td></td>
+      <td></td>
+      <td>dynamic</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Four versions of the dynamic binary value problem</td>
+      <td></td>
+      <td>DynamicBinVal, <a href="https://arxiv.org/pdf/2404.15837">https://arxiv.org/pdf/2404.15837</a></td>
+      <td>impl_iohexperimenter</td>
+      <td></td>
+      <td></td>
+      <td>artificial</td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>IOHexperimenter</td>
+      <td>C++/Python</td>
+      <td></td>
+      <td><a href="https://github.com/IOHprofiler/IOHexperimenter">https://github.com/IOHprofiler/IOHexperimenter</a></td>
+      <td>IOHprofiler experimenter framework</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_emo2017">
+      <td>suite_emo2017</td>
+      <td>EMO2017</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>4-24</td>
+      <td>2</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>BBComp EMO 2017, <a href="https://www.ini.rub.de/PEOPLE/glasmtbl/projects/bbcomp/">https://www.ini.rub.de/PEOPLE/glasmtbl/projects/bbcomp/</a></td>
+      <td>impl_emo2017</td>
+      <td></td>
+      <td></td>
+      <td>real-world</td>
+      <td></td>
+      <td></td>
+      <td>4-24</td>
+      <td></td>
+      <td>EMO 2017 real-world problems</td>
+      <td></td>
+      <td></td>
+      <td><a href="https://www.ini.rub.de/PEOPLE/glasmtbl/projects/bbcomp/downloads/realworld-problems-bbcomp-EMO-2017.zip">https://www.ini.rub.de/PEOPLE/glasmtbl/projects/bbcomp/downloads/realworld-problems-bbcomp-EMO-2017.zip</a></td>
+      <td>BBComp EMO-2017 real-world problem archive</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_etmof">
+      <td>suite_etmof</td>
+      <td>ETMOF</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>25-10000</td>
+      <td>[10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 2, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 3, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 4, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 5, 50, 6, 7, 8, 9]</td>
+      <td>dynamic</td>
+      <td></td>
+      <td></td>
+      <td>dynamic</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Evolutionary many-task optimization framework, <a href="https://doi.org/10.48550/arXiv.2110.08033">https://doi.org/10.48550/arXiv.2110.08033</a></td>
+      <td>impl_etmof</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>25-10000</td>
+      <td></td>
+      <td>ETMOF</td>
+      <td></td>
+      <td></td>
+      <td><a href="https://github.com/songbai-liu/etmo">https://github.com/songbai-liu/etmo</a></td>
+      <td>Evolutionary many-task optimization framework</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_expobench">
+      <td>suite_expobench</td>
+      <td>EXPObench</td>
+      <td>Suite</td>
+      <td>categorical | continuous | integer</td>
+      <td>30-405</td>
+      <td>1</td>
+      <td>noisy</td>
+      <td>box | unknown</td>
+      <td>>=2</td>
+      <td></td>
+      <td>["observational", "real-life"]</td>
+      <td>no</td>
+      <td></td>
+      <td></td>
+      <td>EXPensive Optimization benchmark library</td>
+      <td>Wind farm layout optimization, gas filter design, pipe shape optimization, hyperparameter tuning, and hospital simulation</td>
+      <td></td>
+      <td>EXPObench, <a href="https://doi.org/10.1016/j.asoc.2023.110744">https://doi.org/10.1016/j.asoc.2023.110744</a></td>
+      <td>impl_expobench</td>
+      <td></td>
+      <td></td>
+      <td>real-world</td>
+      <td></td>
+      <td>10-135</td>
+      <td>10-135</td>
+      <td>10-135</td>
+      <td>EXPObench</td>
+      <td>Python</td>
+      <td>2 to 80 seconds</td>
+      <td><a href="https://github.com/AlgTUDelft/ExpensiveOptimBenchmark">https://github.com/AlgTUDelft/ExpensiveOptimBenchmark</a></td>
+      <td>EXPensive Optimization benchmark library (wind farm layout, gas filter design, pipe shape, hyperparameter tuning, hospital simulation)</td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_gbea">
+      <td>suite_gbea</td>
+      <td>GBEA</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>[1, 2]</td>
+      <td>noisy</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>noisy</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>expensive evaluations 5s-35s, RW-GAN-Mario and TopTrumps are part of GBEA</td>
+      <td></td>
+      <td>Game benchmark for evolutionary algorithms, <a href="https://doi.org/10.1145/3321707.3321805">https://doi.org/10.1145/3321707.3321805</a></td>
+      <td>impl_gbea</td>
+      <td>multimodal</td>
+      <td></td>
+      <td>real-world</td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>coco-gbea</td>
+      <td></td>
+      <td>5s-35s</td>
+      <td><a href="https://github.com/ttusar/coco-gbea">https://github.com/ttusar/coco-gbea</a></td>
+      <td>Game-Benchmark for Evolutionary Algorithms (COCO fork)</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_gnbg">
+      <td>suite_gnbg</td>
+      <td>GNBG</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Generalized Numerical Benchmark Generator</td>
+      <td></td>
+      <td>GNBG, <a href="https://arxiv.org/abs/2312.07083">https://arxiv.org/abs/2312.07083</a></td>
+      <td>impl_gnbg</td>
+      <td></td>
+      <td></td>
+      <td>artificial</td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>GNBG Generator</td>
+      <td></td>
+      <td></td>
+      <td><a href="https://github.com/Danial-Yazdani/GNBG-Generator">https://github.com/Danial-Yazdani/GNBG-Generator</a></td>
+      <td>Generalized Numerical Benchmark Generator</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_gnbg_ii">
+      <td>suite_gnbg_ii</td>
+      <td>GNBG-II</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Generalized Numerical Benchmark Generator (version 2). Also available in IOH.</td>
+      <td></td>
+      <td>GNBG-II, <a href="https://dl.acm.org/doi/pdf/10.1145/3712255.3734271">https://dl.acm.org/doi/pdf/10.1145/3712255.3734271</a></td>
+      <td>["impl_gnbg_ii", "impl_iohgnbg"]</td>
+      <td></td>
+      <td></td>
+      <td>artificial</td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>IOHGNBG | GNBG-II</td>
+      <td></td>
+      <td></td>
+      <td><a href="https://github.com/IOHprofiler/IOHGNBG">https://github.com/IOHprofiler/IOHGNBG</a> | <a href="https://github.com/rohitsalgotra/GNBG-II">https://github.com/rohitsalgotra/GNBG-II</a></td>
+      <td>IOHprofiler version of GNBG | Generalized Numerical Benchmark Generator version 2</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_iohclustering">
+      <td>suite_iohclustering</td>
+      <td>IOHClustering</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Set of benchmark problems from clustering: optimization task is selecting cluster centers for a given set of data.</td>
+      <td></td>
+      <td>IOHClustering, <a href="https://arxiv.org/pdf/2505.09233">https://arxiv.org/pdf/2505.09233</a></td>
+      <td>impl_iohclustering</td>
+      <td>multimodal</td>
+      <td></td>
+      <td>artificial-from-real-data</td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>IOHClustering</td>
+      <td></td>
+      <td></td>
+      <td><a href="https://github.com/IOHprofiler/IOHClustering">https://github.com/IOHprofiler/IOHClustering</a></td>
+      <td>Clustering-based optimization benchmark built on ML datasets</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_kinematics_robotarm">
+      <td>suite_kinematics_robotarm</td>
+      <td>KinematicsRobotArm</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>21</td>
+      <td>1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Kinematics of a robot arm, <a href="https://doi.org/10.1023/A:1013258808932">https://doi.org/10.1023/A:1013258808932</a></td>
+      <td>impl_transfer_rf_bbob_rw</td>
+      <td>unimodal</td>
+      <td></td>
+      <td>real-world</td>
+      <td></td>
+      <td></td>
+      <td>21</td>
+      <td></td>
+      <td>Transfer Random Forests BBOB Real-world</td>
+      <td></td>
+      <td></td>
+      <td><a href="https://github.com/ShuaiqunPan/Transfer_Random_forests_BBOB_Real_world">https://github.com/ShuaiqunPan/Transfer_Random_forests_BBOB_Real_world</a></td>
+      <td>Real-world BBOB-like problem implementations (Porkchop, KinematicsRobotArm)</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_l1_zdt">
+      <td>suite_l1_zdt</td>
+      <td>L1-ZDT</td>
+      <td>Suite</td>
+      <td>continuous | binary</td>
+      <td>>=2</td>
+      <td>2</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Variant of ZDT with linkages between variables within groups</td>
+      <td></td>
+      <td>Linkage ZDT/DTLZ variants, <a href="https://doi.org/10.1145/1143997.1144179">https://doi.org/10.1145/1143997.1144179</a></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_l2_dtlz">
+      <td>suite_l2_dtlz</td>
+      <td>L2-DTLZ</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>[10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Variant of DTLZ2/DTLZ3 with linkages between all variables</td>
+      <td></td>
+      <td>Linkage ZDT/DTLZ variants, <a href="https://doi.org/10.1145/1143997.1144179">https://doi.org/10.1145/1143997.1144179</a></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_l2_zdt">
+      <td>suite_l2_zdt</td>
+      <td>L2-ZDT</td>
+      <td>Suite</td>
+      <td>continuous | binary</td>
+      <td>>=2</td>
+      <td>2</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Variant of ZDT with linkages between all variables</td>
+      <td></td>
+      <td>Linkage ZDT/DTLZ variants, <a href="https://doi.org/10.1145/1143997.1144179">https://doi.org/10.1145/1143997.1144179</a></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_l3_dtlz">
+      <td>suite_l3_dtlz</td>
+      <td>L3-DTLZ</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>[10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Variant of L2-DTLZ with anti-linkage mapping</td>
+      <td></td>
+      <td>Linkage ZDT/DTLZ variants, <a href="https://doi.org/10.1145/1143997.1144179">https://doi.org/10.1145/1143997.1144179</a></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_l3_zdt">
+      <td>suite_l3_zdt</td>
+      <td>L3-ZDT</td>
+      <td>Suite</td>
+      <td>continuous | binary</td>
+      <td>>=2</td>
+      <td>2</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Variant of L2-ZDT with anti-linkage mapping</td>
+      <td></td>
+      <td>Linkage ZDT/DTLZ variants, <a href="https://doi.org/10.1145/1143997.1144179">https://doi.org/10.1145/1143997.1144179</a></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_maop">
+      <td>suite_maop</td>
+      <td>MaOP</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>[10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
+      <td>noisy</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>unknown</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>MaOP benchmark, <a href="https://doi.org/10.1016/j.swevo.2019.02.003">https://doi.org/10.1016/j.swevo.2019.02.003</a></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_mechbench">
+      <td>suite_mechbench</td>
+      <td>MECHBench</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>1</td>
+      <td></td>
+      <td>unknown</td>
+      <td>{1, 2}</td>
+      <td></td>
+      <td></td>
+      <td>no</td>
+      <td></td>
+      <td></td>
+      <td>MECHBench</td>
+      <td>Set of problems inspired by Structural Mechanics Design Optimization. Embeds physical simulations (plasticity only, no fracture/damage). Unstructured/non-isotropic multimodality.</td>
+      <td></td>
+      <td>MECHBench, <a href="https://arxiv.org/abs/2511.10821">https://arxiv.org/abs/2511.10821</a></td>
+      <td>impl_mechbench</td>
+      <td>multimodal</td>
+      <td></td>
+      <td>real-world</td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>MECHBench</td>
+      <td>Python</td>
+      <td>1-7 minutes</td>
+      <td><a href="https://github.com/BayesOptApp/MECHBench">https://github.com/BayesOptApp/MECHBench</a></td>
+      <td>Structural mechanics design optimization benchmark</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_mf2">
+      <td>suite_mf2</td>
+      <td>MF2</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>1</td>
+      <td>multi-fidelity</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>[1, 2]</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>mf2: a collection of multi-fidelity benchmark functions in Python, <a href="https://doi.org/10.21105/joss.02049">https://doi.org/10.21105/joss.02049</a></td>
+      <td>impl_mf2</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>mf2</td>
+      <td>Python</td>
+      <td></td>
+      <td><a href="https://github.com/sjvrijn/mf2">https://github.com/sjvrijn/mf2</a></td>
+      <td>Multi-fidelity test function collection</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_minus_dtlz">
+      <td>suite_minus_dtlz</td>
+      <td>Minus DTLZ</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>[10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Variant of DTLZ that minimises the inverse of the base DTLZ functions</td>
+      <td></td>
+      <td>Minus DTLZ / Minus WFG, <a href="https://doi.org/10.1109/TEVC.2016.2587749">https://doi.org/10.1109/TEVC.2016.2587749</a></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_minus_wfg">
+      <td>suite_minus_wfg</td>
+      <td>Minus WFG</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>[10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Variant of WFG that minimises the inverse of the base WFG functions</td>
+      <td></td>
+      <td>Minus DTLZ / Minus WFG, <a href="https://doi.org/10.1109/TEVC.2016.2587749">https://doi.org/10.1109/TEVC.2016.2587749</a></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_mmopp">
+      <td>suite_mmopp</td>
+      <td>MMOPP</td>
+      <td>Suite</td>
+      <td>unknown</td>
+      <td>0</td>
+      <td>[2, 3, 4, 5, 6, 7]</td>
+      <td></td>
+      <td>unknown</td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>MMOPP technical report, <a href="http://www5.zzu.edu.cn/system/_content/download.jsp?urltype=news.DownloadAttachUrl&owner=1327567121&wbfileid=4764412">http://www5.zzu.edu.cn/system/_content/download.jsp?urltype=news.DownloadAttachUrl&owner=1327567121&wbfileid=4764412</a></td>
+      <td>impl_mmopp</td>
+      <td>multimodal</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>MMOPP</td>
+      <td></td>
+      <td></td>
+      <td><a href="http://www5.zzu.edu.cn/ecilab/info/1036/1251.htm">http://www5.zzu.edu.cn/ecilab/info/1036/1251.htm</a></td>
+      <td>ECI lab distribution page for MMOPP</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_modact">
+      <td>suite_modact</td>
+      <td>MODAct</td>
+      <td>Suite</td>
+      <td>continuous | integer</td>
+      <td>40</td>
+      <td>[2, 3, 4, 5]</td>
+      <td></td>
+      <td>unknown</td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>multiobjective design of actuators</td>
+      <td>Realistic Constrained Multi-Objective Optimization Benchmark Problems from Design.</td>
+      <td></td>
+      <td>MODAct, <a href="https://doi.org/10.1109/TEVC.2020.3020046">https://doi.org/10.1109/TEVC.2020.3020046</a></td>
+      <td>["impl_modact", "impl_pymoo"]</td>
+      <td></td>
+      <td></td>
+      <td>real-world</td>
+      <td></td>
+      <td></td>
+      <td>20</td>
+      <td>20</td>
+      <td>modact | pymoo</td>
+      <td>Python</td>
+      <td>20ms</td>
+      <td><a href="https://github.com/epfl-lamd/modact">https://github.com/epfl-lamd/modact</a> | <a href="https://github.com/anyoptimization/pymoo">https://github.com/anyoptimization/pymoo</a></td>
+      <td>EPFL-LAMD modact package | Multi-objective optimization in Python</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_morepo">
+      <td>suite_morepo</td>
+      <td>MOrepo</td>
+      <td>Suite</td>
+      <td>unknown</td>
+      <td>0</td>
+      <td>2</td>
+      <td>dynamic | noisy</td>
+      <td>unknown</td>
+      <td>>=1</td>
+      <td>unknown</td>
+      <td>unknown</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>impl_morepo</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>MOrepo</td>
+      <td></td>
+      <td></td>
+      <td><a href="https://github.com/MCDMSociety/MOrepo">https://github.com/MCDMSociety/MOrepo</a></td>
+      <td>Multi-objective optimisation problem repository</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_pbo">
+      <td>suite_pbo</td>
+      <td>PBO</td>
+      <td>Suite</td>
+      <td>binary</td>
+      <td>>=1</td>
+      <td>1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Suite of 25 binary optimization problems</td>
+      <td></td>
+      <td>PBO benchmarks, <a href="https://dl.acm.org/doi/pdf/10.1145/3319619.3326810">https://dl.acm.org/doi/pdf/10.1145/3319619.3326810</a></td>
+      <td>impl_iohexperimenter</td>
+      <td></td>
+      <td></td>
+      <td>artificial</td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>IOHexperimenter</td>
+      <td>C++/Python</td>
+      <td></td>
+      <td><a href="https://github.com/IOHprofiler/IOHexperimenter">https://github.com/IOHprofiler/IOHexperimenter</a></td>
+      <td>IOHprofiler experimenter framework</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_porkchop">
+      <td>suite_porkchop</td>
+      <td>PorkchopPlotInterplanetaryTrajectory</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>2</td>
+      <td>1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Porkchop plot interplanetary trajectory benchmark, <a href="https://doi.org/10.1109/CEC65147.2025.11042973">https://doi.org/10.1109/CEC65147.2025.11042973</a></td>
+      <td>impl_transfer_rf_bbob_rw</td>
+      <td>multimodal</td>
+      <td></td>
+      <td>real-world</td>
+      <td></td>
+      <td></td>
+      <td>2</td>
+      <td></td>
+      <td>Transfer Random Forests BBOB Real-world</td>
+      <td></td>
+      <td></td>
+      <td><a href="https://github.com/ShuaiqunPan/Transfer_Random_forests_BBOB_Real_world">https://github.com/ShuaiqunPan/Transfer_Random_forests_BBOB_Real_world</a></td>
+      <td>Real-world BBOB-like problem implementations (Porkchop, KinematicsRobotArm)</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_re">
+      <td>suite_re</td>
+      <td>RE</td>
+      <td>Suite</td>
+      <td>continuous | integer</td>
+      <td>4-14</td>
+      <td>[2, 3, 4, 5, 6, 7, 8, 9]</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Easy-to-evaluate real-world multi-objective optimization problems, Ryoji Tanabe; Hisao Ishibuchi, <a href="https://doi.org/10.1016/j.asoc.2020.106078">https://doi.org/10.1016/j.asoc.2020.106078</a></td>
+      <td>impl_reproblems</td>
+      <td></td>
+      <td></td>
+      <td>real-world-like</td>
+      <td></td>
+      <td></td>
+      <td>2-7</td>
+      <td>2-7</td>
+      <td>reproblems</td>
+      <td>Python</td>
+      <td></td>
+      <td><a href="https://github.com/ryojitanabe/reproblems">https://github.com/ryojitanabe/reproblems</a></td>
+      <td>Real-world inspired multi-objective optimization problem suite</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_rwmvop">
+      <td>suite_rwmvop</td>
+      <td>RWMVOP</td>
+      <td>Suite</td>
+      <td>continuous | integer | categorical</td>
+      <td>>=3</td>
+      <td>1</td>
+      <td></td>
+      <td>unknown</td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>RWMVOP, <a href="https://doi.org/10.1109/TEVC.2013.2281531">https://doi.org/10.1109/TEVC.2013.2281531</a></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>real-world</td>
+      <td></td>
+      <td>>=1</td>
+      <td>>=1</td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_sbox_cost">
+      <td>suite_sbox_cost</td>
+      <td>SBOX-COST</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>problems from BBOB but allows instances with the optimum close to the boundary</td>
+      <td></td>
+      <td>SBOX-COST, <a href="https://doi.org/10.48550/arXiv.2305.12221">https://doi.org/10.48550/arXiv.2305.12221</a></td>
+      <td>impl_iohexperimenter</td>
+      <td>multimodal</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>IOHexperimenter</td>
+      <td>C++/Python</td>
+      <td></td>
+      <td><a href="https://github.com/IOHprofiler/IOHexperimenter">https://github.com/IOHprofiler/IOHexperimenter</a></td>
+      <td>IOHprofiler experimenter framework</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_sdp">
+      <td>suite_sdp</td>
+      <td>SDP</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>[10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
+      <td>dynamic | noisy</td>
+      <td></td>
+      <td></td>
+      <td>dynamic</td>
+      <td>unknown</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>SDP dynamic multi-objective benchmark, <a href="https://doi.org/10.1109/TCYB.2019.2896021">https://doi.org/10.1109/TCYB.2019.2896021</a></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_submodular">
+      <td>suite_submodular</td>
+      <td>Submodular Optimization</td>
+      <td>Suite</td>
+      <td>binary</td>
+      <td>>=1</td>
+      <td>1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>set of graph-based submodular optimization problems from 4 problem types</td>
+      <td></td>
+      <td>Submodular optimization benchmark, <a href="https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10254181">https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10254181</a></td>
+      <td>impl_iohexperimenter</td>
+      <td></td>
+      <td></td>
+      <td>artificial</td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>IOHexperimenter</td>
+      <td>C++/Python</td>
+      <td></td>
+      <td><a href="https://github.com/IOHprofiler/IOHexperimenter">https://github.com/IOHprofiler/IOHexperimenter</a></td>
+      <td>IOHprofiler experimenter framework</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_tulipa_energy">
+      <td>suite_tulipa_energy</td>
+      <td>TulipaEnergy</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>1</td>
+      <td>noisy | multi-fidelity</td>
+      <td>unknown</td>
+      <td>>=2</td>
+      <td></td>
+      <td>parameter</td>
+      <td></td>
+      <td></td>
+      <td>[1, 2]</td>
+      <td>TulipaEnergyModel.jl</td>
+      <td>Determine the optimal investment and operation decisions for different assets in the energy system (production, consumption, conversion, storage, transport) while minimizing loss of load. Modelled as a potentially very large linear program with multiple fidelity levels.</td>
+      <td></td>
+      <td>TulipaEnergyModel.jl scientific references, <a href="https://tulipaenergy.github.io/TulipaEnergyModel.jl/stable/40-scientific-foundation/45-scientific-references">https://tulipaenergy.github.io/TulipaEnergyModel.jl/stable/40-scientific-foundation/45-scientific-references</a></td>
+      <td>impl_tulipa</td>
+      <td>unimodal</td>
+      <td></td>
