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@@ -315,7 +315,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
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<td>CFD</td>
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<td></td>
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<td>expensive evaluations 30s-15m</td>
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<td>suite</td>
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<td>1-2</td>
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<td>scalable</td>
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<td>GBEA</td>
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<td></td>
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<td>expensive evaluations 5s-35s</td>
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<td>suite</td>
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<td>1-2</td>
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<td>scalable</td>
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<td>Car structure</td>
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<td></td>
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<td>54 constraints</td>
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<td>suite</td>
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<td>2</td>
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<td>144-222</td>
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<td>JSEC2019</td>
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<td></td>
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<td>expensive evaluations 3s; 22 constraints</td>
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<td>single</td>
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<td>1-5</td>
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<td>32</td>
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<td>SBOX-COST</td>
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<td></td>
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<td>problems from BBOB but allows instances with the optimum close to the boundary</td>
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<td>suite</td>
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<td>1</td>
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<td>scalable</td>
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<td>ρMNK-Landscapes</td>
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<td></td>
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<td>tunable variable and objective dimensions; tunable multimodality and correlation between objectives</td>
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<td>generator</td>
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<td>scalable</td>
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<td>scalable</td>
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<td>mUBQP</td>
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<td></td>
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<td>tunable variable and objective dimensions; tunable density and correlation between objectives</td>
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<td>generator</td>
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<td>scalable</td>
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<td>scalable</td>
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<td>ρmTSP</td>
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<td></td>
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<td>tunable variable and objective dimensions; tunable instance type (euclidian/random); tunable correlation between objectives</td>
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<td>generator</td>
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<td>scalable</td>
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<td>scalable</td>
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<td>Ealain</td>
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<td></td>
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<td>Real-world-like, easily extensible to increase complexity</td>
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<td>generator</td>
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<td>1+</td>
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<td>scalable</td>
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<td>optional</td>
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<td>no</td>
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<td>optional</td>
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<td></td>
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<td>Real-world-like</td>
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<td><a href="https://doi.org/10.1145/3638530.3654299" target="_blank">https://doi.org/10.1145/3638530.3654299</a></td>
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<td><a href="https://github.com/qrenau/Ealain" target="_blank">https://github.com/qrenau/Ealain</a></td>
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<td>MA-BBOB</td>
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<td></td>
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<td>Generator that creates affine combinations of BBOB functions</td>
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<td>generator</td>
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<td>1</td>
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<td>scalable</td>
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<td>no</td>
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<td>no</td>
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<td>no</td>
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<td></td>
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<td>artificial</td>
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<td><a href="https://doi.org/10.1145/3673908" target="_blank">https://doi.org/10.1145/3673908</a></td>
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<td><a href="https://github.com/IOHprofiler/IOHexperimenter/blob/master/example/Competitions/MA-BBOB/Example_MABBOB.ipynb" target="_blank">https://github.com/IOHprofiler/IOHexperimenter/blob/master/example/Competitions/MA-BBOB/Example_MABBOB.ipynb</a></td>
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<td>MPM2</td>
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<td></td>
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<td>nonlinear nonseparable nonsymmetric; scalable in terms of time to evaluate the objective function</td>
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<td>generator</td>
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<td>1</td>
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<td>scalable</td>
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<td>Convex DTLZ2</td>
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<td>Variant of DTLZ2 with a convex Pareto front (instead of concave)</td>
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<td>single</td>
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<td>2+</td>
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<td>scalable</td>
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<td>Inverted DTLZ1</td>
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<td></td>
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<td>Variant of DTLZ1 with an inverted Pareto front</td>
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<td>single</td>
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<td>2+</td>
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<td>scalable</td>
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<td>Minus DTLZ</td>
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<td>Variant of DTLZ that minimises the inverse of the base DTLZ functions</td>
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<td>suite</td>
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<td>2+</td>
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<td>scalable</td>
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<td>Minus WFG</td>
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<td>Variant of WFG that minimises the inverse of the base WFG functions</td>
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<td>suite</td>
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<td>2+</td>
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<td>scalable</td>
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<td>L1-ZDT</td>
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<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>
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<td>suite</td>
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<td>2</td>
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<td>scalable</td>
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<td>L2-ZDT</td>
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<td>Variant of ZDT with linkages between all variables; Linear recombination operators can potentially take advantage of the problem structure</td>
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<td>suite</td>
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<td>2</td>
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<td>scalable</td>
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<td>L3-ZDT</td>
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<td>Variant of L2-ZDT using a mapping to prevent linear recombination operators from potentially taking advantage of the problem structure</td>
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<td>suite</td>
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<td>2</td>
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<td>scalable</td>
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<td>L2-DTLZ</td>
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<td>Variant of DTLZ2 and DTLZ3 with linkages between all variables; Linear recombination operators can potentially take advantage of the problem structure</td>
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<td>2+</td>
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<td>scalable</td>
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<td>L3-DTLZ</td>
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<td>Variant of L2-DTLZ using a mapping to prevent linear recombination operators from potentially taking advantage of the problem structure</td>
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<td>suite</td>
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<td>2+</td>
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<td>scalable</td>
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<td>MODAct - multiobjective design of actuators</td>
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<td>Realistic Constrained Multi-Objective Optimization Benchmark Problems from Design</td>
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<td>Realistic Constrained Multi-Objective Optimization Benchmark Problems from Design. Need the https://github.com/epfl-lamd/modact package installed; evaluation times around 20ms</td>
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<td>suite</td>
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<td>2 3 4 or 5</td>
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<td>20</td>
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<td>IOHClustering</td>
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<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</td>
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<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>
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<td>suite; generator</td>
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<td>1</td>
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<td>scalable</td>
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<td>GNBG-II</td>
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<td>Generalized Numerical Benchmark Generator (version 2)</td>
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<td>Generalized Numerical Benchmark Generator (version 2). Also in IOH https://github.com/IOHprofiler/IOHGNBG</td>
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<td>suite; generator</td>
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<td>1</td>
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<td>scalable</td>
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<td>CEC2013</td>
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<td>suite used for cec2013 competition</td>
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<td>suite used for cec2013 competition. Also in IOH https://github.com/IOHprofiler/IOHexperimenter</td>
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<td>suite</td>
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<td>1</td>
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<td>scalable</td>
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<td>CEC2022</td>
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<td>suite used for cec2022 competition</td>
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<td>suite used for cec2022 competition. Also in IOH https://github.com/IOHprofiler/IOHexperimenter</td>
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<td>suite</td>
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<td>1</td>
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<td>scalable</td>

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