Problem examples

examples/GKLS.yaml

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+gkls:
+  name: GKLS
+  long_name: GKLS Generator
+  type: generator 
+  variables:
+    binary: 0
+    categorical: 0  
+    continuous: 
+    integer: 0
+  objectives: 
+  - 1
+  constraints: 
+  soft_constraints: 
+    box: 0
+  allows_partial_evaluation: no 
+  can_evaluate_objectives_independently: no 
+  code_examples: null
+  description: a set of test problems is constructed by defining a convex quadratic function which is systematically distorted by polynomials in order to produce local (and one global) minima
+  dynamic_type: null 
+  fidelity_levels: 1  
+  modality: 
+  - unimodal
+  - multimodal
+  noise_type: null 
+  references: 
+  - title: Algorithm 829 Software for Generation ofClasses of Test Functions with Known Localand Global Minima for Global Optimization
+    authors: MARCO GAVIANO, DMITRI E. KVASOV, DANIELA LERA and YAROSLAV D. SERGEYEV
+    url: https://dl.acm.org/doi/epdf/10.1145/962437.962444
+  source: artificial
+  tags: null 
+  implementations:
+  - gkls_c
+gkls_c: 
+  name: GKLS_C
+  description: Original C-implementation of GKLS
+  link:
+  - type: zipfils
+    url: https://netlib.org/toms/829.gz
+  language: C 
+  evaluation_time: null
+  requirements: null 
+

examples/ShapeOptimizationCFD.yaml

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+shapoptcfd:
+  name: ShapeOptimisation w CFD
+  long_name: Computationally Expensive Shape Optimisation Problems Using Computational Fluid Dynamics
+  type: suite 
+  variables:
+    binary: 0
+    categorical: 0  
+    continuous: 
+      min: 2
+    integer: 0
+  objectives: 
+  - [1,2]
+  constraints: 
+    function: 
+      min: 1
+    box: 1
+  soft_constraints: null
+  allows_partial_evaluation: no 
+  can_evaluate_objectives_independently: no 
+  code_examples: null
+  description: A Suite of Computationally Expensive Shape Optimisation Problems Using Computational Fluid Dynamics
+  dynamic_type: null 
+  fidelity_levels: 1  
+  modality: null
+  noise_type: null 
+  references: 
+  - title: A Suite of Computationally Expensive Shape Optimisation Problems Using Computational Fluid Dynamics
+    authors: S. Daniels, A. Rahat, R. Everson, G. Tabor and J. Fieldsend.
+    url: https://link.springer.com/chapter/10.1007/978-3-319-99259-4_24
+  source: real-world
+  tags: null
+  problems: 
+  - pitzdaily
+  - kaplandrafttube
+  - heatexchanger 
+  implementations:
+  - shapeopt
+shapeopt: 
+  name: shapeopt
+  description: Original implementation of ShapeOptimisation w CFD
+  link:
+  - type: repository
+    url: https://bitbucket.org/arahat/cfd-test-problem-suite/src/master/
+  language: Python 
+  evaluation_time: 30-1000 seconds
+  requirements: Relies on OpenFOAM 
+pitzdaily:
+  name: PitzDaily
+  long_name: PitzDaily Shape Optimisation Problem
+  type: problem 
+  variables:
+    binary: 0
+    categorical: 0  
+    continuous: 
+      min: 2
+    integer: 0
+  objectives: 
+  - 1
+  constraints: 
+    function: 
+      min: 1
+    box: 1
+  soft_constraints: null
+  allows_partial_evaluation: no 
+  can_evaluate_objectives_independently: no 
+  code_examples: null
+  description: A Computationally Expensive Shape Optimisation Problem Using Computational Fluid Dynamics
+  dynamic_type: null 
+  fidelity_levels: 1  
+  modality: null
+  noise_type: null 
+  references: 
+  - title: A Suite of Computationally Expensive Shape Optimisation Problems Using Computational Fluid Dynamics
+    authors: S. Daniels, A. Rahat, R. Everson, G. Tabor and J. Fieldsend.
+    url: https://link.springer.com/chapter/10.1007/978-3-319-99259-4_24
+  source: real-world
+  tags: null
+  implementations:
+  - shapeopt
+kaplandrafttube:
+  name: Kaplan Draft Tube
+  long_name: Sharp-heeled Kaplan Draft Tube Shape Optimisation Problem
+  type: problem 
+  variables:
+    binary: 0
+    categorical: 0  
+    continuous: 
+      min: 2
+    integer: 0
+  objectives: 
+  - 1
+  constraints: 
+    function: 
+      min: 1
+    box: 1
+  soft_constraints: null
+  allows_partial_evaluation: no 
+  can_evaluate_objectives_independently: no 
+  code_examples: null
+  description: A Computationally Expensive Shape Optimisation Problem Using Computational Fluid Dynamics
+  dynamic_type: null 
+  fidelity_levels: 1  
+  modality: null
+  noise_type: null 
+  references: 
+  - title: A Suite of Computationally Expensive Shape Optimisation Problems Using Computational Fluid Dynamics
+    authors: S. Daniels, A. Rahat, R. Everson, G. Tabor and J. Fieldsend.
+    url: https://link.springer.com/chapter/10.1007/978-3-319-99259-4_24
+  source: real-world
+  tags: null
+  implementations:
+  - shapeopt
+heatexchanger:
+  name: Heat Exchanger
+  long_name: Heat Exchanger Shape Optimisation Problem
+  type: problem 
+  variables:
+    binary: 0
+    categorical: 0  
+    continuous: 
+      min: 2
+    integer: 0
+  objectives: 
+  - 2
+  constraints: 
+    function: 
+      min: 1
+    box: 1
+  soft_constraints: null
+  allows_partial_evaluation: no 
+  can_evaluate_objectives_independently: no 
+  code_examples: null
+  description: A Computationally Expensive Shape Optimisation Problem Using Computational Fluid Dynamics
+  dynamic_type: null 
+  fidelity_levels: 1  
+  modality: null
+  noise_type: null 
+  references: 
+  - title: A Suite of Computationally Expensive Shape Optimisation Problems Using Computational Fluid Dynamics
+    authors: S. Daniels, A. Rahat, R. Everson, G. Tabor and J. Fieldsend.
+    url: https://link.springer.com/chapter/10.1007/978-3-319-99259-4_24
+  source: real-world
+  tags: null
+  implementations:
+  - shapeopt