+      <td>real-world</td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>TulipaEnergyModel.jl</td>
+      <td>Julia / JuMP</td>
+      <td>minutes to hours</td>
+      <td><a href="https://tulipaenergy.github.io/TulipaEnergyModel.jl/stable/">https://tulipaenergy.github.io/TulipaEnergyModel.jl/stable/</a> | <a href="https://github.com/TulipaEnergy/Tulipa-OBZ-CaseStudy">https://github.com/TulipaEnergy/Tulipa-OBZ-CaseStudy</a></td>
+      <td>Large linear program for optimal investment and operation of energy systems</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_vehicle_dynamics">
+      <td>suite_vehicle_dynamics</td>
+      <td>VehicleDynamics</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>2</td>
+      <td>1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>VehicleDynamics benchmark, <a href="https://www.scitepress.org/Papers/2023/121580/121580.pdf">https://www.scitepress.org/Papers/2023/121580/121580.pdf</a></td>
+      <td>impl_vehicle_dynamics</td>
+      <td>multimodal</td>
+      <td></td>
+      <td>real-world</td>
+      <td></td>
+      <td></td>
+      <td>2</td>
+      <td></td>
+      <td>VehicleDynamics (Zenodo)</td>
+      <td></td>
+      <td></td>
+      <td><a href="https://zenodo.org/records/8307853">https://zenodo.org/records/8307853</a></td>
+      <td>Zenodo archive for the vehicle dynamics benchmark</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_wfg">
+      <td>suite_wfg</td>
+      <td>WFG</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>[10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>A review of multiobjective test problems and a scalable test problem toolkit, Simon Huband; Philip Hingston; Luigi Barone; Lyndon While, <a href="https://doi.org/10.1109/TEVC.2005.861417">https://doi.org/10.1109/TEVC.2005.861417</a></td>
+      <td>impl_pymoo</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>pymoo</td>
+      <td>Python</td>
+      <td></td>
+      <td><a href="https://github.com/anyoptimization/pymoo">https://github.com/anyoptimization/pymoo</a></td>
+      <td>Multi-objective optimization in Python</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_zdt">
+      <td>suite_zdt</td>
+      <td>ZDT</td>
+      <td>Suite</td>
+      <td>continuous | binary</td>
+      <td>>=2</td>
+      <td>2</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Comparison of multiobjective evolutionary algorithms: empirical results, Eckart Zitzler; Kalyanmoy Deb; Lothar Thiele, <a href="https://doi.org/10.1162/106365600568202">https://doi.org/10.1162/106365600568202</a></td>
+      <td>impl_pymoo</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>pymoo</td>
+      <td>Python</td>
+      <td></td>
+      <td><a href="https://github.com/anyoptimization/pymoo">https://github.com/anyoptimization/pymoo</a></td>
+      <td>Multi-objective optimization in Python</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+  </tbody>
+<tfoot><tr><th>ID</th> <th>Name</th> <th>Type</th> <th>Variable Types</th> <th>Total Variables</th> <th>Objectives</th> <th>Properties</th> <th>Constraint Types</th> <th>Total Constraints</th> <th>Dynamics</th> <th>Noise</th> <th>Partial Evaluations</th> <th>Independent Objectives</th> <th>Fidelity Levels</th> <th>Full Name</th> <th>Description</th> <th>Tags</th> <th>References</th> <th>Implementations</th> <th>Modality</th> <th>Examples</th> <th>Source</th> <th>Binary Vars</th> <th>Categorical Vars</th> <th>Continuous Vars</th> <th>Integer Vars</th> <th>Implementation Names</th> <th>Implementation Languages</th> <th>Implementation Evaluation Times</th> <th>Implementation Links</th> <th>Implementation Descriptions</th> <th>Implementation Requirements</th> <th>Hard Box Constraints</th> <th>Soft Box Constraints</th> <th>Hard Linear Constraints</th> <th>Soft Linear Constraints</th> <th>Hard Function Constraints</th> <th>Soft Function Constraints</th></tr></tfoot></table>
+    </div>
+</section>
+
+<section class="details-shell" id="problem-details" aria-live="polite">
+    <h3 class="details-title">Problem details</h3>
+    <p class="details-hint" id="problem-details-hint">Click a table row to inspect full details.</p>
+    <dl class="details-grid" id="problem-details-content"></dl>
+    <div class="snippet-shell" id="problem-snippet-shell">
+        <h4 class="snippet-title">README.md</h4>
+        <p class="snippet-hint" id="problem-readme-hint">README content for this snippet folder will appear here.</p>
+        <div class="snippet-readme markdown-body" id="problem-readme-content"></div>
+        <h4 class="snippet-title">Snippet: <code id="problem-snippet-path">call_{problem_id}.py</code></h4>
+        <p class="snippet-meta"><a id="problem-snippet-folder-link" href="#" target="_blank" rel="noopener noreferrer">Open snippet folder</a></p>
+        <p class="snippet-hint" id="problem-snippet-hint">Select a problem to check for an available code snippet.</p>
+        <pre class="snippet-code"><code id="problem-snippet-content"></code></pre>
+    </div>
+</section>
 
 
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-            </div>
-            <div class="column-controls">
-                <label class="column-chip"><input class="col-toggle" type="checkbox" data-column="0"><span>ID</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="1" checked><span>Name</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="2" checked><span>Type</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="3" checked><span>Variable Types</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="4" checked><span>Total Variables</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="5" checked><span>Objectives</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="6" checked><span>Properties</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="7" checked><span>Constraint Types</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="8" checked><span>Total Constraints</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="9" checked><span>Dynamics</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="10" checked><span>Noise</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="11" checked><span>Partial Evaluations</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="12" checked><span>Independent Objectives</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="13" checked><span>Fidelity Levels</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="14"><span>Full Name</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="15"><span>Description</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="16"><span>Tags</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="17"><span>References</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="18"><span>Implementations</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="19"><span>Modality</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="20"><span>Examples</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="21"><span>Source</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="22"><span>Binary Vars</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="23"><span>Categorical Vars</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="24"><span>Continuous Vars</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="25"><span>Integer Vars</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="26"><span>Implementation Names</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="27"><span>Implementation Languages</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="28"><span>Implementation Evaluation Times</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="29"><span>Implementation Links</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="30"><span>Implementation Descriptions</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="31"><span>Implementation Requirements</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="32"><span>Hard Box Constraints</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="33"><span>Soft Box Constraints</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="34"><span>Hard Linear Constraints</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="35"><span>Soft Linear Constraints</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="36"><span>Hard Function Constraints</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="37"><span>Soft Function Constraints</span></label>
-            </div>
-        </details>
-    </div>
-    <div class="table-wrap">
-        <table class="dataframe display compact display styled-table" id="problems">
-  <thead>
-    <tr style="text-align: right;">
-      <th>ID</th>
-      <th>Name</th>
-      <th>Type</th>
-      <th>Variable Types</th>
-      <th>Total Variables</th>
-      <th>Objectives</th>
-      <th>Properties</th>
-      <th>Constraint Types</th>
-      <th>Total Constraints</th>
-      <th>Dynamics</th>
-      <th>Noise</th>
-      <th>Partial Evaluations</th>
-      <th>Independent Objectives</th>
-      <th>Fidelity Levels</th>
-      <th>Full Name</th>
-      <th>Description</th>
-      <th>Tags</th>
-      <th>References</th>
-      <th>Implementations</th>
-      <th>Modality</th>
-      <th>Examples</th>
-      <th>Source</th>
-      <th>Binary Vars</th>
-      <th>Categorical Vars</th>
-      <th>Continuous Vars</th>
-      <th>Integer Vars</th>
-      <th>Implementation Names</th>
-      <th>Implementation Languages</th>
-      <th>Implementation Evaluation Times</th>
-      <th>Implementation Links</th>
-      <th>Implementation Descriptions</th>
-      <th>Implementation Requirements</th>
-      <th>Hard Box Constraints</th>
-      <th>Soft Box Constraints</th>
-      <th>Hard Linear Constraints</th>
-      <th>Soft Linear Constraints</th>
-      <th>Hard Function Constraints</th>
-      <th>Soft Function Constraints</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr data-problem-id="fn_ato">
-      <td>fn_ato</td>
-      <td>ATO</td>
-      <td>Problem</td>
-      <td>continuous</td>
-      <td>10</td>
-      <td>2</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>no</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>Parameters of the Modules of the Automatic Train Operation are optimized; two objectives: minimizing energy consumption and minimizing driving duration.</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>unimodal</td>
-      <td></td>
-      <td>real-world</td>
-      <td></td>
-      <td></td>
-      <td>10</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="fn_building_spatial">
-      <td>fn_building_spatial</td>
-      <td>Building spatial design</td>
-      <td>Problem</td>
-      <td>binary | continuous</td>
-      <td>>=2</td>
-      <td>2</td>
-      <td></td>
-      <td>unknown | box</td>
-      <td>>=2</td>
-      <td></td>
-      <td></td>
-      <td>no</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>Optimise the spatial layout of a building to minimise energy consumption for climate control and minimise the strain on the structure. Many hard constraints; mixed-variable (continuous+binary); expensive evaluations.</td>
-      <td></td>
-      <td>Building spatial design, <a href="https://hdl.handle.net/1887/81789">https://hdl.handle.net/1887/81789</a></td>
-      <td>impl_bso_toolbox</td>
-      <td></td>
-      <td></td>
-      <td>real-world</td>
-      <td>>=1</td>
-      <td></td>
-      <td>>=1</td>
-      <td></td>
-      <td>BSO-toolbox</td>
-      <td>C++</td>
-      <td>~1s (smallest) to ~40s (larger)</td>
-      <td><a href="https://github.com/TUe-excellent-buildings/BSO-toolbox">https://github.com/TUe-excellent-buildings/BSO-toolbox</a></td>
-      <td>Building Spatial Design toolbox (TU/e)</td>
-      <td></td>
-      <td>>=1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="fn_convex_dtlz2">
-      <td>fn_convex_dtlz2</td>
-      <td>Convex DTLZ2</td>
-      <td>Problem</td>
-      <td>continuous</td>
-      <td>>=1</td>
-      <td>[10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>Variant of DTLZ2 with a convex Pareto front (instead of concave)</td>
-      <td></td>
-      <td>Convex DTLZ2, <a href="https://doi.org/10.1109/TEVC.2013.2281535">https://doi.org/10.1109/TEVC.2013.2281535</a></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>>=1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="fn_emdo">
-      <td>fn_emdo</td>
-      <td>Electric Motor Design Optimization</td>
-      <td>Problem</td>
-      <td>integer | continuous</td>
-      <td>26</td>
-      <td>1</td>
-      <td>noisy</td>
-      <td>box | unknown</td>
-      <td>>=14</td>
-      <td></td>
-      <td>noisy</td>
-      <td>no</td>
-      <td></td>
-      <td></td>
-      <td>Electric Motor Design Optimization</td>
-      <td># Goal\nFind a design of a synchronous electric motor for power steering systems that minimizes costs and satisfies all constraints.\n\n# Motivation\nChallenging to find good solutions in a limited time.\n\n# Key Challenges\n* Time-consuming solution evaluation\n* Highly-constrained problem\n* Constraints are multimodal\n\nThis is not an available problem, but could be interesting to show to researchers which difficulties appear in real-world problems.</td>
-      <td></td>
-      <td>A Multi-Step Evaluation Process in Electric Motor Design, Tea Tušar; Peter Korošec; Bogdan Filipič, <a href="https://dis.ijs.si/tea/Publications/Tusar23Multistep.pdf">https://dis.ijs.si/tea/Publications/Tusar23Multistep.pdf</a></td>
-      <td>impl_emdo</td>
-      <td>multimodal</td>
-      <td></td>
-      <td>real-world</td>
-      <td></td>
-      <td></td>
-      <td>13</td>
-      <td>13</td>
-      <td>Electric Motor Design Optimization</td>
-      <td>Python</td>
-      <td>8 minutes</td>
-      <td></td>
-      <td>Not publicly available</td>
-      <td></td>
-      <td>>=1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="fn_fleetopt">
-      <td>fn_fleetopt</td>
-      <td>FleetOpt</td>
-      <td>Problem</td>
-      <td>integer</td>
-      <td>{54, 13208}</td>
-      <td>1</td>
-      <td></td>
-      <td>unknown</td>
-      <td>>=1</td>
-      <td></td>
-      <td></td>
-      <td>yes</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>UK healthcare organisation fleet optimisation: reduce the fleet of non-emergency healthcare trip vehicles while still ensuring all trips can be covered. Bilevel: upper level 54 vars, lower level 13208 vars.</td>
-      <td></td>
-      <td>FleetOpt, <a href="https://dl.acm.org/doi/abs/10.1145/3638530.3664137">https://dl.acm.org/doi/abs/10.1145/3638530.3664137</a></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>real-world</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>{54, 13208}</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="fn_gasoline">
-      <td>fn_gasoline</td>
-      <td>Gasoline direct injection engine design</td>
-      <td>Problem</td>
-      <td>integer | continuous</td>
-      <td>14</td>
-      <td>2</td>
-      <td>multi-fidelity</td>
-      <td>unknown</td>
-      <td>5</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>[1, 2]</td>
-      <td></td>
-      <td>Multi-objective optimization to minimize fuel consumption and NOx emissions over a two-minute dynamic duty cycle, subject to five constraints (turbine inlet temperature, knock occurrences, peak cylinder pressure, peak cylinder pressure rise, total work). Seven decision variables cover hardware choices and engine control parameters.</td>
-      <td></td>
-      <td>Gasoline direct injection engine design, <a href="https://doi.org/10.1016/j.ejor.2022.08.032">https://doi.org/10.1016/j.ejor.2022.08.032</a></td>
-      <td>impl_gasoline</td>
-      <td></td>
-      <td></td>
-      <td>real-world</td>
-      <td></td>
-      <td></td>
-      <td>7</td>
-      <td>7</td>
-      <td>Gasoline direct injection engine design</td>
-      <td>Matlab Simulink / Wave RT</td>
-      <td></td>
-      <td><a href="https://doi.org/10.1016/j.ejor.2022.08.032">https://doi.org/10.1016/j.ejor.2022.08.032</a></td>
-      <td>Proprietary Matlab Simulink + Wave RT co-simulation</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="fn_invdeceptive_deceptive_rotell">
-      <td>fn_invdeceptive_deceptive_rotell</td>
-      <td>InverseDeceptiveTrap+RotatedEllipsoid / DeceptiveTrap+RotatedEllipsoid</td>
-      <td>Problem</td>
-      <td>binary | continuous</td>
-      <td>>=2</td>
-      <td>2</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>Mixed-variable multi-objective test problems, <a href="https://doi.org/10.1145/3449726.3459521">https://doi.org/10.1145/3449726.3459521</a></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>artificial</td>
-      <td>>=1</td>
-      <td></td>
-      <td>>=1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="fn_inverted_dtlz1">
-      <td>fn_inverted_dtlz1</td>
-      <td>Inverted DTLZ1</td>
-      <td>Problem</td>
-      <td>continuous</td>
-      <td>>=1</td>
-      <td>[10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>Variant of DTLZ1 with an inverted Pareto front</td>
-      <td></td>
-      <td>Inverted DTLZ1, <a href="https://doi.org/10.1109/TEVC.2013.2281534">https://doi.org/10.1109/TEVC.2013.2281534</a></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>>=1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="fn_jsec2019">
-      <td>fn_jsec2019</td>
-      <td>JSEC2019</td>
-      <td>Problem</td>
-      <td>continuous</td>
-      <td>32</td>
-      <td>[1, 2, 3, 4, 5]</td>
-      <td></td>
-      <td>unknown</td>
-      <td>22</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>expensive evaluations 3s; 22 constraints</td>
-      <td></td>
-      <td>JPNSEC EC-Symposium 2019 competition, <a href="http://www.jpnsec.org/files/competition2019/EC-Symposium-2019-Competition-English.html">http://www.jpnsec.org/files/competition2019/EC-Symposium-2019-Competition-English.html</a></td>
-      <td>impl_jsec2019</td>
-      <td></td>
-      <td></td>
-      <td>real-world</td>
-      <td></td>
-      <td></td>
-      <td>32</td>
-      <td></td>
-      <td>JSEC 2019 competition</td>
-      <td></td>
-      <td>3s</td>
-      <td><a href="http://www.jpnsec.org/files/competition2019/EC-Symposium-2019-Competition-English.html">http://www.jpnsec.org/files/competition2019/EC-Symposium-2019-Competition-English.html</a></td>
-      <td>JPNSEC EC-Symposium 2019 competition problem</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="fn_onemax_sphere_deceptive_rotell">
-      <td>fn_onemax_sphere_deceptive_rotell</td>
-      <td>Onemax+Sphere / DeceptiveTrap+RotatedEllipsoid</td>
-      <td>Problem</td>
-      <td>binary | continuous</td>
-      <td>>=2</td>
-      <td>2</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>Mixed-variable multi-objective test problems, <a href="https://doi.org/10.1145/3449726.3459521">https://doi.org/10.1145/3449726.3459521</a></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>artificial</td>
-      <td>>=1</td>
-      <td></td>
-      <td>>=1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="fn_onemax_sphere_zeromax_sphere">
-      <td>fn_onemax_sphere_zeromax_sphere</td>
-      <td>Onemax+Sphere / Zeromax+Sphere</td>
-      <td>Problem</td>
-      <td>binary | continuous</td>
-      <td>>=2</td>
-      <td>2</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>Onemax+Sphere / Zeromax+Sphere, <a href="https://doi.org/10.1145/3449726.3459521">https://doi.org/10.1145/3449726.3459521</a></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>artificial</td>
-      <td>>=1</td>
-      <td></td>
-      <td>>=1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="fn_radar_waveform">
-      <td>fn_radar_waveform</td>
-      <td>Radar waveform</td>
-      <td>Problem</td>
-      <td>integer</td>
-      <td>4-12</td>
-      <td>9</td>
-      <td></td>
-      <td>unknown</td>
-      <td>>=1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>Radar waveform design, <a href="https://doi.org/10.1007/978-3-540-70928-2_53">https://doi.org/10.1007/978-3-540-70928-2_53</a></td>
-      <td>impl_radar_waveform</td>
-      <td></td>
-      <td></td>
-      <td>real-world</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>4-12</td>
-      <td>Evan Hughes radar waveform code</td>
-      <td></td>
-      <td></td>
-      <td><a href="http://code.evanhughes.org/">http://code.evanhughes.org/</a></td>
-      <td>Radar waveform design reference implementation</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="gen_beacon">
-      <td>gen_beacon</td>
-      <td>BEACON</td>
-      <td>Generator</td>
-      <td>continuous</td>
-      <td>>=1</td>
-      <td>2</td>
-      <td></td>
-      <td>box</td>
-      <td>0</td>
-      <td></td>
-      <td></td>
-      <td>no</td>
-      <td></td>
-      <td></td>
-      <td>Continuous Bi-objective Benchmark problems with Explicit Adjustable COrrelatioN control</td>
-      <td>Generator for bi-objective benchmark problems with explicitly controlled correlations in continuous spaces. Multimodal with random structure.</td>
-      <td></td>
-      <td>BEACON, <a href="https://dl.acm.org/doi/10.1145/3712255.3734303">https://dl.acm.org/doi/10.1145/3712255.3734303</a></td>
-      <td>impl_beacon</td>
-      <td>multimodal</td>
-      <td></td>
-      <td>artificial</td>
-      <td></td>
-      <td></td>
-      <td>>=1</td>
-      <td></td>
-      <td>BEACON</td>
-      <td>Python</td>
-      <td>negligible</td>
-      <td><a href="https://github.com/Stebbet/BEACON/">https://github.com/Stebbet/BEACON/</a></td>
-      <td>Continuous Bi-objective Benchmark with Explicit Adjustable COrrelatioN control</td>
-      <td></td>
-      <td>0</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="gen_bono_bench">
-      <td>gen_bono_bench</td>
-      <td>BONO-Bench</td>
-      <td>Generator</td>
-      <td>continuous</td>
-      <td>>=1</td>
-      <td>2</td>
-      <td></td>
-      <td>box</td>
-      <td>>=1</td>
-      <td></td>
-      <td></td>
-      <td>no</td>
-      <td></td>
-      <td></td>
-      <td>Bi-objective Numerical Optimization Benchmark</td>
-      <td>Bi-objective problem generator and suite with scalable continuous decision space. Features complex problem properties and Pareto front approximations with error guarantees for the hypervolume and exact R2 indicators.</td>
-      <td></td>
-      <td></td>
-      <td>impl_bonobench</td>
-      <td>multimodal</td>
-      <td></td>
-      <td>artificial</td>
-      <td></td>
-      <td></td>
-      <td>>=1</td>
-      <td></td>
-      <td>BONO-Bench</td>
-      <td>Python</td>
-      <td></td>
-      <td><a href="https://github.com/schaepermeier/bonobench">https://github.com/schaepermeier/bonobench</a></td>
-      <td>Bi-objective Numerical Optimization Benchmark (BONO-Bench)</td>
-      <td></td>
-      <td>>=1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="gen_ealain">
-      <td>gen_ealain</td>
-      <td>Ealain</td>
-      <td>Generator</td>
-      <td>binary | continuous | integer</td>
-      <td>>=3</td>
-      <td>[1, 10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
-      <td>dynamic | multi-fidelity</td>
-      <td>unknown</td>
-      <td>>=1</td>
-      <td>optional</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>[1, 2]</td>
-      <td></td>
-      <td>Real-world-like, easily extensible to increase complexity</td>
-      <td></td>
-      <td>Ealain, <a href="https://doi.org/10.1145/3638530.3654299">https://doi.org/10.1145/3638530.3654299</a></td>
-      <td>impl_ealain</td>
-      <td></td>
-      <td></td>
-      <td>real-world-like</td>
-      <td>>=1</td>
-      <td></td>
-      <td>>=1</td>
-      <td>>=1</td>
-      <td>Ealain</td>
-      <td></td>
-      <td></td>
-      <td><a href="https://github.com/qrenau/Ealain">https://github.com/qrenau/Ealain</a></td>
-      <td>Real-world-like extensible benchmark problem generator</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="gen_gnbg">
-      <td>gen_gnbg</td>
-      <td>GNBG</td>
-      <td>Generator</td>
-      <td>continuous</td>
-      <td>>=1</td>
-      <td>1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>Generator counterpart of GNBG.</td>
-      <td></td>
-      <td>GNBG, <a href="https://arxiv.org/abs/2312.07083">https://arxiv.org/abs/2312.07083</a></td>
-      <td>impl_gnbg</td>
-      <td></td>
-      <td></td>
-      <td>artificial</td>
-      <td></td>
-      <td></td>
-      <td>>=1</td>
-      <td></td>
-      <td>GNBG Generator</td>
-      <td></td>
-      <td></td>
-      <td><a href="https://github.com/Danial-Yazdani/GNBG-Generator">https://github.com/Danial-Yazdani/GNBG-Generator</a></td>
-      <td>Generalized Numerical Benchmark Generator</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="gen_gnbg_ii">
-      <td>gen_gnbg_ii</td>
-      <td>GNBG-II</td>
-      <td>Generator</td>
-      <td>continuous</td>
-      <td>>=1</td>
-      <td>1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>Generator counterpart of GNBG-II.</td>
-      <td></td>
-      <td>GNBG-II, <a href="https://dl.acm.org/doi/pdf/10.1145/3712255.3734271">https://dl.acm.org/doi/pdf/10.1145/3712255.3734271</a></td>
-      <td>["impl_gnbg_ii", "impl_iohgnbg"]</td>
-      <td></td>
-      <td></td>
-      <td>artificial</td>
-      <td></td>
-      <td></td>
-      <td>>=1</td>
-      <td></td>
-      <td>GNBG-II | IOHGNBG</td>
-      <td></td>
-      <td></td>
-      <td><a href="https://github.com/rohitsalgotra/GNBG-II">https://github.com/rohitsalgotra/GNBG-II</a> | <a href="https://github.com/IOHprofiler/IOHGNBG">https://github.com/IOHprofiler/IOHGNBG</a></td>
-      <td>Generalized Numerical Benchmark Generator version 2 | IOHprofiler version of GNBG</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="gen_gpd">
-      <td>gen_gpd</td>
-      <td>GPD</td>
-      <td>Generator</td>
-      <td>continuous</td>
-      <td>>=1</td>
-      <td>[10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
-      <td>noisy</td>
-      <td>unknown</td>
-      <td>>=1</td>
-      <td></td>
-      <td>optional</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>GPD generator, <a href="https://doi.org/10.1016/j.asoc.2020.106139">https://doi.org/10.1016/j.asoc.2020.106139</a></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>>=1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="gen_iohclustering">
-      <td>gen_iohclustering</td>
-      <td>IOHClustering</td>
-      <td>Generator</td>
-      <td>continuous</td>
-      <td>>=1</td>
-      <td>1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>Generator counterpart of the IOHClustering suite.</td>
-      <td></td>
-      <td>IOHClustering, <a href="https://arxiv.org/pdf/2505.09233">https://arxiv.org/pdf/2505.09233</a></td>
-      <td>impl_iohclustering</td>
-      <td>multimodal</td>
-      <td></td>
-      <td>artificial-from-real-data</td>
-      <td></td>
-      <td></td>
-      <td>>=1</td>
-      <td></td>
-      <td>IOHClustering</td>
-      <td></td>
-      <td></td>
-      <td><a href="https://github.com/IOHprofiler/IOHClustering">https://github.com/IOHprofiler/IOHClustering</a></td>
-      <td>Clustering-based optimization benchmark built on ML datasets</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="gen_ma_bbob">
-      <td>gen_ma_bbob</td>
-      <td>MA-BBOB</td>
-      <td>Generator</td>
-      <td>continuous</td>
-      <td>>=1</td>
-      <td>1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>Generator that creates affine combinations of BBOB functions</td>
-      <td></td>
-      <td>MA-BBOB, <a href="https://doi.org/10.1145/3673908">https://doi.org/10.1145/3673908</a></td>
-      <td>["impl_iohexperimenter", "impl_ma_bbob"]</td>
-      <td>multimodal</td>
-      <td></td>
-      <td>artificial</td>
-      <td></td>
-      <td></td>
-      <td>>=1</td>
-      <td></td>
-      <td>IOHexperimenter | MA-BBOB (IOHexperimenter)</td>
-      <td>C++/Python</td>
-      <td></td>
-      <td><a href="https://github.com/IOHprofiler/IOHexperimenter">https://github.com/IOHprofiler/IOHexperimenter</a> | <a href="https://github.com/IOHprofiler/IOHexperimenter/blob/master/example/Competitions/MA-BBOB/Example_MABBOB.ipynb">https://github.com/IOHprofiler/IOHexperimenter/blob/master/example/Competitions/MA-BBOB/Example_MABBOB.ipynb</a></td>
-      <td>IOHprofiler experimenter framework | Example notebook for MA-BBOB in IOHexperimenter</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="gen_mpm2">
-      <td>gen_mpm2</td>
-      <td>MPM2</td>
-      <td>Generator</td>
-      <td>continuous</td>
-      <td>>=1</td>
-      <td>1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>nonlinear nonseparable nonsymmetric; scalable in terms of time to evaluate the objective function</td>
-      <td></td>
-      <td>MPM2 technical report TR15-01, <a href="https://ls11-www.cs.tu-dortmund.de/_media/techreports/tr15-01.pdf">https://ls11-www.cs.tu-dortmund.de/_media/techreports/tr15-01.pdf</a></td>
-      <td>impl_mpm2</td>
-      <td>multimodal</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>>=1</td>
-      <td></td>
-      <td>MPM2 (smoof)</td>
-      <td>Python</td>
-      <td></td>
-      <td><a href="https://github.com/jakobbossek/smoof/blob/master/inst/mpm2.py">https://github.com/jakobbossek/smoof/blob/master/inst/mpm2.py</a></td>
-      <td>Python implementation of MPM2 distributed with smoof</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="gen_mubqp">
-      <td>gen_mubqp</td>
-      <td>mUBQP</td>
-      <td>Generator</td>
-      <td>binary</td>
-      <td>>=1</td>
-      <td>[1, 10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>tunable variable and objective dimensions; tunable density and correlation between objectives</td>
-      <td></td>
-      <td>mUBQP benchmark, <a href="https://doi.org/10.1016/j.asoc.2013.11.008">https://doi.org/10.1016/j.asoc.2013.11.008</a></td>
-      <td>impl_mocobench</td>
-      <td>["multimodal", "quadratic"]</td>
-      <td></td>
-      <td></td>
-      <td>>=1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>mocobench</td>
-      <td>C++</td>
-      <td></td>
-      <td><a href="https://gitlab.com/aliefooghe/mocobench/">https://gitlab.com/aliefooghe/mocobench/</a></td>
-      <td>Multi-objective combinatorial optimization benchmark</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="gen_puboi">
-      <td>gen_puboi</td>
-      <td>PUBOi</td>
-      <td>Generator</td>
-      <td>binary</td>
-      <td>>=1</td>
-      <td>1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>no</td>
-      <td></td>
-      <td></td>
-      <td>Polynomial Unconstrained Binary Optimization with tunable importance</td>
-      <td>A benchmark in which variable importance is tunable, based on the Walsh function.</td>
-      <td></td>
-      <td>PUBOi, <a href="https://link.springer.com/chapter/10.1007/978-3-031-04148-8_12">https://link.springer.com/chapter/10.1007/978-3-031-04148-8_12</a></td>
-      <td>impl_puboi</td>
-      <td></td>
-      <td></td>
-      <td>artificial</td>
-      <td>>=1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>PUBO Importance Benchmark</td>
-      <td>Python / C++</td>
-      <td></td>
-      <td><a href="https://gitlab.com/verel/pubo-importance-benchmark">https://gitlab.com/verel/pubo-importance-benchmark</a></td>
-      <td>A benchmark in which variable importance is tunable, based on the Walsh function</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="gen_randoptgen">
-      <td>gen_randoptgen</td>
-      <td>RandOptGen</td>
-      <td>Generator</td>
-      <td>binary | continuous | integer</td>
-      <td>>=3</td>
-      <td>[1, 10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>no</td>
-      <td></td>
-      <td></td>
-      <td>RandOptGen</td>
-      <td>A Unified Random Problem Generator for Single- and Multi-Objective Optimization Problems with Mixed-Variable Input Spaces.</td>
-      <td></td>
-      <td></td>
-      <td>impl_randoptgen</td>
-      <td>multimodal</td>
-      <td></td>
-      <td>artificial</td>
-      <td>>=1</td>
-      <td></td>
-      <td>>=1</td>
-      <td>>=1</td>
-      <td>RandOptGen</td>
-      <td>Python</td>
-      <td>milliseconds</td>
-      <td><a href="https://github.com/MALEO-research-group/RandOptGen">https://github.com/MALEO-research-group/RandOptGen</a> | <a href="https://doi.org/10.1145/3712256.3726478">https://doi.org/10.1145/3712256.3726478</a></td>
-      <td>Unified Random Problem Generator for Single- and Multi-Objective Optimization with Mixed-Variable Input Spaces</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="gen_rho_mnk_landscapes">
-      <td>gen_rho_mnk_landscapes</td>
-      <td>ρMNK-Landscapes</td>
-      <td>Generator</td>
-      <td>binary</td>
-      <td>>=1</td>
-      <td>[1, 10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>tunable variable and objective dimensions; tunable multimodality and correlation between objectives</td>
-      <td></td>
-      <td>On the design of multi-objective evolutionary algorithms based on NK-landscapes, <a href="https://doi.org/10.1016/j.ejor.2012.12.019">https://doi.org/10.1016/j.ejor.2012.12.019</a></td>
-      <td>impl_mocobench</td>
-      <td>multimodal</td>
-      <td></td>
-      <td></td>
-      <td>>=1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>mocobench</td>
-      <td>C++</td>
-      <td></td>
-      <td><a href="https://gitlab.com/aliefooghe/mocobench/">https://gitlab.com/aliefooghe/mocobench/</a></td>
-      <td>Multi-objective combinatorial optimization benchmark</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="gen_rho_mtsp">
-      <td>gen_rho_mtsp</td>
-      <td>ρmTSP</td>
-      <td>Generator</td>
-      <td>unknown</td>
-      <td>>=1</td>
-      <td>[1, 10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>tunable variable and objective dimensions; tunable instance type (euclidean/random); tunable correlation between objectives</td>
-      <td></td>
-      <td>On the impact of multi-objective scalability for the ρmTSP, <a href="https://doi.org/10.1007/978-3-319-45823-6_40">https://doi.org/10.1007/978-3-319-45823-6_40</a></td>
-      <td>impl_mocobench</td>
-      <td>["multimodal", "quadratic"]</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>mocobench</td>
-      <td>C++</td>
-      <td></td>
-      <td><a href="https://gitlab.com/aliefooghe/mocobench/">https://gitlab.com/aliefooghe/mocobench/</a></td>
-      <td>Multi-objective combinatorial optimization benchmark</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="gen_wmodel">
-      <td>gen_wmodel</td>
-      <td>W-model</td>
-      <td>Generator</td>
-      <td>binary</td>
-      <td>>=1</td>
-      <td>1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>Tunable generator for binary optimization based on several difficulty features</td>
-      <td></td>
-      <td>W-model, <a href="https://dl.acm.org/doi/abs/10.1145/3205651.3208240">https://dl.acm.org/doi/abs/10.1145/3205651.3208240</a></td>
-      <td>impl_wmodel</td>
-      <td></td>
-      <td></td>
-      <td>artificial</td>
-      <td>>=1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>BBDOB W-Model</td>
-      <td></td>
-      <td></td>
-      <td><a href="https://github.com/thomasWeise/BBDOB_W_Model">https://github.com/thomasWeise/BBDOB_W_Model</a></td>
-      <td>Tunable generator for binary optimization</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_amvop">
-      <td>suite_amvop</td>
-      <td>AMVOP</td>
-      <td>Suite</td>
-      <td>integer | continuous | categorical</td>
-      <td>>=3</td>
-      <td>1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>AMVOP, <a href="https://doi.org/10.1109/TEVC.2013.2281531">https://doi.org/10.1109/TEVC.2013.2281531</a></td>
-      <td></td>
-      <td>multimodal</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>>=1</td>
-      <td>>=1</td>
-      <td>>=1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_bbob">
-      <td>suite_bbob</td>
-      <td>BBOB</td>
-      <td>Suite</td>
-      <td>continuous</td>
-      <td>>=1</td>
-      <td>1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>COCO: a platform for comparing continuous optimizers in a black-box setting, <a href="https://doi.org/10.1080/10556788.2020.1808977">https://doi.org/10.1080/10556788.2020.1808977</a></td>
-      <td>impl_coco</td>
-      <td>multimodal</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>>=1</td>
-      <td></td>
-      <td>COCO framework</td>
-      <td>C/Python</td>
-      <td></td>
-      <td><a href="https://github.com/numbbo/coco">https://github.com/numbbo/coco</a></td>
-      <td>Comparing Continuous Optimizers: black-box optimization benchmarking platform</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_bbob_biobj">
-      <td>suite_bbob_biobj</td>
-      <td>BBOB-biobj</td>
-      <td>Suite</td>
-      <td>continuous</td>
-      <td>2-40</td>
-      <td>2</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>BBOB bi-objective test suite, <a href="https://doi.org/10.48550/arXiv.1604.00359">https://doi.org/10.48550/arXiv.1604.00359</a></td>
-      <td>impl_coco</td>
-      <td>multimodal</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>2-40</td>
-      <td></td>
-      <td>COCO framework</td>
-      <td>C/Python</td>
-      <td></td>
-      <td><a href="https://github.com/numbbo/coco">https://github.com/numbbo/coco</a></td>
-      <td>Comparing Continuous Optimizers: black-box optimization benchmarking platform</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_bbob_biobj_mixint">
-      <td>suite_bbob_biobj_mixint</td>
-      <td>BBOB-biobj-mixint</td>
-      <td>Suite</td>
-      <td>continuous | integer</td>
-      <td>10-320</td>
-      <td>2</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>BBOB bi-objective mixed-integer test suite, <a href="https://doi.org/10.1145/3321707.3321868">https://doi.org/10.1145/3321707.3321868</a></td>
-      <td>impl_coco</td>
-      <td>multimodal</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>5-160</td>
-      <td>5-160</td>
-      <td>COCO framework</td>
-      <td>C/Python</td>
-      <td></td>
-      <td><a href="https://github.com/numbbo/coco">https://github.com/numbbo/coco</a></td>
-      <td>Comparing Continuous Optimizers: black-box optimization benchmarking platform</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_bbob_constrained">
-      <td>suite_bbob_constrained</td>
-      <td>BBOB-constrained</td>
-      <td>Suite</td>
-      <td>continuous</td>
-      <td>2-40</td>
-      <td>1</td>
-      <td></td>
-      <td>unknown</td>
-      <td>>=1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>bbob-constrained documentation, <a href="http://numbbo.github.io/coco-doc/bbob-constrained/">http://numbbo.github.io/coco-doc/bbob-constrained/</a></td>
-      <td>impl_coco</td>
-      <td>multimodal</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>2-40</td>
-      <td></td>
-      <td>COCO framework</td>
-      <td>C/Python</td>
-      <td></td>
-      <td><a href="https://github.com/numbbo/coco">https://github.com/numbbo/coco</a></td>
-      <td>Comparing Continuous Optimizers: black-box optimization benchmarking platform</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_bbob_largescale">
-      <td>suite_bbob_largescale</td>
-      <td>BBOB-largescale</td>
-      <td>Suite</td>
-      <td>continuous</td>
-      <td>20-640</td>
-      <td>1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>BBOB large-scale test suite, <a href="https://doi.org/10.48550/arXiv.1903.06396">https://doi.org/10.48550/arXiv.1903.06396</a></td>
-      <td>impl_coco</td>
-      <td>multimodal</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>20-640</td>
-      <td></td>
-      <td>COCO framework</td>
-      <td>C/Python</td>
-      <td></td>
-      <td><a href="https://github.com/numbbo/coco">https://github.com/numbbo/coco</a></td>
-      <td>Comparing Continuous Optimizers: black-box optimization benchmarking platform</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_bbob_mixint">
-      <td>suite_bbob_mixint</td>
-      <td>BBOB-mixint</td>
-      <td>Suite</td>
-      <td>continuous | integer</td>
-      <td>10-320</td>
-      <td>1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>BBOB mixed-integer test suite, <a href="https://doi.org/10.1145/3321707.3321868">https://doi.org/10.1145/3321707.3321868</a></td>
-      <td>impl_coco</td>
-      <td>multimodal</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>5-160</td>
-      <td>5-160</td>
-      <td>COCO framework</td>
-      <td>C/Python</td>
-      <td></td>
-      <td><a href="https://github.com/numbbo/coco">https://github.com/numbbo/coco</a></td>
-      <td>Comparing Continuous Optimizers: black-box optimization benchmarking platform</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_bbob_noisy">
-      <td>suite_bbob_noisy</td>
-      <td>BBOB-noisy</td>
-      <td>Suite</td>
-      <td>continuous</td>
-      <td>>=1</td>
-      <td>1</td>
-      <td>noisy</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>noisy</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>Real-parameter black-box optimization benchmarking: noisy functions definitions, <a href="https://hal.inria.fr/inria-00369466">https://hal.inria.fr/inria-00369466</a></td>
-      <td>impl_coco_legacy</td>
-      <td>multimodal</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>>=1</td>
-      <td></td>
-      <td>COCO legacy (bbob-noisy)</td>
-      <td>C/Python</td>
-      <td></td>
-      <td><a href="https://web.archive.org/web/20210416065610/https://coco.gforge.inria.fr/doku.php?id=downloads">https://web.archive.org/web/20210416065610/https://coco.gforge.inria.fr/doku.php?id=downloads</a></td>
-      <td>Archived COCO download page that hosted the bbob-noisy suite</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_bp">
-      <td>suite_bp</td>
-      <td>BP</td>
-      <td>Suite</td>
-      <td>continuous</td>
-      <td>>=1</td>
-      <td>[10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
-      <td>noisy</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>unknown</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>BP benchmark, <a href="https://doi.org/10.1109/CEC.2019.8790277">https://doi.org/10.1109/CEC.2019.8790277</a></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>>=1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_brachytherapy">
-      <td>suite_brachytherapy</td>
-      <td>Brachytherapy treatment planning</td>
-      <td>Suite</td>
-      <td>continuous</td>
-      <td>100-500</td>
-      <td>[2, 3]</td>
-      <td>multi-fidelity</td>
-      <td>unknown</td>
-      <td>>=1</td>
-      <td></td>
-      <td></td>
-      <td>yes</td>
-      <td></td>
-      <td>[1, 2]</td>
-      <td>Brachytherapy treatment planning</td>
-      <td>Treatment planning for internal radiation therapy. Multi-objective with aggregated objectives; no public source code.</td>
-      <td></td>
-      <td>Brachytherapy treatment planning, <a href="https://www.sciencedirect.com/science/article/pii/S1538472123016781">https://www.sciencedirect.com/science/article/pii/S1538472123016781</a></td>
-      <td></td>
-      <td>multimodal</td>
-      <td></td>
-      <td>real-world</td>
-      <td></td>
-      <td></td>
-      <td>100-500</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_car_structure">
-      <td>suite_car_structure</td>
-      <td>Car structure</td>
-      <td>Suite</td>
-      <td>integer</td>
-      <td>144-222</td>
-      <td>2</td>
-      <td></td>
-      <td>unknown</td>
-      <td>54</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>54 constraints</td>
-      <td></td>
-      <td>Car structure design benchmark, <a href="https://doi.org/10.1145/3205651.3205702">https://doi.org/10.1145/3205651.3205702</a></td>
-      <td>impl_car_structure</td>
-      <td></td>
-      <td></td>
-      <td>real-world</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>144-222</td>
-      <td>Car-structure benchmark</td>
-      <td></td>
-      <td></td>
-      <td><a href="http://ladse.eng.isas.jaxa.jp/benchmark/">http://ladse.eng.isas.jaxa.jp/benchmark/</a></td>
-      <td>JAXA LADSE benchmark problems</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_cdmp">
-      <td>suite_cdmp</td>
-      <td>CDMP</td>
-      <td>Suite</td>
-      <td>continuous</td>
-      <td>>=1</td>
-      <td>[10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
-      <td>dynamic | noisy</td>
-      <td>unknown</td>
-      <td>>=1</td>
-      <td>unknown</td>
-      <td>unknown</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>CDMP benchmark, <a href="https://doi.org/10.1145/3321707.3321878">https://doi.org/10.1145/3321707.3321878</a></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>>=1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_cec2013">
-      <td>suite_cec2013</td>
-      <td>CEC2013</td>
-      <td>Suite</td>
-      <td>continuous</td>
-      <td>>=1</td>
-      <td>1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>suite used for cec2013 competition. Also in IOH.</td>
-      <td></td>
-      <td>CEC2013 definitions, <a href="https://peerj.com/articles/cs-2671/CEC2013.pdf">https://peerj.com/articles/cs-2671/CEC2013.pdf</a></td>
-      <td>["impl_cec2013", "impl_iohexperimenter"]</td>
-      <td></td>
-      <td></td>
-      <td>artificial</td>
-      <td></td>
-      <td></td>
-      <td>>=1</td>
-      <td></td>
-      <td>IOHexperimenter | CEC2013 reference code</td>
-      <td>C++/Python</td>
-      <td></td>
-      <td><a href="https://github.com/IOHprofiler/IOHexperimenter">https://github.com/IOHprofiler/IOHexperimenter</a> | <a href="https://github.com/P-N-Suganthan/CEC2013">https://github.com/P-N-Suganthan/CEC2013</a></td>
-      <td>IOHprofiler experimenter framework | Suganthan's reference implementation</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_cec2015_dmoo">