template.yaml

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+# Please enter the information relevant to your problem/suite/generator.
+#
+# Instructions:
+# - Give your entry a unique id (e.g., "my_problem"). 
+# - Enter null for fields where the value is unknown.
+# - If there are any properties you want to add, use the 'tags' list to add them.
+your_problem_id:
+  name: short_problem_name
+  long_name: your_full_problem_name
+  type: problem # One of problem, suite or generator
+  variables: # For each variable type, enter the number of variables of that type, or a range by specifiying min and/or max
+    binary: 0 # Number of this varialbe type
+    categorical: [0,5]  # Options for numbers of this variable type (here, the suite has problems with either 0 or 5 categorical variables)
+    continuous: # For a range, you can specify the bounds
+      max: 80
+      min: 1
+    integer: # Or specify only one of the bounds, and leave the other as null
+      max: 5
+  objectives: # For objectives, also indicate the number, either as an integer, list or range
+  - 1
+  constraints: #constraints refers to hard constrains (violations don't have a true fitness value)
+  #Similar to variables, specify for each type of constraints the number, list, or a range. 
+    box: 1
+  soft_constraints: 
+    linear: 
+      max: 5
+    function: [5, 10]
+  allows_partial_evaluation: yes / no / some #Whether evaluation of subset(s) of decision variables are possible
+  can_evaluate_objectives_independently: yes / no / some # Can objective functions be evaluated independently of each other yes, no or some
+  code_examples: null #Link to a code example in the OPL repository
+  description: lorem ipsum #Textual description of the problem/suite/generator
+  dynamic_type: null #If the problem is dynamic, specify the type of dynamics. If not, enter null.
+  fidelity_levels: 1 #Number of fidelity levels, if applicable. If not, enter 1. 
+  modality: null #list of modalities (e.g. unimodal, multimodal)
+  noise_type: null #type of noise (e.g. additive, multiplicative, heteroscedastic)
+  references: null #list of references or citations
+  source: null #source of the problem/suite/generator
+  tags: null #list of tags or keywords
+  instances: null #For problems, specify the number of instances, or a list of instance ids, or a range of instance ids. For suites, specify the number of problems, or a list of problem ids, or a range of problem ids. For generators, specify the number of problems that can be generated, or a list of problem ids that can be generated, or a range of problem ids that can be generated.
+  problems: null #For suites, optionally specify the problems that are part of the suite. Each problem should then have a unique id, and be another entry in this yaml file. For generators, specify the problems that can be generated. Each problem should have a unique id, and be another entry in this yaml file. For problems, enter null.
+  implementations:
+  - your_implementation #If available, make an entry for the implementation of the problem/suite/generator as well. You can list multiple implementations if needed
+your_implementation: #implementations should also have unique identifiers
+  name: your_implementation
+  description: lorem ipsum #textual description of the implementation
+  link:
+  - type: GitHub
+    url: github.com/yourproject
+  - type: Package
+    url: linktoyourpackage
+  language: python # language of the implementation
+  evaluation_time: seconds #approximate time for a single full function evaluation
+  requirements: null #any important requirements to be able to run your implemenation