-      <td>suite_cec2015_dmoo</td>
-      <td>CEC2015-DMOO</td>
-      <td>Suite</td>
-      <td>continuous</td>
-      <td>0</td>
-      <td>[2, 3]</td>
-      <td>dynamic</td>
-      <td>unknown</td>
-      <td>>=1</td>
-      <td>dynamic</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>Benchmark Functions for CEC 2015 Special Session and Competition on Dynamic Multi-objective Optimization</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>0</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_cec2018_dt">
-      <td>suite_cec2018_dt</td>
-      <td>CEC2018 DT</td>
-      <td>Suite</td>
-      <td>unknown</td>
-      <td>>=1</td>
-      <td>[2, 3]</td>
-      <td>dynamic</td>
-      <td></td>
-      <td></td>
-      <td>dynamic</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>CEC2018 Competition on Dynamic Multiobjective Optimisation</td>
-      <td>14 problems. Time-dependent: Pareto front/Pareto set geometry; irregular Pareto front shapes; variable-linkage; number of disconnected Pareto front segments; etc.</td>
-      <td></td>
-      <td>CEC2018 DMOP Competition TR, <a href="https://www.academia.edu/download/94499025/TR-CEC2018-DMOP-Competition.pdf">https://www.academia.edu/download/94499025/TR-CEC2018-DMOP-Competition.pdf</a></td>
-      <td>impl_pymoo</td>
-      <td></td>
-      <td></td>
-      <td>artificial</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>pymoo</td>
-      <td>Python</td>
-      <td></td>
-      <td><a href="https://github.com/anyoptimization/pymoo">https://github.com/anyoptimization/pymoo</a></td>
-      <td>Multi-objective optimization in Python</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_cec2022">
-      <td>suite_cec2022</td>
-      <td>CEC2022</td>
-      <td>Suite</td>
-      <td>continuous</td>
-      <td>>=1</td>
-      <td>1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>suite used for cec2022 competition. Also in IOH.</td>
-      <td></td>
-      <td>CEC2022 TR, <a href="https://github.com/P-N-Suganthan/2022-SO-BO/blob/main/CEC2022%20TR.pdf">https://github.com/P-N-Suganthan/2022-SO-BO/blob/main/CEC2022%20TR.pdf</a></td>
-      <td>["impl_cec2022", "impl_iohexperimenter"]</td>
-      <td></td>
-      <td></td>
-      <td>artificial</td>
-      <td></td>
-      <td></td>
-      <td>>=1</td>
-      <td></td>
-      <td>IOHexperimenter | CEC2022 reference code</td>
-      <td>C++/Python</td>
-      <td></td>
-      <td><a href="https://github.com/IOHprofiler/IOHexperimenter">https://github.com/IOHprofiler/IOHexperimenter</a> | <a href="https://github.com/P-N-Suganthan/2022-SO-BO">https://github.com/P-N-Suganthan/2022-SO-BO</a></td>
-      <td>IOHprofiler experimenter framework | Suganthan's reference implementation</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_cfd">
-      <td>suite_cfd</td>
-      <td>CFD</td>
-      <td>Suite</td>
-      <td>unknown</td>
-      <td>>=1</td>
-      <td>[1, 2]</td>
-      <td></td>
-      <td>unknown</td>
-      <td>>=1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>expensive evaluations 30s-15m</td>
-      <td></td>
-      <td>CFD test problem suite, <a href="https://doi.org/10.1007/978-3-319-99259-4_24">https://doi.org/10.1007/978-3-319-99259-4_24</a></td>
-      <td>impl_cfd</td>
-      <td></td>
-      <td></td>
-      <td>real-world</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>CFD test problem suite</td>
-      <td></td>
-      <td>30s-15m</td>
-      <td><a href="https://bitbucket.org/arahat/cfd-test-problem-suite">https://bitbucket.org/arahat/cfd-test-problem-suite</a></td>
-      <td>Expensive real-world CFD-based test problems</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_cre">
-      <td>suite_cre</td>
-      <td>CRE</td>
-      <td>Suite</td>
-      <td>integer | continuous</td>
-      <td>6-14</td>
-      <td>[2, 3, 4, 5]</td>
-      <td></td>
-      <td>unknown</td>
-      <td>>=1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>Easy-to-evaluate real-world multi-objective optimization problems, Ryoji Tanabe; Hisao Ishibuchi, <a href="https://doi.org/10.1016/j.asoc.2020.106078">https://doi.org/10.1016/j.asoc.2020.106078</a></td>
-      <td>impl_reproblems</td>
-      <td></td>
-      <td></td>
-      <td>real-world-like</td>
-      <td></td>
-      <td></td>
-      <td>3-7</td>
-      <td>3-7</td>
-      <td>reproblems</td>
-      <td>Python</td>
-      <td></td>
-      <td><a href="https://github.com/ryojitanabe/reproblems">https://github.com/ryojitanabe/reproblems</a></td>
-      <td>Real-world inspired multi-objective optimization problem suite</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_cuter">
-      <td>suite_cuter</td>
-      <td>CUTEr</td>
-      <td>Suite</td>
-      <td>binary | continuous | integer</td>
-      <td>>=3</td>
-      <td>1</td>
-      <td></td>
-      <td>unknown</td>
-      <td>>=1</td>
-      <td></td>
-      <td></td>
-      <td>no</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>A constrained and unconstrained testing environment.</td>
-      <td></td>
-      <td>CUTEr, <a href="https://dl.acm.org/doi/10.1145/962437.962439">https://dl.acm.org/doi/10.1145/962437.962439</a></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>artificial</td>
-      <td>>=1</td>
-      <td></td>
-      <td>>=1</td>
-      <td>>=1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_cutest">
-      <td>suite_cutest</td>
-      <td>CUTEst</td>
-      <td>Suite</td>
-      <td>binary | continuous | integer</td>
-      <td>>=3</td>
-      <td>1</td>
-      <td></td>
-      <td>unknown | box</td>
-      <td>>=2</td>
-      <td></td>
-      <td></td>
-      <td>no</td>
-      <td></td>
-      <td></td>
-      <td>Constrained and Unconstrained Testing Environment with safe threads</td>
-      <td>CUTEst for optimization software</td>
-      <td></td>
-      <td>CUTEst, <a href="https://link.springer.com/article/10.1007/s10589-014-9687-3">https://link.springer.com/article/10.1007/s10589-014-9687-3</a></td>
-      <td>impl_pycutest</td>
-      <td>multimodal</td>
-      <td></td>
-      <td>artificial</td>
-      <td>>=1</td>
-      <td></td>
-      <td>>=1</td>
-      <td>>=1</td>
-      <td>pycutest</td>
-      <td>Python / C++ / Fortran</td>
-      <td></td>
-      <td><a href="https://github.com/jfowkes/pycutest">https://github.com/jfowkes/pycutest</a></td>
-      <td>Python interface to CUTEst</td>
-      <td></td>
-      <td>>=1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_dtlz">
-      <td>suite_dtlz</td>
-      <td>DTLZ</td>
-      <td>Suite</td>
-      <td>continuous</td>
-      <td>>=1</td>
-      <td>[10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>Scalable multi-objective optimization test problems, Kalyanmoy Deb; Lothar Thiele; Marco Laumanns; Eckart Zitzler, <a href="https://doi.org/10.1109/CEC.2002.1007032">https://doi.org/10.1109/CEC.2002.1007032</a></td>
-      <td>impl_pymoo</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>>=1</td>
-      <td></td>
-      <td>pymoo</td>
-      <td>Python</td>
-      <td></td>
-      <td><a href="https://github.com/anyoptimization/pymoo">https://github.com/anyoptimization/pymoo</a></td>
-      <td>Multi-objective optimization in Python</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_dynamicbinval">
-      <td>suite_dynamicbinval</td>
-      <td>DynamicBinVal</td>
-      <td>Suite</td>
-      <td>binary</td>
-      <td>>=1</td>
-      <td>1</td>
-      <td>dynamic</td>
-      <td></td>
-      <td></td>
-      <td>dynamic</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>Four versions of the dynamic binary value problem</td>
-      <td></td>
-      <td>DynamicBinVal, <a href="https://arxiv.org/pdf/2404.15837">https://arxiv.org/pdf/2404.15837</a></td>
-      <td>impl_iohexperimenter</td>
-      <td></td>
-      <td></td>
-      <td>artificial</td>
-      <td>>=1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>IOHexperimenter</td>
-      <td>C++/Python</td>
-      <td></td>
-      <td><a href="https://github.com/IOHprofiler/IOHexperimenter">https://github.com/IOHprofiler/IOHexperimenter</a></td>
-      <td>IOHprofiler experimenter framework</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_emo2017">
-      <td>suite_emo2017</td>
-      <td>EMO2017</td>
-      <td>Suite</td>
-      <td>continuous</td>
-      <td>4-24</td>
-      <td>2</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>BBComp EMO 2017, <a href="https://www.ini.rub.de/PEOPLE/glasmtbl/projects/bbcomp/">https://www.ini.rub.de/PEOPLE/glasmtbl/projects/bbcomp/</a></td>
-      <td>impl_emo2017</td>
-      <td></td>
-      <td></td>
-      <td>real-world</td>
-      <td></td>
-      <td></td>
-      <td>4-24</td>
-      <td></td>
-      <td>EMO 2017 real-world problems</td>
-      <td></td>
-      <td></td>
-      <td><a href="https://www.ini.rub.de/PEOPLE/glasmtbl/projects/bbcomp/downloads/realworld-problems-bbcomp-EMO-2017.zip">https://www.ini.rub.de/PEOPLE/glasmtbl/projects/bbcomp/downloads/realworld-problems-bbcomp-EMO-2017.zip</a></td>
-      <td>BBComp EMO-2017 real-world problem archive</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_etmof">
-      <td>suite_etmof</td>
-      <td>ETMOF</td>
-      <td>Suite</td>
-      <td>continuous</td>
-      <td>25-10000</td>
-      <td>[10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 2, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 3, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 4, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 5, 50, 6, 7, 8, 9]</td>
-      <td>dynamic</td>
-      <td></td>
-      <td></td>
-      <td>dynamic</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>Evolutionary many-task optimization framework, <a href="https://doi.org/10.48550/arXiv.2110.08033">https://doi.org/10.48550/arXiv.2110.08033</a></td>
-      <td>impl_etmof</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>25-10000</td>
-      <td></td>
-      <td>ETMOF</td>
-      <td></td>
-      <td></td>
-      <td><a href="https://github.com/songbai-liu/etmo">https://github.com/songbai-liu/etmo</a></td>
-      <td>Evolutionary many-task optimization framework</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_expobench">
-      <td>suite_expobench</td>
-      <td>EXPObench</td>
-      <td>Suite</td>
-      <td>categorical | continuous | integer</td>
-      <td>30-405</td>
-      <td>1</td>
-      <td>noisy</td>
-      <td>unknown | box</td>
-      <td>>=2</td>
-      <td></td>
-      <td>["observational", "real-life"]</td>
-      <td>no</td>
-      <td></td>
-      <td></td>
-      <td>EXPensive Optimization benchmark library</td>
-      <td>Wind farm layout optimization, gas filter design, pipe shape optimization, hyperparameter tuning, and hospital simulation</td>
-      <td></td>
-      <td>EXPObench, <a href="https://doi.org/10.1016/j.asoc.2023.110744">https://doi.org/10.1016/j.asoc.2023.110744</a></td>
-      <td>impl_expobench</td>
-      <td></td>
-      <td></td>
-      <td>real-world</td>
-      <td></td>
-      <td>10-135</td>
-      <td>10-135</td>
-      <td>10-135</td>
-      <td>EXPObench</td>
-      <td>Python</td>
-      <td>2 to 80 seconds</td>
-      <td><a href="https://github.com/AlgTUDelft/ExpensiveOptimBenchmark">https://github.com/AlgTUDelft/ExpensiveOptimBenchmark</a></td>
-      <td>EXPensive Optimization benchmark library (wind farm layout, gas filter design, pipe shape, hyperparameter tuning, hospital simulation)</td>
-      <td></td>
-      <td>>=1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_gbea">
-      <td>suite_gbea</td>
-      <td>GBEA</td>
-      <td>Suite</td>
-      <td>continuous</td>
-      <td>>=1</td>
-      <td>[1, 2]</td>
-      <td>noisy</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>noisy</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>expensive evaluations 5s-35s, RW-GAN-Mario and TopTrumps are part of GBEA</td>
-      <td></td>
-      <td>Game benchmark for evolutionary algorithms, <a href="https://doi.org/10.1145/3321707.3321805">https://doi.org/10.1145/3321707.3321805</a></td>
-      <td>impl_gbea</td>
-      <td>multimodal</td>
-      <td></td>
-      <td>real-world</td>
-      <td></td>
-      <td></td>
-      <td>>=1</td>
-      <td></td>
-      <td>coco-gbea</td>
-      <td></td>
-      <td>5s-35s</td>
-      <td><a href="https://github.com/ttusar/coco-gbea">https://github.com/ttusar/coco-gbea</a></td>
-      <td>Game-Benchmark for Evolutionary Algorithms (COCO fork)</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_gnbg">
-      <td>suite_gnbg</td>
-      <td>GNBG</td>
-      <td>Suite</td>
-      <td>continuous</td>
-      <td>>=1</td>
-      <td>1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>Generalized Numerical Benchmark Generator</td>
-      <td></td>
-      <td>GNBG, <a href="https://arxiv.org/abs/2312.07083">https://arxiv.org/abs/2312.07083</a></td>
-      <td>impl_gnbg</td>
-      <td></td>
-      <td></td>
-      <td>artificial</td>
-      <td></td>
-      <td></td>
-      <td>>=1</td>
-      <td></td>
-      <td>GNBG Generator</td>
-      <td></td>
-      <td></td>
-      <td><a href="https://github.com/Danial-Yazdani/GNBG-Generator">https://github.com/Danial-Yazdani/GNBG-Generator</a></td>
-      <td>Generalized Numerical Benchmark Generator</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_gnbg_ii">
-      <td>suite_gnbg_ii</td>
-      <td>GNBG-II</td>
-      <td>Suite</td>
-      <td>continuous</td>
-      <td>>=1</td>
-      <td>1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>Generalized Numerical Benchmark Generator (version 2). Also available in IOH.</td>
-      <td></td>
-      <td>GNBG-II, <a href="https://dl.acm.org/doi/pdf/10.1145/3712255.3734271">https://dl.acm.org/doi/pdf/10.1145/3712255.3734271</a></td>
-      <td>["impl_gnbg_ii", "impl_iohgnbg"]</td>
-      <td></td>
-      <td></td>
-      <td>artificial</td>
-      <td></td>
-      <td></td>
-      <td>>=1</td>
-      <td></td>
-      <td>GNBG-II | IOHGNBG</td>
-      <td></td>
-      <td></td>
-      <td><a href="https://github.com/rohitsalgotra/GNBG-II">https://github.com/rohitsalgotra/GNBG-II</a> | <a href="https://github.com/IOHprofiler/IOHGNBG">https://github.com/IOHprofiler/IOHGNBG</a></td>
-      <td>Generalized Numerical Benchmark Generator version 2 | IOHprofiler version of GNBG</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_iohclustering">
-      <td>suite_iohclustering</td>
-      <td>IOHClustering</td>
-      <td>Suite</td>
-      <td>continuous</td>
-      <td>>=1</td>
-      <td>1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>Set of benchmark problems from clustering: optimization task is selecting cluster centers for a given set of data.</td>
-      <td></td>
-      <td>IOHClustering, <a href="https://arxiv.org/pdf/2505.09233">https://arxiv.org/pdf/2505.09233</a></td>
-      <td>impl_iohclustering</td>
-      <td>multimodal</td>
-      <td></td>
-      <td>artificial-from-real-data</td>
-      <td></td>
-      <td></td>
-      <td>>=1</td>
-      <td></td>
-      <td>IOHClustering</td>
-      <td></td>
-      <td></td>
-      <td><a href="https://github.com/IOHprofiler/IOHClustering">https://github.com/IOHprofiler/IOHClustering</a></td>
-      <td>Clustering-based optimization benchmark built on ML datasets</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_kinematics_robotarm">
-      <td>suite_kinematics_robotarm</td>
-      <td>KinematicsRobotArm</td>
-      <td>Suite</td>
-      <td>continuous</td>
-      <td>21</td>
-      <td>1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>Kinematics of a robot arm, <a href="https://doi.org/10.1023/A:1013258808932">https://doi.org/10.1023/A:1013258808932</a></td>
-      <td>impl_transfer_rf_bbob_rw</td>
-      <td>unimodal</td>
-      <td></td>
-      <td>real-world</td>
-      <td></td>
-      <td></td>
-      <td>21</td>
-      <td></td>
-      <td>Transfer Random Forests BBOB Real-world</td>
-      <td></td>
-      <td></td>
-      <td><a href="https://github.com/ShuaiqunPan/Transfer_Random_forests_BBOB_Real_world">https://github.com/ShuaiqunPan/Transfer_Random_forests_BBOB_Real_world</a></td>
-      <td>Real-world BBOB-like problem implementations (Porkchop, KinematicsRobotArm)</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_l1_zdt">
-      <td>suite_l1_zdt</td>
-      <td>L1-ZDT</td>
-      <td>Suite</td>
-      <td>binary | continuous</td>
-      <td>>=2</td>
-      <td>2</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>Variant of ZDT with linkages between variables within groups</td>
-      <td></td>
-      <td>Linkage ZDT/DTLZ variants, <a href="https://doi.org/10.1145/1143997.1144179">https://doi.org/10.1145/1143997.1144179</a></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>>=1</td>
-      <td></td>
-      <td>>=1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_l2_dtlz">
-      <td>suite_l2_dtlz</td>
-      <td>L2-DTLZ</td>
-      <td>Suite</td>
-      <td>continuous</td>
-      <td>>=1</td>
-      <td>[10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>Variant of DTLZ2/DTLZ3 with linkages between all variables</td>
-      <td></td>
-      <td>Linkage ZDT/DTLZ variants, <a href="https://doi.org/10.1145/1143997.1144179">https://doi.org/10.1145/1143997.1144179</a></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>>=1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_l2_zdt">
-      <td>suite_l2_zdt</td>
-      <td>L2-ZDT</td>
-      <td>Suite</td>
-      <td>binary | continuous</td>
-      <td>>=2</td>
-      <td>2</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>Variant of ZDT with linkages between all variables</td>
-      <td></td>
-      <td>Linkage ZDT/DTLZ variants, <a href="https://doi.org/10.1145/1143997.1144179">https://doi.org/10.1145/1143997.1144179</a></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>>=1</td>
-      <td></td>
-      <td>>=1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_l3_dtlz">
-      <td>suite_l3_dtlz</td>
-      <td>L3-DTLZ</td>
-      <td>Suite</td>
-      <td>continuous</td>
-      <td>>=1</td>
-      <td>[10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>Variant of L2-DTLZ with anti-linkage mapping</td>
-      <td></td>
-      <td>Linkage ZDT/DTLZ variants, <a href="https://doi.org/10.1145/1143997.1144179">https://doi.org/10.1145/1143997.1144179</a></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>>=1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_l3_zdt">
-      <td>suite_l3_zdt</td>
-      <td>L3-ZDT</td>
-      <td>Suite</td>
-      <td>binary | continuous</td>
-      <td>>=2</td>
-      <td>2</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>Variant of L2-ZDT with anti-linkage mapping</td>
-      <td></td>
-      <td>Linkage ZDT/DTLZ variants, <a href="https://doi.org/10.1145/1143997.1144179">https://doi.org/10.1145/1143997.1144179</a></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>>=1</td>
-      <td></td>
-      <td>>=1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_maop">
-      <td>suite_maop</td>
-      <td>MaOP</td>
-      <td>Suite</td>
-      <td>continuous</td>
-      <td>>=1</td>
-      <td>[10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
-      <td>noisy</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>unknown</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>MaOP benchmark, <a href="https://doi.org/10.1016/j.swevo.2019.02.003">https://doi.org/10.1016/j.swevo.2019.02.003</a></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>>=1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_mechbench">
-      <td>suite_mechbench</td>
-      <td>MECHBench</td>
-      <td>Suite</td>
-      <td>continuous</td>
-      <td>>=1</td>
-      <td>1</td>
-      <td></td>
-      <td>unknown</td>
-      <td>{1, 2}</td>
-      <td></td>
-      <td></td>
-      <td>no</td>
-      <td></td>
-      <td></td>
-      <td>MECHBench</td>
-      <td>Set of problems inspired by Structural Mechanics Design Optimization. Embeds physical simulations (plasticity only, no fracture/damage). Unstructured/non-isotropic multimodality.</td>
-      <td></td>
-      <td>MECHBench, <a href="https://arxiv.org/abs/2511.10821">https://arxiv.org/abs/2511.10821</a></td>
-      <td>impl_mechbench</td>
-      <td>multimodal</td>
-      <td></td>
-      <td>real-world</td>
-      <td></td>
-      <td></td>
-      <td>>=1</td>
-      <td></td>
-      <td>MECHBench</td>
-      <td>Python</td>
-      <td>1-7 minutes</td>
-      <td><a href="https://github.com/BayesOptApp/MECHBench">https://github.com/BayesOptApp/MECHBench</a></td>
-      <td>Structural mechanics design optimization benchmark</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_mf2">
-      <td>suite_mf2</td>
-      <td>MF2</td>
-      <td>Suite</td>
-      <td>continuous</td>
-      <td>>=1</td>
-      <td>1</td>
-      <td>multi-fidelity</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>[1, 2]</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>mf2: a collection of multi-fidelity benchmark functions in Python, <a href="https://doi.org/10.21105/joss.02049">https://doi.org/10.21105/joss.02049</a></td>
-      <td>impl_mf2</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>>=1</td>
-      <td></td>
-      <td>mf2</td>
-      <td>Python</td>
-      <td></td>
-      <td><a href="https://github.com/sjvrijn/mf2">https://github.com/sjvrijn/mf2</a></td>
-      <td>Multi-fidelity test function collection</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_minus_dtlz">
-      <td>suite_minus_dtlz</td>
-      <td>Minus DTLZ</td>
-      <td>Suite</td>
-      <td>continuous</td>
-      <td>>=1</td>
-      <td>[10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>Variant of DTLZ that minimises the inverse of the base DTLZ functions</td>
-      <td></td>
-      <td>Minus DTLZ / Minus WFG, <a href="https://doi.org/10.1109/TEVC.2016.2587749">https://doi.org/10.1109/TEVC.2016.2587749</a></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>>=1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_minus_wfg">
-      <td>suite_minus_wfg</td>
-      <td>Minus WFG</td>
-      <td>Suite</td>
-      <td>continuous</td>
-      <td>>=1</td>
-      <td>[10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>Variant of WFG that minimises the inverse of the base WFG functions</td>
-      <td></td>
-      <td>Minus DTLZ / Minus WFG, <a href="https://doi.org/10.1109/TEVC.2016.2587749">https://doi.org/10.1109/TEVC.2016.2587749</a></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>>=1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_mmopp">
-      <td>suite_mmopp</td>
-      <td>MMOPP</td>
-      <td>Suite</td>
-      <td>unknown</td>
-      <td>0</td>
-      <td>[2, 3, 4, 5, 6, 7]</td>
-      <td></td>
-      <td>unknown</td>
-      <td>>=1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>MMOPP technical report, <a href="http://www5.zzu.edu.cn/system/_content/download.jsp?urltype=news.DownloadAttachUrl&owner=1327567121&wbfileid=4764412">http://www5.zzu.edu.cn/system/_content/download.jsp?urltype=news.DownloadAttachUrl&owner=1327567121&wbfileid=4764412</a></td>
-      <td>impl_mmopp</td>
-      <td>multimodal</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>MMOPP</td>
-      <td></td>
-      <td></td>
-      <td><a href="http://www5.zzu.edu.cn/ecilab/info/1036/1251.htm">http://www5.zzu.edu.cn/ecilab/info/1036/1251.htm</a></td>
-      <td>ECI lab distribution page for MMOPP</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_modact">
-      <td>suite_modact</td>
-      <td>MODAct</td>
-      <td>Suite</td>
-      <td>continuous | integer</td>
-      <td>40</td>
-      <td>[2, 3, 4, 5]</td>
-      <td></td>
-      <td>unknown</td>
-      <td>>=1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>multiobjective design of actuators</td>
-      <td>Realistic Constrained Multi-Objective Optimization Benchmark Problems from Design.</td>
-      <td></td>
-      <td>MODAct, <a href="https://doi.org/10.1109/TEVC.2020.3020046">https://doi.org/10.1109/TEVC.2020.3020046</a></td>
-      <td>["impl_modact", "impl_pymoo"]</td>
-      <td></td>
-      <td></td>
-      <td>real-world</td>
-      <td></td>
-      <td></td>
-      <td>20</td>
-      <td>20</td>
-      <td>modact | pymoo</td>
-      <td>Python</td>
-      <td>20ms</td>
-      <td><a href="https://github.com/epfl-lamd/modact">https://github.com/epfl-lamd/modact</a> | <a href="https://github.com/anyoptimization/pymoo">https://github.com/anyoptimization/pymoo</a></td>
-      <td>EPFL-LAMD modact package | Multi-objective optimization in Python</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_morepo">
-      <td>suite_morepo</td>
-      <td>MOrepo</td>
-      <td>Suite</td>
-      <td>unknown</td>
-      <td>0</td>
-      <td>2</td>
-      <td>dynamic | noisy</td>
-      <td>unknown</td>
-      <td>>=1</td>
-      <td>unknown</td>
-      <td>unknown</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>impl_morepo</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>MOrepo</td>
-      <td></td>
-      <td></td>
-      <td><a href="https://github.com/MCDMSociety/MOrepo">https://github.com/MCDMSociety/MOrepo</a></td>
-      <td>Multi-objective optimisation problem repository</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_pbo">
-      <td>suite_pbo</td>
-      <td>PBO</td>
-      <td>Suite</td>
-      <td>binary</td>
-      <td>>=1</td>
-      <td>1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>Suite of 25 binary optimization problems</td>
-      <td></td>
-      <td>PBO benchmarks, <a href="https://dl.acm.org/doi/pdf/10.1145/3319619.3326810">https://dl.acm.org/doi/pdf/10.1145/3319619.3326810</a></td>
-      <td>impl_iohexperimenter</td>
-      <td></td>
-      <td></td>
-      <td>artificial</td>
-      <td>>=1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>IOHexperimenter</td>
-      <td>C++/Python</td>
-      <td></td>
-      <td><a href="https://github.com/IOHprofiler/IOHexperimenter">https://github.com/IOHprofiler/IOHexperimenter</a></td>
-      <td>IOHprofiler experimenter framework</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_porkchop">
-      <td>suite_porkchop</td>
-      <td>PorkchopPlotInterplanetaryTrajectory</td>
-      <td>Suite</td>
-      <td>continuous</td>
-      <td>2</td>
-      <td>1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>Porkchop plot interplanetary trajectory benchmark, <a href="https://doi.org/10.1109/CEC65147.2025.11042973">https://doi.org/10.1109/CEC65147.2025.11042973</a></td>
-      <td>impl_transfer_rf_bbob_rw</td>
-      <td>multimodal</td>
-      <td></td>
-      <td>real-world</td>
-      <td></td>
-      <td></td>
-      <td>2</td>
-      <td></td>
-      <td>Transfer Random Forests BBOB Real-world</td>
-      <td></td>
-      <td></td>
-      <td><a href="https://github.com/ShuaiqunPan/Transfer_Random_forests_BBOB_Real_world">https://github.com/ShuaiqunPan/Transfer_Random_forests_BBOB_Real_world</a></td>
-      <td>Real-world BBOB-like problem implementations (Porkchop, KinematicsRobotArm)</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_re">
-      <td>suite_re</td>
-      <td>RE</td>
-      <td>Suite</td>
-      <td>continuous | integer</td>
-      <td>4-14</td>
-      <td>[2, 3, 4, 5, 6, 7, 8, 9]</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>Easy-to-evaluate real-world multi-objective optimization problems, Ryoji Tanabe; Hisao Ishibuchi, <a href="https://doi.org/10.1016/j.asoc.2020.106078">https://doi.org/10.1016/j.asoc.2020.106078</a></td>
-      <td>impl_reproblems</td>
-      <td></td>
-      <td></td>
-      <td>real-world-like</td>
-      <td></td>
-      <td></td>
-      <td>2-7</td>
-      <td>2-7</td>
-      <td>reproblems</td>
-      <td>Python</td>
-      <td></td>
-      <td><a href="https://github.com/ryojitanabe/reproblems">https://github.com/ryojitanabe/reproblems</a></td>
-      <td>Real-world inspired multi-objective optimization problem suite</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_rwmvop">
-      <td>suite_rwmvop</td>
-      <td>RWMVOP</td>
-      <td>Suite</td>
-      <td>integer | continuous | categorical</td>
-      <td>>=3</td>
-      <td>1</td>
-      <td></td>
-      <td>unknown</td>
-      <td>>=1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>RWMVOP, <a href="https://doi.org/10.1109/TEVC.2013.2281531">https://doi.org/10.1109/TEVC.2013.2281531</a></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>real-world</td>
-      <td></td>
-      <td>>=1</td>
-      <td>>=1</td>
-      <td>>=1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_sbox_cost">
-      <td>suite_sbox_cost</td>
-      <td>SBOX-COST</td>
-      <td>Suite</td>
-      <td>continuous</td>
-      <td>>=1</td>
-      <td>1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>problems from BBOB but allows instances with the optimum close to the boundary</td>
-      <td></td>
-      <td>SBOX-COST, <a href="https://doi.org/10.48550/arXiv.2305.12221">https://doi.org/10.48550/arXiv.2305.12221</a></td>
-      <td>impl_iohexperimenter</td>
-      <td>multimodal</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>>=1</td>
-      <td></td>
-      <td>IOHexperimenter</td>
-      <td>C++/Python</td>
-      <td></td>
-      <td><a href="https://github.com/IOHprofiler/IOHexperimenter">https://github.com/IOHprofiler/IOHexperimenter</a></td>
-      <td>IOHprofiler experimenter framework</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_sdp">
-      <td>suite_sdp</td>
-      <td>SDP</td>
-      <td>Suite</td>
-      <td>continuous</td>
-      <td>>=1</td>
-      <td>[10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
-      <td>dynamic | noisy</td>
-      <td></td>
-      <td></td>
-      <td>dynamic</td>
-      <td>unknown</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>SDP dynamic multi-objective benchmark, <a href="https://doi.org/10.1109/TCYB.2019.2896021">https://doi.org/10.1109/TCYB.2019.2896021</a></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>>=1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_submodular">
-      <td>suite_submodular</td>
-      <td>Submodular Optimization</td>
-      <td>Suite</td>
-      <td>binary</td>
-      <td>>=1</td>
-      <td>1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>set of graph-based submodular optimization problems from 4 problem types</td>
-      <td></td>
-      <td>Submodular optimization benchmark, <a href="https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10254181">https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10254181</a></td>
-      <td>impl_iohexperimenter</td>
-      <td></td>
-      <td></td>
-      <td>artificial</td>
-      <td>>=1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>IOHexperimenter</td>
-      <td>C++/Python</td>
-      <td></td>
-      <td><a href="https://github.com/IOHprofiler/IOHexperimenter">https://github.com/IOHprofiler/IOHexperimenter</a></td>
-      <td>IOHprofiler experimenter framework</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_tulipa_energy">
-      <td>suite_tulipa_energy</td>
-      <td>TulipaEnergy</td>
-      <td>Suite</td>
-      <td>continuous</td>
-      <td>>=1</td>
-      <td>1</td>
-      <td>noisy | multi-fidelity</td>
-      <td>unknown</td>
-      <td>>=2</td>
-      <td></td>
-      <td>parameter</td>
-      <td></td>
-      <td></td>
-      <td>[1, 2]</td>
-      <td>TulipaEnergyModel.jl</td>
-      <td>Determine the optimal investment and operation decisions for different assets in the energy system (production, consumption, conversion, storage, transport) while minimizing loss of load. Modelled as a potentially very large linear program with multiple fidelity levels.</td>
-      <td></td>
-      <td>TulipaEnergyModel.jl scientific references, <a href="https://tulipaenergy.github.io/TulipaEnergyModel.jl/stable/40-scientific-foundation/45-scientific-references">https://tulipaenergy.github.io/TulipaEnergyModel.jl/stable/40-scientific-foundation/45-scientific-references</a></td>
-      <td>impl_tulipa</td>
-      <td>unimodal</td>
-      <td></td>
-      <td>real-world</td>
-      <td></td>
-      <td></td>
-      <td>>=1</td>
-      <td></td>
-      <td>TulipaEnergyModel.jl</td>
-      <td>Julia / JuMP</td>
-      <td>minutes to hours</td>
-      <td><a href="https://tulipaenergy.github.io/TulipaEnergyModel.jl/stable/">https://tulipaenergy.github.io/TulipaEnergyModel.jl/stable/</a> | <a href="https://github.com/TulipaEnergy/Tulipa-OBZ-CaseStudy">https://github.com/TulipaEnergy/Tulipa-OBZ-CaseStudy</a></td>
-      <td>Large linear program for optimal investment and operation of energy systems</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_vehicle_dynamics">
-      <td>suite_vehicle_dynamics</td>
-      <td>VehicleDynamics</td>
-      <td>Suite</td>
-      <td>continuous</td>
-      <td>2</td>
-      <td>1</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>VehicleDynamics benchmark, <a href="https://www.scitepress.org/Papers/2023/121580/121580.pdf">https://www.scitepress.org/Papers/2023/121580/121580.pdf</a></td>
-      <td>impl_vehicle_dynamics</td>
-      <td>multimodal</td>
-      <td></td>
-      <td>real-world</td>
-      <td></td>
-      <td></td>
-      <td>2</td>
-      <td></td>
-      <td>VehicleDynamics (Zenodo)</td>
-      <td></td>
-      <td></td>
-      <td><a href="https://zenodo.org/records/8307853">https://zenodo.org/records/8307853</a></td>
-      <td>Zenodo archive for the vehicle dynamics benchmark</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_wfg">
-      <td>suite_wfg</td>
-      <td>WFG</td>
-      <td>Suite</td>
-      <td>continuous</td>
-      <td>>=1</td>
-      <td>[10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>A review of multiobjective test problems and a scalable test problem toolkit, Simon Huband; Philip Hingston; Luigi Barone; Lyndon While, <a href="https://doi.org/10.1109/TEVC.2005.861417">https://doi.org/10.1109/TEVC.2005.861417</a></td>
-      <td>impl_pymoo</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>>=1</td>
-      <td></td>
-      <td>pymoo</td>
-      <td>Python</td>
-      <td></td>
-      <td><a href="https://github.com/anyoptimization/pymoo">https://github.com/anyoptimization/pymoo</a></td>
-      <td>Multi-objective optimization in Python</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-    <tr data-problem-id="suite_zdt">
-      <td>suite_zdt</td>
-      <td>ZDT</td>
-      <td>Suite</td>
-      <td>binary | continuous</td>
-      <td>>=2</td>
-      <td>2</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>Comparison of multiobjective evolutionary algorithms: empirical results, Eckart Zitzler; Kalyanmoy Deb; Lothar Thiele, <a href="https://doi.org/10.1162/106365600568202">https://doi.org/10.1162/106365600568202</a></td>
-      <td>impl_pymoo</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td>>=1</td>
-      <td></td>
-      <td>>=1</td>
-      <td></td>
-      <td>pymoo</td>
-      <td>Python</td>
-      <td></td>
-      <td><a href="https://github.com/anyoptimization/pymoo">https://github.com/anyoptimization/pymoo</a></td>
-      <td>Multi-objective optimization in Python</td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-      <td></td>
-    </tr>
-  </tbody>
-<tfoot><tr><th>ID</th> <th>Name</th> <th>Type</th> <th>Variable Types</th> <th>Total Variables</th> <th>Objectives</th> <th>Properties</th> <th>Constraint Types</th> <th>Total Constraints</th> <th>Dynamics</th> <th>Noise</th> <th>Partial Evaluations</th> <th>Independent Objectives</th> <th>Fidelity Levels</th> <th>Full Name</th> <th>Description</th> <th>Tags</th> <th>References</th> <th>Implementations</th> <th>Modality</th> <th>Examples</th> <th>Source</th> <th>Binary Vars</th> <th>Categorical Vars</th> <th>Continuous Vars</th> <th>Integer Vars</th> <th>Implementation Names</th> <th>Implementation Languages</th> <th>Implementation Evaluation Times</th> <th>Implementation Links</th> <th>Implementation Descriptions</th> <th>Implementation Requirements</th> <th>Hard Box Constraints</th> <th>Soft Box Constraints</th> <th>Hard Linear Constraints</th> <th>Soft Linear Constraints</th> <th>Hard Function Constraints</th> <th>Soft Function Constraints</th></tr></tfoot></table>
-    </div>
-</section>
-
-<section class="details-shell" id="problem-details" aria-live="polite">
-    <h3 class="details-title">Problem details</h3>
-    <p class="details-hint" id="problem-details-hint">Click a table row to inspect full details.</p>
-    <dl class="details-grid" id="problem-details-content"></dl>
-    <div class="snippet-shell" id="problem-snippet-shell">
-        <h4 class="snippet-title">README.md</h4>
-        <p class="snippet-hint" id="problem-readme-hint">README content for this snippet folder will appear here.</p>
-        <div class="snippet-readme markdown-body" id="problem-readme-content"></div>
-        <h4 class="snippet-title">Snippet: <code id="problem-snippet-path">call_{problem_id}.py</code></h4>
-        <p class="snippet-meta"><a id="problem-snippet-folder-link" href="#" target="_blank" rel="noopener noreferrer">Open snippet folder</a></p>
-        <p class="snippet-hint" id="problem-snippet-hint">Select a problem to check for an available code snippet.</p>
-        <pre class="snippet-code"><code id="problem-snippet-content"></code></pre>
-    </div>
-</section>
+<section class="table-shell">
+    <div class="table-toolbar">
+        <details class="column-toolbar-disclosure">
+            <summary class="toolbar-title">Toggle Visible Columns</summary>
+            <div class="toolbar-actions">
+                <button type="button" class="toolbar-btn" id="show-all-columns">Show all</button>
+                <button type="button" class="toolbar-btn" id="hide-all-columns">Hide all</button>
+            </div>
+            <div class="column-controls">
+                <label class="column-chip"><input class="col-toggle" type="checkbox" data-column="0"><span>ID</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="1" checked><span>Name</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="2" checked><span>Type</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="3" checked><span>Variable Types</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="4" checked><span>Total Variables</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="5" checked><span>Objectives</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="6" checked><span>Properties</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="7" checked><span>Constraint Types</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="8" checked><span>Total Constraints</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="9" checked><span>Dynamics</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="10" checked><span>Noise</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="11" checked><span>Partial Evaluations</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="12" checked><span>Independent Objectives</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="13" checked><span>Fidelity Levels</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="14"><span>Full Name</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="15"><span>Description</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="16"><span>Tags</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="17"><span>References</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="18"><span>Implementations</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="19"><span>Modality</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="20"><span>Examples</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="21"><span>Source</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="22"><span>Binary Vars</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="23"><span>Categorical Vars</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="24"><span>Continuous Vars</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="25"><span>Integer Vars</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="26"><span>Implementation Names</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="27"><span>Implementation Languages</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="28"><span>Implementation Evaluation Times</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="29"><span>Implementation Links</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="30"><span>Implementation Descriptions</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="31"><span>Implementation Requirements</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="32"><span>Hard Box Constraints</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="33"><span>Soft Box Constraints</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="34"><span>Hard Linear Constraints</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="35"><span>Soft Linear Constraints</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="36"><span>Hard Function Constraints</span></label><label class="column-chip"><input class="col-toggle" type="checkbox" data-column="37"><span>Soft Function Constraints</span></label>
+            </div>
+        </details>
+    </div>
+    <div class="table-wrap">
+        <table class="dataframe display compact display styled-table" id="problems">
+  <thead>
+    <tr style="text-align: right;">
+      <th>ID</th>
+      <th>Name</th>
+      <th>Type</th>
+      <th>Variable Types</th>
+      <th>Total Variables</th>
+      <th>Objectives</th>
+      <th>Properties</th>
+      <th>Constraint Types</th>
+      <th>Total Constraints</th>
+      <th>Dynamics</th>
+      <th>Noise</th>
+      <th>Partial Evaluations</th>
+      <th>Independent Objectives</th>
+      <th>Fidelity Levels</th>
+      <th>Full Name</th>
+      <th>Description</th>
+      <th>Tags</th>
+      <th>References</th>
+      <th>Implementations</th>
+      <th>Modality</th>
+      <th>Examples</th>
+      <th>Source</th>
+      <th>Binary Vars</th>
+      <th>Categorical Vars</th>
+      <th>Continuous Vars</th>
+      <th>Integer Vars</th>
+      <th>Implementation Names</th>
+      <th>Implementation Languages</th>
+      <th>Implementation Evaluation Times</th>
+      <th>Implementation Links</th>
+      <th>Implementation Descriptions</th>
+      <th>Implementation Requirements</th>
+      <th>Hard Box Constraints</th>
+      <th>Soft Box Constraints</th>
+      <th>Hard Linear Constraints</th>
+      <th>Soft Linear Constraints</th>
+      <th>Hard Function Constraints</th>
+      <th>Soft Function Constraints</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr data-problem-id="fn_ato">
+      <td>fn_ato</td>
+      <td>ATO</td>
+      <td>Problem</td>
+      <td>continuous</td>
+      <td>10</td>
+      <td>2</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>no</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Parameters of the Modules of the Automatic Train Operation are optimized; two objectives: minimizing energy consumption and minimizing driving duration.</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>unimodal</td>
+      <td></td>
+      <td>real-world</td>
+      <td></td>
+      <td></td>
+      <td>10</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="fn_building_spatial">
+      <td>fn_building_spatial</td>
+      <td>Building spatial design</td>
+      <td>Problem</td>
+      <td>continuous | binary</td>
+      <td>>=2</td>
+      <td>2</td>
+      <td></td>
+      <td>box | unknown</td>
+      <td>>=2</td>
+      <td></td>
+      <td></td>
+      <td>no</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Optimise the spatial layout of a building to minimise energy consumption for climate control and minimise the strain on the structure. Many hard constraints; mixed-variable (continuous+binary); expensive evaluations.</td>
+      <td></td>
+      <td>Building spatial design, <a href="https://hdl.handle.net/1887/81789">https://hdl.handle.net/1887/81789</a></td>
+      <td>impl_bso_toolbox</td>
+      <td></td>
+      <td></td>
+      <td>real-world</td>
+      <td>>=1</td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>BSO-toolbox</td>
+      <td>C++</td>
+      <td>~1s (smallest) to ~40s (larger)</td>
+      <td><a href="https://github.com/TUe-excellent-buildings/BSO-toolbox">https://github.com/TUe-excellent-buildings/BSO-toolbox</a></td>
+      <td>Building Spatial Design toolbox (TU/e)</td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="fn_convex_dtlz2">
+      <td>fn_convex_dtlz2</td>
+      <td>Convex DTLZ2</td>
+      <td>Problem</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>[10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Variant of DTLZ2 with a convex Pareto front (instead of concave)</td>
+      <td></td>
+      <td>Convex DTLZ2, <a href="https://doi.org/10.1109/TEVC.2013.2281535">https://doi.org/10.1109/TEVC.2013.2281535</a></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="fn_emdo">
+      <td>fn_emdo</td>
+      <td>Electric Motor Design Optimization</td>
+      <td>Problem</td>
+      <td>continuous | integer</td>
+      <td>26</td>
+      <td>1</td>
+      <td>noisy</td>
+      <td>unknown | box</td>
+      <td>>=14</td>
+      <td></td>
+      <td>noisy</td>
+      <td>no</td>
+      <td></td>
+      <td></td>
+      <td>Electric Motor Design Optimization</td>
+      <td># Goal\nFind a design of a synchronous electric motor for power steering systems that minimizes costs and satisfies all constraints.\n\n# Motivation\nChallenging to find good solutions in a limited time.\n\n# Key Challenges\n* Time-consuming solution evaluation\n* Highly-constrained problem\n* Constraints are multimodal\n\nThis is not an available problem, but could be interesting to show to researchers which difficulties appear in real-world problems.</td>
+      <td></td>
+      <td>A Multi-Step Evaluation Process in Electric Motor Design, Tea Tušar; Peter Korošec; Bogdan Filipič, <a href="https://dis.ijs.si/tea/Publications/Tusar23Multistep.pdf">https://dis.ijs.si/tea/Publications/Tusar23Multistep.pdf</a></td>
+      <td>impl_emdo</td>
+      <td>multimodal</td>
+      <td></td>
+      <td>real-world</td>
+      <td></td>
+      <td></td>
+      <td>13</td>
+      <td>13</td>
+      <td>Electric Motor Design Optimization</td>
+      <td>Python</td>
+      <td>8 minutes</td>
+      <td></td>
+      <td>Not publicly available</td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="fn_fleetopt">
+      <td>fn_fleetopt</td>
+      <td>FleetOpt</td>
+      <td>Problem</td>
+      <td>integer</td>
+      <td>{54, 13208}</td>
+      <td>1</td>
+      <td></td>
+      <td>unknown</td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td>yes</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>UK healthcare organisation fleet optimisation: reduce the fleet of non-emergency healthcare trip vehicles while still ensuring all trips can be covered. Bilevel: upper level 54 vars, lower level 13208 vars.</td>
+      <td></td>
+      <td>FleetOpt, <a href="https://dl.acm.org/doi/abs/10.1145/3638530.3664137">https://dl.acm.org/doi/abs/10.1145/3638530.3664137</a></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>real-world</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>{54, 13208}</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="fn_gasoline">
+      <td>fn_gasoline</td>
+      <td>Gasoline direct injection engine design</td>
+      <td>Problem</td>
+      <td>integer | continuous</td>
+      <td>14</td>
+      <td>2</td>
+      <td>multi-fidelity</td>
+      <td>unknown</td>
+      <td>5</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>[1, 2]</td>
+      <td></td>
+      <td>Multi-objective optimization to minimize fuel consumption and NOx emissions over a two-minute dynamic duty cycle, subject to five constraints (turbine inlet temperature, knock occurrences, peak cylinder pressure, peak cylinder pressure rise, total work). Seven decision variables cover hardware choices and engine control parameters.</td>
+      <td></td>
+      <td>Gasoline direct injection engine design, <a href="https://doi.org/10.1016/j.ejor.2022.08.032">https://doi.org/10.1016/j.ejor.2022.08.032</a></td>
+      <td>impl_gasoline</td>
+      <td></td>
+      <td></td>
+      <td>real-world</td>
+      <td></td>
+      <td></td>
+      <td>7</td>
+      <td>7</td>
+      <td>Gasoline direct injection engine design</td>
+      <td>Matlab Simulink / Wave RT</td>
+      <td></td>
+      <td><a href="https://doi.org/10.1016/j.ejor.2022.08.032">https://doi.org/10.1016/j.ejor.2022.08.032</a></td>
+      <td>Proprietary Matlab Simulink + Wave RT co-simulation</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="fn_invdeceptive_deceptive_rotell">
+      <td>fn_invdeceptive_deceptive_rotell</td>
+      <td>InverseDeceptiveTrap+RotatedEllipsoid / DeceptiveTrap+RotatedEllipsoid</td>
+      <td>Problem</td>
+      <td>continuous | binary</td>
+      <td>>=2</td>
+      <td>2</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Mixed-variable multi-objective test problems, <a href="https://doi.org/10.1145/3449726.3459521">https://doi.org/10.1145/3449726.3459521</a></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>artificial</td>
+      <td>>=1</td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="fn_inverted_dtlz1">
+      <td>fn_inverted_dtlz1</td>
+      <td>Inverted DTLZ1</td>
+      <td>Problem</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>[10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Variant of DTLZ1 with an inverted Pareto front</td>
+      <td></td>
+      <td>Inverted DTLZ1, <a href="https://doi.org/10.1109/TEVC.2013.2281534">https://doi.org/10.1109/TEVC.2013.2281534</a></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="fn_jsec2019">
+      <td>fn_jsec2019</td>
+      <td>JSEC2019</td>
+      <td>Problem</td>
+      <td>continuous</td>
+      <td>32</td>
+      <td>[1, 2, 3, 4, 5]</td>
+      <td></td>
+      <td>unknown</td>
+      <td>22</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>expensive evaluations 3s; 22 constraints</td>
+      <td></td>
+      <td>JPNSEC EC-Symposium 2019 competition, <a href="http://www.jpnsec.org/files/competition2019/EC-Symposium-2019-Competition-English.html">http://www.jpnsec.org/files/competition2019/EC-Symposium-2019-Competition-English.html</a></td>
+      <td>impl_jsec2019</td>
+      <td></td>
+      <td></td>
+      <td>real-world</td>
+      <td></td>
+      <td></td>
+      <td>32</td>
+      <td></td>
+      <td>JSEC 2019 competition</td>
+      <td></td>
+      <td>3s</td>
+      <td><a href="http://www.jpnsec.org/files/competition2019/EC-Symposium-2019-Competition-English.html">http://www.jpnsec.org/files/competition2019/EC-Symposium-2019-Competition-English.html</a></td>
+      <td>JPNSEC EC-Symposium 2019 competition problem</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="fn_onemax_sphere_deceptive_rotell">
+      <td>fn_onemax_sphere_deceptive_rotell</td>
+      <td>Onemax+Sphere / DeceptiveTrap+RotatedEllipsoid</td>
+      <td>Problem</td>
+      <td>continuous | binary</td>
+      <td>>=2</td>
+      <td>2</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Mixed-variable multi-objective test problems, <a href="https://doi.org/10.1145/3449726.3459521">https://doi.org/10.1145/3449726.3459521</a></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>artificial</td>
+      <td>>=1</td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="fn_onemax_sphere_zeromax_sphere">
+      <td>fn_onemax_sphere_zeromax_sphere</td>
+      <td>Onemax+Sphere / Zeromax+Sphere</td>
+      <td>Problem</td>
+      <td>continuous | binary</td>
+      <td>>=2</td>
+      <td>2</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Onemax+Sphere / Zeromax+Sphere, <a href="https://doi.org/10.1145/3449726.3459521">https://doi.org/10.1145/3449726.3459521</a></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>artificial</td>
+      <td>>=1</td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="fn_radar_waveform">
+      <td>fn_radar_waveform</td>
+      <td>Radar waveform</td>
+      <td>Problem</td>
+      <td>integer</td>
+      <td>4-12</td>
+      <td>9</td>
+      <td></td>
+      <td>unknown</td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Radar waveform design, <a href="https://doi.org/10.1007/978-3-540-70928-2_53">https://doi.org/10.1007/978-3-540-70928-2_53</a></td>
+      <td>impl_radar_waveform</td>
+      <td></td>
+      <td></td>
+      <td>real-world</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>4-12</td>
+      <td>Evan Hughes radar waveform code</td>
+      <td></td>
+      <td></td>
+      <td><a href="http://code.evanhughes.org/">http://code.evanhughes.org/</a></td>
+      <td>Radar waveform design reference implementation</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="gen_beacon">
+      <td>gen_beacon</td>
+      <td>BEACON</td>
+      <td>Generator</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>2</td>
+      <td></td>
+      <td>box</td>
+      <td>0</td>
+      <td></td>
+      <td></td>
+      <td>no</td>
+      <td></td>
+      <td></td>
+      <td>Continuous Bi-objective Benchmark problems with Explicit Adjustable COrrelatioN control</td>
+      <td>Generator for bi-objective benchmark problems with explicitly controlled correlations in continuous spaces. Multimodal with random structure.</td>
+      <td></td>
+      <td>BEACON, <a href="https://dl.acm.org/doi/10.1145/3712255.3734303">https://dl.acm.org/doi/10.1145/3712255.3734303</a></td>
+      <td>impl_beacon</td>
+      <td>multimodal</td>
+      <td></td>
+      <td>artificial</td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>BEACON</td>
+      <td>Python</td>
+      <td>negligible</td>
+      <td><a href="https://github.com/Stebbet/BEACON/">https://github.com/Stebbet/BEACON/</a></td>
+      <td>Continuous Bi-objective Benchmark with Explicit Adjustable COrrelatioN control</td>
+      <td></td>
+      <td>0</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="gen_bono_bench">
+      <td>gen_bono_bench</td>
+      <td>BONO-Bench</td>
+      <td>Generator</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>2</td>
+      <td></td>
+      <td>box</td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td>no</td>
+      <td></td>
+      <td></td>
+      <td>Bi-objective Numerical Optimization Benchmark</td>
+      <td>Bi-objective problem generator and suite with scalable continuous decision space. Features complex problem properties and Pareto front approximations with error guarantees for the hypervolume and exact R2 indicators.</td>
+      <td></td>
+      <td></td>
+      <td>impl_bonobench</td>
+      <td>multimodal</td>
+      <td></td>
+      <td>artificial</td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>BONO-Bench</td>
+      <td>Python</td>
+      <td></td>
+      <td><a href="https://github.com/schaepermeier/bonobench">https://github.com/schaepermeier/bonobench</a></td>
+      <td>Bi-objective Numerical Optimization Benchmark (BONO-Bench)</td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="gen_ealain">
+      <td>gen_ealain</td>
+      <td>Ealain</td>
+      <td>Generator</td>
+      <td>continuous | integer | binary</td>
+      <td>>=3</td>
+      <td>[1, 10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
+      <td>dynamic | multi-fidelity</td>
+      <td>unknown</td>
+      <td>>=1</td>
+      <td>optional</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>[1, 2]</td>
+      <td></td>
+      <td>Real-world-like, easily extensible to increase complexity</td>
+      <td></td>
+      <td>Ealain, <a href="https://doi.org/10.1145/3638530.3654299">https://doi.org/10.1145/3638530.3654299</a></td>
+      <td>impl_ealain</td>
+      <td></td>
+      <td></td>
+      <td>real-world-like</td>
+      <td>>=1</td>
+      <td></td>
+      <td>>=1</td>
+      <td>>=1</td>
+      <td>Ealain</td>
+      <td></td>
+      <td></td>
+      <td><a href="https://github.com/qrenau/Ealain">https://github.com/qrenau/Ealain</a></td>
+      <td>Real-world-like extensible benchmark problem generator</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="gen_gnbg">
+      <td>gen_gnbg</td>
+      <td>GNBG</td>
+      <td>Generator</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Generator counterpart of GNBG.</td>
+      <td></td>
+      <td>GNBG, <a href="https://arxiv.org/abs/2312.07083">https://arxiv.org/abs/2312.07083</a></td>
+      <td>impl_gnbg</td>
+      <td></td>
+      <td></td>
+      <td>artificial</td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>GNBG Generator</td>
+      <td></td>
+      <td></td>
+      <td><a href="https://github.com/Danial-Yazdani/GNBG-Generator">https://github.com/Danial-Yazdani/GNBG-Generator</a></td>
+      <td>Generalized Numerical Benchmark Generator</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="gen_gnbg_ii">
+      <td>gen_gnbg_ii</td>
+      <td>GNBG-II</td>
+      <td>Generator</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Generator counterpart of GNBG-II.</td>
+      <td></td>
+      <td>GNBG-II, <a href="https://dl.acm.org/doi/pdf/10.1145/3712255.3734271">https://dl.acm.org/doi/pdf/10.1145/3712255.3734271</a></td>
+      <td>["impl_gnbg_ii", "impl_iohgnbg"]</td>
+      <td></td>
+      <td></td>
+      <td>artificial</td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>IOHGNBG | GNBG-II</td>
+      <td></td>
+      <td></td>
+      <td><a href="https://github.com/IOHprofiler/IOHGNBG">https://github.com/IOHprofiler/IOHGNBG</a> | <a href="https://github.com/rohitsalgotra/GNBG-II">https://github.com/rohitsalgotra/GNBG-II</a></td>
+      <td>IOHprofiler version of GNBG | Generalized Numerical Benchmark Generator version 2</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="gen_gpd">
+      <td>gen_gpd</td>
+      <td>GPD</td>
+      <td>Generator</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>[10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
+      <td>noisy</td>
+      <td>unknown</td>
+      <td>>=1</td>
+      <td></td>
+      <td>optional</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>GPD generator, <a href="https://doi.org/10.1016/j.asoc.2020.106139">https://doi.org/10.1016/j.asoc.2020.106139</a></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="gen_iohclustering">
+      <td>gen_iohclustering</td>
+      <td>IOHClustering</td>
+      <td>Generator</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Generator counterpart of the IOHClustering suite.</td>
+      <td></td>
+      <td>IOHClustering, <a href="https://arxiv.org/pdf/2505.09233">https://arxiv.org/pdf/2505.09233</a></td>
+      <td>impl_iohclustering</td>
+      <td>multimodal</td>
+      <td></td>
+      <td>artificial-from-real-data</td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>IOHClustering</td>
+      <td></td>
+      <td></td>
+      <td><a href="https://github.com/IOHprofiler/IOHClustering">https://github.com/IOHprofiler/IOHClustering</a></td>
+      <td>Clustering-based optimization benchmark built on ML datasets</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="gen_ma_bbob">
+      <td>gen_ma_bbob</td>
+      <td>MA-BBOB</td>
+      <td>Generator</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Generator that creates affine combinations of BBOB functions</td>
+      <td></td>
+      <td>MA-BBOB, <a href="https://doi.org/10.1145/3673908">https://doi.org/10.1145/3673908</a></td>
+      <td>["impl_iohexperimenter", "impl_ma_bbob"]</td>
+      <td>multimodal</td>
+      <td></td>
+      <td>artificial</td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>MA-BBOB (IOHexperimenter) | IOHexperimenter</td>
+      <td>C++/Python</td>
+      <td></td>
+      <td><a href="https://github.com/IOHprofiler/IOHexperimenter/blob/master/example/Competitions/MA-BBOB/Example_MABBOB.ipynb">https://github.com/IOHprofiler/IOHexperimenter/blob/master/example/Competitions/MA-BBOB/Example_MABBOB.ipynb</a> | <a href="https://github.com/IOHprofiler/IOHexperimenter">https://github.com/IOHprofiler/IOHexperimenter</a></td>
+      <td>Example notebook for MA-BBOB in IOHexperimenter | IOHprofiler experimenter framework</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="gen_mpm2">
+      <td>gen_mpm2</td>
+      <td>MPM2</td>
+      <td>Generator</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>nonlinear nonseparable nonsymmetric; scalable in terms of time to evaluate the objective function</td>
+      <td></td>
+      <td>MPM2 technical report TR15-01, <a href="https://ls11-www.cs.tu-dortmund.de/_media/techreports/tr15-01.pdf">https://ls11-www.cs.tu-dortmund.de/_media/techreports/tr15-01.pdf</a></td>
+      <td>impl_mpm2</td>
+      <td>multimodal</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>MPM2 (smoof)</td>
+      <td>Python</td>
+      <td></td>
+      <td><a href="https://github.com/jakobbossek/smoof/blob/master/inst/mpm2.py">https://github.com/jakobbossek/smoof/blob/master/inst/mpm2.py</a></td>
+      <td>Python implementation of MPM2 distributed with smoof</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="gen_mubqp">
+      <td>gen_mubqp</td>
+      <td>mUBQP</td>
+      <td>Generator</td>
+      <td>binary</td>
+      <td>>=1</td>
+      <td>[1, 10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>tunable variable and objective dimensions; tunable density and correlation between objectives</td>
+      <td></td>
+      <td>mUBQP benchmark, <a href="https://doi.org/10.1016/j.asoc.2013.11.008">https://doi.org/10.1016/j.asoc.2013.11.008</a></td>
+      <td>impl_mocobench</td>
+      <td>["multimodal", "quadratic"]</td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>mocobench</td>
+      <td>C++</td>
+      <td></td>
+      <td><a href="https://gitlab.com/aliefooghe/mocobench/">https://gitlab.com/aliefooghe/mocobench/</a></td>
+      <td>Multi-objective combinatorial optimization benchmark</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="gen_puboi">
+      <td>gen_puboi</td>
+      <td>PUBOi</td>
+      <td>Generator</td>
+      <td>binary</td>
+      <td>>=1</td>
+      <td>1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>no</td>
+      <td></td>
+      <td></td>
+      <td>Polynomial Unconstrained Binary Optimization with tunable importance</td>
+      <td>A benchmark in which variable importance is tunable, based on the Walsh function.</td>
+      <td></td>
+      <td>PUBOi, <a href="https://link.springer.com/chapter/10.1007/978-3-031-04148-8_12">https://link.springer.com/chapter/10.1007/978-3-031-04148-8_12</a></td>
+      <td>impl_puboi</td>
+      <td></td>
+      <td></td>
+      <td>artificial</td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>PUBO Importance Benchmark</td>
+      <td>Python / C++</td>
+      <td></td>
+      <td><a href="https://gitlab.com/verel/pubo-importance-benchmark">https://gitlab.com/verel/pubo-importance-benchmark</a></td>
+      <td>A benchmark in which variable importance is tunable, based on the Walsh function</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="gen_randoptgen">
+      <td>gen_randoptgen</td>
+      <td>RandOptGen</td>
+      <td>Generator</td>
+      <td>continuous | integer | binary</td>
+      <td>>=3</td>
+      <td>[1, 10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>no</td>
+      <td></td>
+      <td></td>
+      <td>RandOptGen</td>
+      <td>A Unified Random Problem Generator for Single- and Multi-Objective Optimization Problems with Mixed-Variable Input Spaces.</td>
+      <td></td>
+      <td></td>
+      <td>impl_randoptgen</td>
+      <td>multimodal</td>
+      <td></td>
+      <td>artificial</td>
+      <td>>=1</td>
+      <td></td>
+      <td>>=1</td>
+      <td>>=1</td>
+      <td>RandOptGen</td>
+      <td>Python</td>
+      <td>milliseconds</td>
+      <td><a href="https://github.com/MALEO-research-group/RandOptGen">https://github.com/MALEO-research-group/RandOptGen</a> | <a href="https://doi.org/10.1145/3712256.3726478">https://doi.org/10.1145/3712256.3726478</a></td>
+      <td>Unified Random Problem Generator for Single- and Multi-Objective Optimization with Mixed-Variable Input Spaces</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="gen_rho_mnk_landscapes">
+      <td>gen_rho_mnk_landscapes</td>
+      <td>ρMNK-Landscapes</td>
+      <td>Generator</td>
+      <td>binary</td>
+      <td>>=1</td>
+      <td>[1, 10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>tunable variable and objective dimensions; tunable multimodality and correlation between objectives</td>
+      <td></td>
+      <td>On the design of multi-objective evolutionary algorithms based on NK-landscapes, <a href="https://doi.org/10.1016/j.ejor.2012.12.019">https://doi.org/10.1016/j.ejor.2012.12.019</a></td>
+      <td>impl_mocobench</td>
+      <td>multimodal</td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>mocobench</td>
+      <td>C++</td>
+      <td></td>
+      <td><a href="https://gitlab.com/aliefooghe/mocobench/">https://gitlab.com/aliefooghe/mocobench/</a></td>
+      <td>Multi-objective combinatorial optimization benchmark</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="gen_rho_mtsp">
+      <td>gen_rho_mtsp</td>
+      <td>ρmTSP</td>
+      <td>Generator</td>
+      <td>unknown</td>
+      <td>>=1</td>
+      <td>[1, 10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>tunable variable and objective dimensions; tunable instance type (euclidean/random); tunable correlation between objectives</td>
+      <td></td>
+      <td>On the impact of multi-objective scalability for the ρmTSP, <a href="https://doi.org/10.1007/978-3-319-45823-6_40">https://doi.org/10.1007/978-3-319-45823-6_40</a></td>
+      <td>impl_mocobench</td>
+      <td>["multimodal", "quadratic"]</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>mocobench</td>
+      <td>C++</td>
+      <td></td>
+      <td><a href="https://gitlab.com/aliefooghe/mocobench/">https://gitlab.com/aliefooghe/mocobench/</a></td>
+      <td>Multi-objective combinatorial optimization benchmark</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="gen_wmodel">
+      <td>gen_wmodel</td>
+      <td>W-model</td>
+      <td>Generator</td>
+      <td>binary</td>
+      <td>>=1</td>
+      <td>1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Tunable generator for binary optimization based on several difficulty features</td>
+      <td></td>
+      <td>W-model, <a href="https://dl.acm.org/doi/abs/10.1145/3205651.3208240">https://dl.acm.org/doi/abs/10.1145/3205651.3208240</a></td>
+      <td>impl_wmodel</td>
+      <td></td>
+      <td></td>
+      <td>artificial</td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>BBDOB W-Model</td>
+      <td></td>
+      <td></td>
+      <td><a href="https://github.com/thomasWeise/BBDOB_W_Model">https://github.com/thomasWeise/BBDOB_W_Model</a></td>
+      <td>Tunable generator for binary optimization</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_amvop">
+      <td>suite_amvop</td>
+      <td>AMVOP</td>
+      <td>Suite</td>
+      <td>continuous | integer | categorical</td>
+      <td>>=3</td>
+      <td>1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>AMVOP, <a href="https://doi.org/10.1109/TEVC.2013.2281531">https://doi.org/10.1109/TEVC.2013.2281531</a></td>
+      <td></td>
+      <td>multimodal</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td>>=1</td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_bbob">
+      <td>suite_bbob</td>
+      <td>BBOB</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>COCO: a platform for comparing continuous optimizers in a black-box setting, <a href="https://doi.org/10.1080/10556788.2020.1808977">https://doi.org/10.1080/10556788.2020.1808977</a></td>
+      <td>impl_coco</td>
+      <td>multimodal</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>COCO framework</td>
+      <td>C/Python</td>
+      <td></td>
+      <td><a href="https://github.com/numbbo/coco">https://github.com/numbbo/coco</a></td>
+      <td>Comparing Continuous Optimizers: black-box optimization benchmarking platform</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_bbob_biobj">
+      <td>suite_bbob_biobj</td>
+      <td>BBOB-biobj</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>2-40</td>
+      <td>2</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>BBOB bi-objective test suite, <a href="https://doi.org/10.48550/arXiv.1604.00359">https://doi.org/10.48550/arXiv.1604.00359</a></td>
+      <td>impl_coco</td>
+      <td>multimodal</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>2-40</td>
+      <td></td>
+      <td>COCO framework</td>
+      <td>C/Python</td>
+      <td></td>
+      <td><a href="https://github.com/numbbo/coco">https://github.com/numbbo/coco</a></td>
+      <td>Comparing Continuous Optimizers: black-box optimization benchmarking platform</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_bbob_biobj_mixint">
+      <td>suite_bbob_biobj_mixint</td>
+      <td>BBOB-biobj-mixint</td>
+      <td>Suite</td>
+      <td>continuous | integer</td>
+      <td>10-320</td>
+      <td>2</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>BBOB bi-objective mixed-integer test suite, <a href="https://doi.org/10.1145/3321707.3321868">https://doi.org/10.1145/3321707.3321868</a></td>
+      <td>impl_coco</td>
+      <td>multimodal</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>5-160</td>
+      <td>5-160</td>
+      <td>COCO framework</td>
+      <td>C/Python</td>
+      <td></td>
+      <td><a href="https://github.com/numbbo/coco">https://github.com/numbbo/coco</a></td>
+      <td>Comparing Continuous Optimizers: black-box optimization benchmarking platform</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_bbob_constrained">
+      <td>suite_bbob_constrained</td>
+      <td>BBOB-constrained</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>2-40</td>
+      <td>1</td>
+      <td></td>
+      <td>unknown</td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>bbob-constrained documentation, <a href="http://numbbo.github.io/coco-doc/bbob-constrained/">http://numbbo.github.io/coco-doc/bbob-constrained/</a></td>
+      <td>impl_coco</td>
+      <td>multimodal</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>2-40</td>
+      <td></td>
+      <td>COCO framework</td>
+      <td>C/Python</td>
+      <td></td>
+      <td><a href="https://github.com/numbbo/coco">https://github.com/numbbo/coco</a></td>
+      <td>Comparing Continuous Optimizers: black-box optimization benchmarking platform</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_bbob_largescale">
+      <td>suite_bbob_largescale</td>
+      <td>BBOB-largescale</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>20-640</td>
+      <td>1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>BBOB large-scale test suite, <a href="https://doi.org/10.48550/arXiv.1903.06396">https://doi.org/10.48550/arXiv.1903.06396</a></td>
+      <td>impl_coco</td>
+      <td>multimodal</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>20-640</td>
+      <td></td>
+      <td>COCO framework</td>
+      <td>C/Python</td>
+      <td></td>
+      <td><a href="https://github.com/numbbo/coco">https://github.com/numbbo/coco</a></td>
+      <td>Comparing Continuous Optimizers: black-box optimization benchmarking platform</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_bbob_mixint">
+      <td>suite_bbob_mixint</td>
+      <td>BBOB-mixint</td>
+      <td>Suite</td>
+      <td>continuous | integer</td>
+      <td>10-320</td>
+      <td>1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>BBOB mixed-integer test suite, <a href="https://doi.org/10.1145/3321707.3321868">https://doi.org/10.1145/3321707.3321868</a></td>
+      <td>impl_coco</td>
+      <td>multimodal</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>5-160</td>
+      <td>5-160</td>
+      <td>COCO framework</td>
+      <td>C/Python</td>
+      <td></td>
+      <td><a href="https://github.com/numbbo/coco">https://github.com/numbbo/coco</a></td>
+      <td>Comparing Continuous Optimizers: black-box optimization benchmarking platform</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_bbob_noisy">
+      <td>suite_bbob_noisy</td>
+      <td>BBOB-noisy</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>1</td>
+      <td>noisy</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>noisy</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Real-parameter black-box optimization benchmarking: noisy functions definitions, <a href="https://hal.inria.fr/inria-00369466">https://hal.inria.fr/inria-00369466</a></td>
+      <td>impl_coco_legacy</td>
+      <td>multimodal</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>COCO legacy (bbob-noisy)</td>
+      <td>C/Python</td>
+      <td></td>
+      <td><a href="https://web.archive.org/web/20210416065610/https://coco.gforge.inria.fr/doku.php?id=downloads">https://web.archive.org/web/20210416065610/https://coco.gforge.inria.fr/doku.php?id=downloads</a></td>
+      <td>Archived COCO download page that hosted the bbob-noisy suite</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_bp">
+      <td>suite_bp</td>
+      <td>BP</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>[10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
+      <td>noisy</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>unknown</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>BP benchmark, <a href="https://doi.org/10.1109/CEC.2019.8790277">https://doi.org/10.1109/CEC.2019.8790277</a></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_brachytherapy">
+      <td>suite_brachytherapy</td>
+      <td>Brachytherapy treatment planning</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>100-500</td>
+      <td>[2, 3]</td>
+      <td>multi-fidelity</td>
+      <td>unknown</td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td>yes</td>
+      <td></td>
+      <td>[1, 2]</td>
+      <td>Brachytherapy treatment planning</td>
+      <td>Treatment planning for internal radiation therapy. Multi-objective with aggregated objectives; no public source code.</td>
+      <td></td>
+      <td>Brachytherapy treatment planning, <a href="https://www.sciencedirect.com/science/article/pii/S1538472123016781">https://www.sciencedirect.com/science/article/pii/S1538472123016781</a></td>
+      <td></td>
+      <td>multimodal</td>
+      <td></td>
+      <td>real-world</td>
+      <td></td>
+      <td></td>
+      <td>100-500</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_car_structure">
+      <td>suite_car_structure</td>
+      <td>Car structure</td>
+      <td>Suite</td>
+      <td>integer</td>
+      <td>144-222</td>
+      <td>2</td>
+      <td></td>
+      <td>unknown</td>
+      <td>54</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>54 constraints</td>
+      <td></td>
+      <td>Car structure design benchmark, <a href="https://doi.org/10.1145/3205651.3205702">https://doi.org/10.1145/3205651.3205702</a></td>
+      <td>impl_car_structure</td>
+      <td></td>
+      <td></td>
+      <td>real-world</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>144-222</td>
+      <td>Car-structure benchmark</td>
+      <td></td>
+      <td></td>
+      <td><a href="http://ladse.eng.isas.jaxa.jp/benchmark/">http://ladse.eng.isas.jaxa.jp/benchmark/</a></td>
+      <td>JAXA LADSE benchmark problems</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_cdmp">
+      <td>suite_cdmp</td>
+      <td>CDMP</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>[10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
+      <td>dynamic | noisy</td>
+      <td>unknown</td>
+      <td>>=1</td>
+      <td>unknown</td>
+      <td>unknown</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>CDMP benchmark, <a href="https://doi.org/10.1145/3321707.3321878">https://doi.org/10.1145/3321707.3321878</a></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_cec2013">
+      <td>suite_cec2013</td>
+      <td>CEC2013</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>suite used for cec2013 competition. Also in IOH.</td>
+      <td></td>
+      <td>CEC2013 definitions, <a href="https://peerj.com/articles/cs-2671/CEC2013.pdf">https://peerj.com/articles/cs-2671/CEC2013.pdf</a></td>
+      <td>["impl_cec2013", "impl_iohexperimenter"]</td>
+      <td></td>
+      <td></td>
+      <td>artificial</td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>CEC2013 reference code | IOHexperimenter</td>
+      <td>C++/Python</td>
+      <td></td>
+      <td><a href="https://github.com/P-N-Suganthan/CEC2013">https://github.com/P-N-Suganthan/CEC2013</a> | <a href="https://github.com/IOHprofiler/IOHexperimenter">https://github.com/IOHprofiler/IOHexperimenter</a></td>
+      <td>Suganthan's reference implementation | IOHprofiler experimenter framework</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_cec2015_dmoo">
+      <td>suite_cec2015_dmoo</td>
+      <td>CEC2015-DMOO</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>0</td>
+      <td>[2, 3]</td>
+      <td>dynamic</td>
+      <td>unknown</td>
+      <td>>=1</td>
+      <td>dynamic</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Benchmark Functions for CEC 2015 Special Session and Competition on Dynamic Multi-objective Optimization</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>0</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_cec2018_dt">
+      <td>suite_cec2018_dt</td>
+      <td>CEC2018 DT</td>
+      <td>Suite</td>
+      <td>unknown</td>
+      <td>>=1</td>
+      <td>[2, 3]</td>
+      <td>dynamic</td>
+      <td></td>
+      <td></td>
+      <td>dynamic</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>CEC2018 Competition on Dynamic Multiobjective Optimisation</td>
+      <td>14 problems. Time-dependent: Pareto front/Pareto set geometry; irregular Pareto front shapes; variable-linkage; number of disconnected Pareto front segments; etc.</td>
+      <td></td>
+      <td>CEC2018 DMOP Competition TR, <a href="https://www.academia.edu/download/94499025/TR-CEC2018-DMOP-Competition.pdf">https://www.academia.edu/download/94499025/TR-CEC2018-DMOP-Competition.pdf</a></td>
+      <td>impl_pymoo</td>
+      <td></td>
+      <td></td>
+      <td>artificial</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>pymoo</td>
+      <td>Python</td>
+      <td></td>
+      <td><a href="https://github.com/anyoptimization/pymoo">https://github.com/anyoptimization/pymoo</a></td>
+      <td>Multi-objective optimization in Python</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_cec2022">
+      <td>suite_cec2022</td>
+      <td>CEC2022</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>suite used for cec2022 competition. Also in IOH.</td>
+      <td></td>
+      <td>CEC2022 TR, <a href="https://github.com/P-N-Suganthan/2022-SO-BO/blob/main/CEC2022%20TR.pdf">https://github.com/P-N-Suganthan/2022-SO-BO/blob/main/CEC2022%20TR.pdf</a></td>
+      <td>["impl_cec2022", "impl_iohexperimenter"]</td>
+      <td></td>
+      <td></td>
+      <td>artificial</td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>IOHexperimenter | CEC2022 reference code</td>
+      <td>C++/Python</td>
+      <td></td>
+      <td><a href="https://github.com/IOHprofiler/IOHexperimenter">https://github.com/IOHprofiler/IOHexperimenter</a> | <a href="https://github.com/P-N-Suganthan/2022-SO-BO">https://github.com/P-N-Suganthan/2022-SO-BO</a></td>
+      <td>IOHprofiler experimenter framework | Suganthan's reference implementation</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_cfd">
+      <td>suite_cfd</td>
+      <td>CFD</td>
+      <td>Suite</td>
+      <td>unknown</td>
+      <td>>=1</td>
+      <td>[1, 2]</td>
+      <td></td>
+      <td>unknown</td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>expensive evaluations 30s-15m</td>
+      <td></td>
+      <td>CFD test problem suite, <a href="https://doi.org/10.1007/978-3-319-99259-4_24">https://doi.org/10.1007/978-3-319-99259-4_24</a></td>
+      <td>impl_cfd</td>
+      <td></td>
+      <td></td>
+      <td>real-world</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>CFD test problem suite</td>
+      <td></td>
+      <td>30s-15m</td>
+      <td><a href="https://bitbucket.org/arahat/cfd-test-problem-suite">https://bitbucket.org/arahat/cfd-test-problem-suite</a></td>
+      <td>Expensive real-world CFD-based test problems</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_cre">
+      <td>suite_cre</td>
+      <td>CRE</td>
+      <td>Suite</td>
+      <td>continuous | integer</td>
+      <td>6-14</td>
+      <td>[2, 3, 4, 5]</td>
+      <td></td>
+      <td>unknown</td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Easy-to-evaluate real-world multi-objective optimization problems, Ryoji Tanabe; Hisao Ishibuchi, <a href="https://doi.org/10.1016/j.asoc.2020.106078">https://doi.org/10.1016/j.asoc.2020.106078</a></td>
+      <td>impl_reproblems</td>
+      <td></td>
+      <td></td>
+      <td>real-world-like</td>
+      <td></td>
+      <td></td>
+      <td>3-7</td>
+      <td>3-7</td>
+      <td>reproblems</td>
+      <td>Python</td>
+      <td></td>
+      <td><a href="https://github.com/ryojitanabe/reproblems">https://github.com/ryojitanabe/reproblems</a></td>
+      <td>Real-world inspired multi-objective optimization problem suite</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_cuter">
+      <td>suite_cuter</td>
+      <td>CUTEr</td>
+      <td>Suite</td>
+      <td>continuous | integer | binary</td>
+      <td>>=3</td>
+      <td>1</td>
+      <td></td>
+      <td>unknown</td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td>no</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>A constrained and unconstrained testing environment.</td>
+      <td></td>
+      <td>CUTEr, <a href="https://dl.acm.org/doi/10.1145/962437.962439">https://dl.acm.org/doi/10.1145/962437.962439</a></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>artificial</td>
+      <td>>=1</td>
+      <td></td>
+      <td>>=1</td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_cutest">
+      <td>suite_cutest</td>
+      <td>CUTEst</td>
+      <td>Suite</td>
+      <td>continuous | integer | binary</td>
+      <td>>=3</td>
+      <td>1</td>
+      <td></td>
+      <td>box | unknown</td>
+      <td>>=2</td>
+      <td></td>
+      <td></td>
+      <td>no</td>
+      <td></td>
+      <td></td>
+      <td>Constrained and Unconstrained Testing Environment with safe threads</td>
+      <td>CUTEst for optimization software</td>
+      <td></td>
+      <td>CUTEst, <a href="https://link.springer.com/article/10.1007/s10589-014-9687-3">https://link.springer.com/article/10.1007/s10589-014-9687-3</a></td>
+      <td>impl_pycutest</td>
+      <td>multimodal</td>
+      <td></td>
+      <td>artificial</td>
+      <td>>=1</td>
+      <td></td>
+      <td>>=1</td>
+      <td>>=1</td>
+      <td>pycutest</td>
+      <td>Python / C++ / Fortran</td>
+      <td></td>
+      <td><a href="https://github.com/jfowkes/pycutest">https://github.com/jfowkes/pycutest</a></td>
+      <td>Python interface to CUTEst</td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_dtlz">
+      <td>suite_dtlz</td>
+      <td>DTLZ</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>[10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Scalable multi-objective optimization test problems, Kalyanmoy Deb; Lothar Thiele; Marco Laumanns; Eckart Zitzler, <a href="https://doi.org/10.1109/CEC.2002.1007032">https://doi.org/10.1109/CEC.2002.1007032</a></td>
+      <td>impl_pymoo</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>pymoo</td>
+      <td>Python</td>
+      <td></td>
+      <td><a href="https://github.com/anyoptimization/pymoo">https://github.com/anyoptimization/pymoo</a></td>
+      <td>Multi-objective optimization in Python</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_dynamicbinval">
+      <td>suite_dynamicbinval</td>
+      <td>DynamicBinVal</td>
+      <td>Suite</td>
+      <td>binary</td>
+      <td>>=1</td>
+      <td>1</td>
+      <td>dynamic</td>
+      <td></td>
+      <td></td>
+      <td>dynamic</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Four versions of the dynamic binary value problem</td>
+      <td></td>
+      <td>DynamicBinVal, <a href="https://arxiv.org/pdf/2404.15837">https://arxiv.org/pdf/2404.15837</a></td>
+      <td>impl_iohexperimenter</td>
+      <td></td>
+      <td></td>
+      <td>artificial</td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>IOHexperimenter</td>
+      <td>C++/Python</td>
+      <td></td>
+      <td><a href="https://github.com/IOHprofiler/IOHexperimenter">https://github.com/IOHprofiler/IOHexperimenter</a></td>
+      <td>IOHprofiler experimenter framework</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_emo2017">
+      <td>suite_emo2017</td>
+      <td>EMO2017</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>4-24</td>
+      <td>2</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>BBComp EMO 2017, <a href="https://www.ini.rub.de/PEOPLE/glasmtbl/projects/bbcomp/">https://www.ini.rub.de/PEOPLE/glasmtbl/projects/bbcomp/</a></td>
+      <td>impl_emo2017</td>
+      <td></td>
+      <td></td>
+      <td>real-world</td>
+      <td></td>
+      <td></td>
+      <td>4-24</td>
+      <td></td>
+      <td>EMO 2017 real-world problems</td>
+      <td></td>
+      <td></td>
+      <td><a href="https://www.ini.rub.de/PEOPLE/glasmtbl/projects/bbcomp/downloads/realworld-problems-bbcomp-EMO-2017.zip">https://www.ini.rub.de/PEOPLE/glasmtbl/projects/bbcomp/downloads/realworld-problems-bbcomp-EMO-2017.zip</a></td>
+      <td>BBComp EMO-2017 real-world problem archive</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_etmof">
+      <td>suite_etmof</td>
+      <td>ETMOF</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>25-10000</td>
+      <td>[10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 2, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 3, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 4, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 5, 50, 6, 7, 8, 9]</td>
+      <td>dynamic</td>
+      <td></td>
+      <td></td>
+      <td>dynamic</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Evolutionary many-task optimization framework, <a href="https://doi.org/10.48550/arXiv.2110.08033">https://doi.org/10.48550/arXiv.2110.08033</a></td>
+      <td>impl_etmof</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>25-10000</td>
+      <td></td>
+      <td>ETMOF</td>
+      <td></td>
+      <td></td>
+      <td><a href="https://github.com/songbai-liu/etmo">https://github.com/songbai-liu/etmo</a></td>
+      <td>Evolutionary many-task optimization framework</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_expobench">
+      <td>suite_expobench</td>
+      <td>EXPObench</td>
+      <td>Suite</td>
+      <td>categorical | continuous | integer</td>
+      <td>30-405</td>
+      <td>1</td>
+      <td>noisy</td>
+      <td>box | unknown</td>
+      <td>>=2</td>
+      <td></td>
+      <td>["observational", "real-life"]</td>
+      <td>no</td>
+      <td></td>
+      <td></td>
+      <td>EXPensive Optimization benchmark library</td>
+      <td>Wind farm layout optimization, gas filter design, pipe shape optimization, hyperparameter tuning, and hospital simulation</td>
+      <td></td>
+      <td>EXPObench, <a href="https://doi.org/10.1016/j.asoc.2023.110744">https://doi.org/10.1016/j.asoc.2023.110744</a></td>
+      <td>impl_expobench</td>
+      <td></td>
+      <td></td>
+      <td>real-world</td>
+      <td></td>
+      <td>10-135</td>
+      <td>10-135</td>
+      <td>10-135</td>
+      <td>EXPObench</td>
+      <td>Python</td>
+      <td>2 to 80 seconds</td>
+      <td><a href="https://github.com/AlgTUDelft/ExpensiveOptimBenchmark">https://github.com/AlgTUDelft/ExpensiveOptimBenchmark</a></td>
+      <td>EXPensive Optimization benchmark library (wind farm layout, gas filter design, pipe shape, hyperparameter tuning, hospital simulation)</td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_gbea">
+      <td>suite_gbea</td>
+      <td>GBEA</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>[1, 2]</td>
+      <td>noisy</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>noisy</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>expensive evaluations 5s-35s, RW-GAN-Mario and TopTrumps are part of GBEA</td>
+      <td></td>
+      <td>Game benchmark for evolutionary algorithms, <a href="https://doi.org/10.1145/3321707.3321805">https://doi.org/10.1145/3321707.3321805</a></td>
+      <td>impl_gbea</td>
+      <td>multimodal</td>
+      <td></td>
+      <td>real-world</td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>coco-gbea</td>
+      <td></td>
+      <td>5s-35s</td>
+      <td><a href="https://github.com/ttusar/coco-gbea">https://github.com/ttusar/coco-gbea</a></td>
+      <td>Game-Benchmark for Evolutionary Algorithms (COCO fork)</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_gnbg">
+      <td>suite_gnbg</td>
+      <td>GNBG</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Generalized Numerical Benchmark Generator</td>
+      <td></td>
+      <td>GNBG, <a href="https://arxiv.org/abs/2312.07083">https://arxiv.org/abs/2312.07083</a></td>
+      <td>impl_gnbg</td>
+      <td></td>
+      <td></td>
+      <td>artificial</td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>GNBG Generator</td>
+      <td></td>
+      <td></td>
+      <td><a href="https://github.com/Danial-Yazdani/GNBG-Generator">https://github.com/Danial-Yazdani/GNBG-Generator</a></td>
+      <td>Generalized Numerical Benchmark Generator</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_gnbg_ii">
+      <td>suite_gnbg_ii</td>
+      <td>GNBG-II</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Generalized Numerical Benchmark Generator (version 2). Also available in IOH.</td>
+      <td></td>
+      <td>GNBG-II, <a href="https://dl.acm.org/doi/pdf/10.1145/3712255.3734271">https://dl.acm.org/doi/pdf/10.1145/3712255.3734271</a></td>
+      <td>["impl_gnbg_ii", "impl_iohgnbg"]</td>
+      <td></td>
+      <td></td>
+      <td>artificial</td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>IOHGNBG | GNBG-II</td>
+      <td></td>
+      <td></td>
+      <td><a href="https://github.com/IOHprofiler/IOHGNBG">https://github.com/IOHprofiler/IOHGNBG</a> | <a href="https://github.com/rohitsalgotra/GNBG-II">https://github.com/rohitsalgotra/GNBG-II</a></td>
+      <td>IOHprofiler version of GNBG | Generalized Numerical Benchmark Generator version 2</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_iohclustering">
+      <td>suite_iohclustering</td>
+      <td>IOHClustering</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Set of benchmark problems from clustering: optimization task is selecting cluster centers for a given set of data.</td>
+      <td></td>
+      <td>IOHClustering, <a href="https://arxiv.org/pdf/2505.09233">https://arxiv.org/pdf/2505.09233</a></td>
+      <td>impl_iohclustering</td>
+      <td>multimodal</td>
+      <td></td>
+      <td>artificial-from-real-data</td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>IOHClustering</td>
+      <td></td>
+      <td></td>
+      <td><a href="https://github.com/IOHprofiler/IOHClustering">https://github.com/IOHprofiler/IOHClustering</a></td>
+      <td>Clustering-based optimization benchmark built on ML datasets</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_kinematics_robotarm">
+      <td>suite_kinematics_robotarm</td>
+      <td>KinematicsRobotArm</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>21</td>
+      <td>1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Kinematics of a robot arm, <a href="https://doi.org/10.1023/A:1013258808932">https://doi.org/10.1023/A:1013258808932</a></td>
+      <td>impl_transfer_rf_bbob_rw</td>
+      <td>unimodal</td>
+      <td></td>
+      <td>real-world</td>
+      <td></td>
+      <td></td>
+      <td>21</td>
+      <td></td>
+      <td>Transfer Random Forests BBOB Real-world</td>
+      <td></td>
+      <td></td>
+      <td><a href="https://github.com/ShuaiqunPan/Transfer_Random_forests_BBOB_Real_world">https://github.com/ShuaiqunPan/Transfer_Random_forests_BBOB_Real_world</a></td>
+      <td>Real-world BBOB-like problem implementations (Porkchop, KinematicsRobotArm)</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_l1_zdt">
+      <td>suite_l1_zdt</td>
+      <td>L1-ZDT</td>
+      <td>Suite</td>
+      <td>continuous | binary</td>
+      <td>>=2</td>
+      <td>2</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Variant of ZDT with linkages between variables within groups</td>
+      <td></td>
+      <td>Linkage ZDT/DTLZ variants, <a href="https://doi.org/10.1145/1143997.1144179">https://doi.org/10.1145/1143997.1144179</a></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_l2_dtlz">
+      <td>suite_l2_dtlz</td>
+      <td>L2-DTLZ</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>[10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Variant of DTLZ2/DTLZ3 with linkages between all variables</td>
+      <td></td>
+      <td>Linkage ZDT/DTLZ variants, <a href="https://doi.org/10.1145/1143997.1144179">https://doi.org/10.1145/1143997.1144179</a></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_l2_zdt">
+      <td>suite_l2_zdt</td>
+      <td>L2-ZDT</td>
+      <td>Suite</td>
+      <td>continuous | binary</td>
+      <td>>=2</td>
+      <td>2</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Variant of ZDT with linkages between all variables</td>
+      <td></td>
+      <td>Linkage ZDT/DTLZ variants, <a href="https://doi.org/10.1145/1143997.1144179">https://doi.org/10.1145/1143997.1144179</a></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_l3_dtlz">
+      <td>suite_l3_dtlz</td>
+      <td>L3-DTLZ</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>[10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Variant of L2-DTLZ with anti-linkage mapping</td>
+      <td></td>
+      <td>Linkage ZDT/DTLZ variants, <a href="https://doi.org/10.1145/1143997.1144179">https://doi.org/10.1145/1143997.1144179</a></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_l3_zdt">
+      <td>suite_l3_zdt</td>
+      <td>L3-ZDT</td>
+      <td>Suite</td>
+      <td>continuous | binary</td>
+      <td>>=2</td>
+      <td>2</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Variant of L2-ZDT with anti-linkage mapping</td>
+      <td></td>
+      <td>Linkage ZDT/DTLZ variants, <a href="https://doi.org/10.1145/1143997.1144179">https://doi.org/10.1145/1143997.1144179</a></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_maop">
+      <td>suite_maop</td>
+      <td>MaOP</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>[10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
+      <td>noisy</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>unknown</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>MaOP benchmark, <a href="https://doi.org/10.1016/j.swevo.2019.02.003">https://doi.org/10.1016/j.swevo.2019.02.003</a></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_mechbench">
+      <td>suite_mechbench</td>
+      <td>MECHBench</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>1</td>
+      <td></td>
+      <td>unknown</td>
+      <td>{1, 2}</td>
+      <td></td>
+      <td></td>
+      <td>no</td>
+      <td></td>
+      <td></td>
+      <td>MECHBench</td>
+      <td>Set of problems inspired by Structural Mechanics Design Optimization. Embeds physical simulations (plasticity only, no fracture/damage). Unstructured/non-isotropic multimodality.</td>
+      <td></td>
+      <td>MECHBench, <a href="https://arxiv.org/abs/2511.10821">https://arxiv.org/abs/2511.10821</a></td>
+      <td>impl_mechbench</td>
+      <td>multimodal</td>
+      <td></td>
+      <td>real-world</td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>MECHBench</td>
+      <td>Python</td>
+      <td>1-7 minutes</td>
+      <td><a href="https://github.com/BayesOptApp/MECHBench">https://github.com/BayesOptApp/MECHBench</a></td>
+      <td>Structural mechanics design optimization benchmark</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_mf2">
+      <td>suite_mf2</td>
+      <td>MF2</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>1</td>
+      <td>multi-fidelity</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>[1, 2]</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>mf2: a collection of multi-fidelity benchmark functions in Python, <a href="https://doi.org/10.21105/joss.02049">https://doi.org/10.21105/joss.02049</a></td>
+      <td>impl_mf2</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>mf2</td>
+      <td>Python</td>
+      <td></td>
+      <td><a href="https://github.com/sjvrijn/mf2">https://github.com/sjvrijn/mf2</a></td>
+      <td>Multi-fidelity test function collection</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_minus_dtlz">
+      <td>suite_minus_dtlz</td>
+      <td>Minus DTLZ</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>[10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Variant of DTLZ that minimises the inverse of the base DTLZ functions</td>
+      <td></td>
+      <td>Minus DTLZ / Minus WFG, <a href="https://doi.org/10.1109/TEVC.2016.2587749">https://doi.org/10.1109/TEVC.2016.2587749</a></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_minus_wfg">
+      <td>suite_minus_wfg</td>
+      <td>Minus WFG</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>[10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Variant of WFG that minimises the inverse of the base WFG functions</td>
+      <td></td>
+      <td>Minus DTLZ / Minus WFG, <a href="https://doi.org/10.1109/TEVC.2016.2587749">https://doi.org/10.1109/TEVC.2016.2587749</a></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_mmopp">
+      <td>suite_mmopp</td>
+      <td>MMOPP</td>
+      <td>Suite</td>
+      <td>unknown</td>
+      <td>0</td>
+      <td>[2, 3, 4, 5, 6, 7]</td>
+      <td></td>
+      <td>unknown</td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>MMOPP technical report, <a href="http://www5.zzu.edu.cn/system/_content/download.jsp?urltype=news.DownloadAttachUrl&owner=1327567121&wbfileid=4764412">http://www5.zzu.edu.cn/system/_content/download.jsp?urltype=news.DownloadAttachUrl&owner=1327567121&wbfileid=4764412</a></td>
+      <td>impl_mmopp</td>
+      <td>multimodal</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>MMOPP</td>
+      <td></td>
+      <td></td>
+      <td><a href="http://www5.zzu.edu.cn/ecilab/info/1036/1251.htm">http://www5.zzu.edu.cn/ecilab/info/1036/1251.htm</a></td>
+      <td>ECI lab distribution page for MMOPP</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_modact">
+      <td>suite_modact</td>
+      <td>MODAct</td>
+      <td>Suite</td>
+      <td>continuous | integer</td>
+      <td>40</td>
+      <td>[2, 3, 4, 5]</td>
+      <td></td>
+      <td>unknown</td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>multiobjective design of actuators</td>
+      <td>Realistic Constrained Multi-Objective Optimization Benchmark Problems from Design.</td>
+      <td></td>
+      <td>MODAct, <a href="https://doi.org/10.1109/TEVC.2020.3020046">https://doi.org/10.1109/TEVC.2020.3020046</a></td>
+      <td>["impl_modact", "impl_pymoo"]</td>
+      <td></td>
+      <td></td>
+      <td>real-world</td>
+      <td></td>
+      <td></td>
+      <td>20</td>
+      <td>20</td>
+      <td>modact | pymoo</td>
+      <td>Python</td>
+      <td>20ms</td>
+      <td><a href="https://github.com/epfl-lamd/modact">https://github.com/epfl-lamd/modact</a> | <a href="https://github.com/anyoptimization/pymoo">https://github.com/anyoptimization/pymoo</a></td>
+      <td>EPFL-LAMD modact package | Multi-objective optimization in Python</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_morepo">
+      <td>suite_morepo</td>
+      <td>MOrepo</td>
+      <td>Suite</td>
+      <td>unknown</td>
+      <td>0</td>
+      <td>2</td>
+      <td>dynamic | noisy</td>
+      <td>unknown</td>
+      <td>>=1</td>
+      <td>unknown</td>
+      <td>unknown</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>impl_morepo</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>MOrepo</td>
+      <td></td>
+      <td></td>
+      <td><a href="https://github.com/MCDMSociety/MOrepo">https://github.com/MCDMSociety/MOrepo</a></td>
+      <td>Multi-objective optimisation problem repository</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_pbo">
+      <td>suite_pbo</td>
+      <td>PBO</td>
+      <td>Suite</td>
+      <td>binary</td>
+      <td>>=1</td>
+      <td>1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Suite of 25 binary optimization problems</td>
+      <td></td>
+      <td>PBO benchmarks, <a href="https://dl.acm.org/doi/pdf/10.1145/3319619.3326810">https://dl.acm.org/doi/pdf/10.1145/3319619.3326810</a></td>
+      <td>impl_iohexperimenter</td>
+      <td></td>
+      <td></td>
+      <td>artificial</td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>IOHexperimenter</td>
+      <td>C++/Python</td>
+      <td></td>
+      <td><a href="https://github.com/IOHprofiler/IOHexperimenter">https://github.com/IOHprofiler/IOHexperimenter</a></td>
+      <td>IOHprofiler experimenter framework</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_porkchop">
+      <td>suite_porkchop</td>
+      <td>PorkchopPlotInterplanetaryTrajectory</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>2</td>
+      <td>1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Porkchop plot interplanetary trajectory benchmark, <a href="https://doi.org/10.1109/CEC65147.2025.11042973">https://doi.org/10.1109/CEC65147.2025.11042973</a></td>
+      <td>impl_transfer_rf_bbob_rw</td>
+      <td>multimodal</td>
+      <td></td>
+      <td>real-world</td>
+      <td></td>
+      <td></td>
+      <td>2</td>
+      <td></td>
+      <td>Transfer Random Forests BBOB Real-world</td>
+      <td></td>
+      <td></td>
+      <td><a href="https://github.com/ShuaiqunPan/Transfer_Random_forests_BBOB_Real_world">https://github.com/ShuaiqunPan/Transfer_Random_forests_BBOB_Real_world</a></td>
+      <td>Real-world BBOB-like problem implementations (Porkchop, KinematicsRobotArm)</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_re">
+      <td>suite_re</td>
+      <td>RE</td>
+      <td>Suite</td>
+      <td>continuous | integer</td>
+      <td>4-14</td>
+      <td>[2, 3, 4, 5, 6, 7, 8, 9]</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Easy-to-evaluate real-world multi-objective optimization problems, Ryoji Tanabe; Hisao Ishibuchi, <a href="https://doi.org/10.1016/j.asoc.2020.106078">https://doi.org/10.1016/j.asoc.2020.106078</a></td>
+      <td>impl_reproblems</td>
+      <td></td>
+      <td></td>
+      <td>real-world-like</td>
+      <td></td>
+      <td></td>
+      <td>2-7</td>
+      <td>2-7</td>
+      <td>reproblems</td>
+      <td>Python</td>
+      <td></td>
+      <td><a href="https://github.com/ryojitanabe/reproblems">https://github.com/ryojitanabe/reproblems</a></td>
+      <td>Real-world inspired multi-objective optimization problem suite</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_rwmvop">
+      <td>suite_rwmvop</td>
+      <td>RWMVOP</td>
+      <td>Suite</td>
+      <td>continuous | integer | categorical</td>
+      <td>>=3</td>
+      <td>1</td>
+      <td></td>
+      <td>unknown</td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>RWMVOP, <a href="https://doi.org/10.1109/TEVC.2013.2281531">https://doi.org/10.1109/TEVC.2013.2281531</a></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>real-world</td>
+      <td></td>
+      <td>>=1</td>
+      <td>>=1</td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_sbox_cost">
+      <td>suite_sbox_cost</td>
+      <td>SBOX-COST</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>problems from BBOB but allows instances with the optimum close to the boundary</td>
+      <td></td>
+      <td>SBOX-COST, <a href="https://doi.org/10.48550/arXiv.2305.12221">https://doi.org/10.48550/arXiv.2305.12221</a></td>
+      <td>impl_iohexperimenter</td>
+      <td>multimodal</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>IOHexperimenter</td>
+      <td>C++/Python</td>
+      <td></td>
+      <td><a href="https://github.com/IOHprofiler/IOHexperimenter">https://github.com/IOHprofiler/IOHexperimenter</a></td>
+      <td>IOHprofiler experimenter framework</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_sdp">
+      <td>suite_sdp</td>
+      <td>SDP</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>[10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
+      <td>dynamic | noisy</td>
+      <td></td>
+      <td></td>
+      <td>dynamic</td>
+      <td>unknown</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>SDP dynamic multi-objective benchmark, <a href="https://doi.org/10.1109/TCYB.2019.2896021">https://doi.org/10.1109/TCYB.2019.2896021</a></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_submodular">
+      <td>suite_submodular</td>
+      <td>Submodular Optimization</td>
+      <td>Suite</td>
+      <td>binary</td>
+      <td>>=1</td>
+      <td>1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>set of graph-based submodular optimization problems from 4 problem types</td>
+      <td></td>
+      <td>Submodular optimization benchmark, <a href="https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10254181">https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10254181</a></td>
+      <td>impl_iohexperimenter</td>
+      <td></td>
+      <td></td>
+      <td>artificial</td>
+      <td>>=1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>IOHexperimenter</td>
+      <td>C++/Python</td>
+      <td></td>
+      <td><a href="https://github.com/IOHprofiler/IOHexperimenter">https://github.com/IOHprofiler/IOHexperimenter</a></td>
+      <td>IOHprofiler experimenter framework</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_tulipa_energy">
+      <td>suite_tulipa_energy</td>
+      <td>TulipaEnergy</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>1</td>
+      <td>noisy | multi-fidelity</td>
+      <td>unknown</td>
+      <td>>=2</td>
+      <td></td>
+      <td>parameter</td>
+      <td></td>
+      <td></td>
+      <td>[1, 2]</td>
+      <td>TulipaEnergyModel.jl</td>
+      <td>Determine the optimal investment and operation decisions for different assets in the energy system (production, consumption, conversion, storage, transport) while minimizing loss of load. Modelled as a potentially very large linear program with multiple fidelity levels.</td>
+      <td></td>
+      <td>TulipaEnergyModel.jl scientific references, <a href="https://tulipaenergy.github.io/TulipaEnergyModel.jl/stable/40-scientific-foundation/45-scientific-references">https://tulipaenergy.github.io/TulipaEnergyModel.jl/stable/40-scientific-foundation/45-scientific-references</a></td>
+      <td>impl_tulipa</td>
+      <td>unimodal</td>
+      <td></td>
+      <td>real-world</td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>TulipaEnergyModel.jl</td>
+      <td>Julia / JuMP</td>
+      <td>minutes to hours</td>
+      <td><a href="https://tulipaenergy.github.io/TulipaEnergyModel.jl/stable/">https://tulipaenergy.github.io/TulipaEnergyModel.jl/stable/</a> | <a href="https://github.com/TulipaEnergy/Tulipa-OBZ-CaseStudy">https://github.com/TulipaEnergy/Tulipa-OBZ-CaseStudy</a></td>
+      <td>Large linear program for optimal investment and operation of energy systems</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_vehicle_dynamics">
+      <td>suite_vehicle_dynamics</td>
+      <td>VehicleDynamics</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>2</td>
+      <td>1</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>VehicleDynamics benchmark, <a href="https://www.scitepress.org/Papers/2023/121580/121580.pdf">https://www.scitepress.org/Papers/2023/121580/121580.pdf</a></td>
+      <td>impl_vehicle_dynamics</td>
+      <td>multimodal</td>
+      <td></td>
+      <td>real-world</td>
+      <td></td>
+      <td></td>
+      <td>2</td>
+      <td></td>
+      <td>VehicleDynamics (Zenodo)</td>
+      <td></td>
+      <td></td>
+      <td><a href="https://zenodo.org/records/8307853">https://zenodo.org/records/8307853</a></td>
+      <td>Zenodo archive for the vehicle dynamics benchmark</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_wfg">
+      <td>suite_wfg</td>
+      <td>WFG</td>
+      <td>Suite</td>
+      <td>continuous</td>
+      <td>>=1</td>
+      <td>[10, 2, 3, 4, 5, 6, 7, 8, 9]</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>A review of multiobjective test problems and a scalable test problem toolkit, Simon Huband; Philip Hingston; Luigi Barone; Lyndon While, <a href="https://doi.org/10.1109/TEVC.2005.861417">https://doi.org/10.1109/TEVC.2005.861417</a></td>
+      <td>impl_pymoo</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>pymoo</td>
+      <td>Python</td>
+      <td></td>
+      <td><a href="https://github.com/anyoptimization/pymoo">https://github.com/anyoptimization/pymoo</a></td>
+      <td>Multi-objective optimization in Python</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+    <tr data-problem-id="suite_zdt">
+      <td>suite_zdt</td>
+      <td>ZDT</td>
+      <td>Suite</td>
+      <td>continuous | binary</td>
+      <td>>=2</td>
+      <td>2</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>Comparison of multiobjective evolutionary algorithms: empirical results, Eckart Zitzler; Kalyanmoy Deb; Lothar Thiele, <a href="https://doi.org/10.1162/106365600568202">https://doi.org/10.1162/106365600568202</a></td>
+      <td>impl_pymoo</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>>=1</td>
+      <td></td>
+      <td>pymoo</td>
+      <td>Python</td>
+      <td></td>
+      <td><a href="https://github.com/anyoptimization/pymoo">https://github.com/anyoptimization/pymoo</a></td>
+      <td>Multi-objective optimization in Python</td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+      <td></td>
+    </tr>
+  </tbody>
+<tfoot><tr><th>ID</th> <th>Name</th> <th>Type</th> <th>Variable Types</th> <th>Total Variables</th> <th>Objectives</th> <th>Properties</th> <th>Constraint Types</th> <th>Total Constraints</th> <th>Dynamics</th> <th>Noise</th> <th>Partial Evaluations</th> <th>Independent Objectives</th> <th>Fidelity Levels</th> <th>Full Name</th> <th>Description</th> <th>Tags</th> <th>References</th> <th>Implementations</th> <th>Modality</th> <th>Examples</th> <th>Source</th> <th>Binary Vars</th> <th>Categorical Vars</th> <th>Continuous Vars</th> <th>Integer Vars</th> <th>Implementation Names</th> <th>Implementation Languages</th> <th>Implementation Evaluation Times</th> <th>Implementation Links</th> <th>Implementation Descriptions</th> <th>Implementation Requirements</th> <th>Hard Box Constraints</th> <th>Soft Box Constraints</th> <th>Hard Linear Constraints</th> <th>Soft Linear Constraints</th> <th>Hard Function Constraints</th> <th>Soft Function Constraints</th></tr></tfoot></table>
+    </div>
+</section>
+
+<section class="details-shell" id="problem-details" aria-live="polite">
+    <h3 class="details-title">Problem details</h3>
+    <p class="details-hint" id="problem-details-hint">Click a table row to inspect full details.</p>
+    <dl class="details-grid" id="problem-details-content"></dl>
+    <div class="snippet-shell" id="problem-snippet-shell">
+        <h4 class="snippet-title">README.md</h4>
+        <p class="snippet-hint" id="problem-readme-hint">README content for this snippet folder will appear here.</p>
+        <div class="snippet-readme markdown-body" id="problem-readme-content"></div>
+        <h4 class="snippet-title">Snippet: <code id="problem-snippet-path">call_{problem_id}.py</code></h4>
+        <p class="snippet-meta"><a id="problem-snippet-folder-link" href="#" target="_blank" rel="noopener noreferrer">Open snippet folder</a></p>
+        <p class="snippet-hint" id="problem-snippet-hint">Select a problem to check for an available code snippet.</p>
+        <pre class="snippet-code"><code id="problem-snippet-content"></code></pre>
+    </div>
+</section>
diff --git a/docs/table_template.html b/docs/table_template.html
index 3e7a13b..ab18a45 100644
--- a/docs/table_template.html
+++ b/docs/table_template.html
@@ -1,7 +1,7 @@
 <section class="table-shell">
     <div class="table-toolbar">
         <details class="column-toolbar-disclosure">
-            <summary class="toolbar-title">Visible Columns</summary>
+            <summary class="toolbar-title">Toggle Visible Columns</summary>
             <div class="toolbar-actions">
                 <button type="button" class="toolbar-btn" id="show-all-columns">Show all</button>
                 <button type="button" class="toolbar-btn" id="hide-all-columns">Hide all</button>
diff --git a/pyproject.toml b/pyproject.toml
index 9be5ec9..79e56ed 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -11,6 +11,7 @@ dependencies = [
     "pydantic>=2.13.3",
     "pydantic-yaml>=1.6.0",
     "pyyaml>=6.0.3",
+    "pandas",
 ]
 
 [project.scripts]
diff --git a/uv.lock b/uv.lock
index 9850ab7..95c222e 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,6 +1,15 @@
 version = 1
 revision = 3
 requires-python = ">=3.10"
+resolution-markers = [
+    "python_full_version >= '3.14' and sys_platform == 'win32'",
+    "python_full_version >= '3.14' and sys_platform == 'emscripten'",
+    "python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+    "python_full_version >= '3.11' and python_full_version < '3.14' and sys_platform == 'win32'",
+    "python_full_version >= '3.11' and python_full_version < '3.14' and sys_platform == 'emscripten'",
+    "python_full_version >= '3.11' and python_full_version < '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+    "python_full_version < '3.11'",
+]
 
 [[package]]
 name = "annotated-types"
@@ -143,7 +152,7 @@ name = "exceptiongroup"
 version = "1.3.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "typing-extensions", marker = "python_full_version < '3.13'" },
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 name = "opltools"
 version = "0.1.0"
 source = { editable = "." }
 dependencies = [
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     { name = "pydantic" },
     { name = "pydantic-yaml" },
     { name = "pyyaml" },
@@ -204,6 +367,7 @@ dev = [
 
 [package.metadata]
 requires-dist = [
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     { name = "pydantic", specifier = ">=2.13.3" },
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