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Update webpage with new problems.
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‎docs/index.html‎

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<td>real-world</td>
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<td>Realistic Constrained Multi-Objective Optimization Benchmark Problems from Design</td>
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</tr>
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<tr>
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<td>IOHClustering</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>continuous</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>yes</td>
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<td>no</td>
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<td>Based on ML clustering datasets</td>
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<td><a href="https://arxiv.org/pdf/2505.09233" target="_blank">https://arxiv.org/pdf/2505.09233</a></td>
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<td><a href="https://github.com/IOHprofiler/IOHClustering" target="_blank">https://github.com/IOHprofiler/IOHClustering</a></td>
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<td>artificial, but based on real data</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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</tr>
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<tr>
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<td>GNBG-II</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>continuous</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>no</td>
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<td>Implementation in IOHexperimenter: https://github.com/IOHprofiler/IOHGNBG</td>
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<td><a href="https://dl.acm.org/doi/pdf/10.1145/3712255.3734271" target="_blank">https://dl.acm.org/doi/pdf/10.1145/3712255.3734271</a></td>
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<td><a href="https://github.com/rohitsalgotra/GNBG-II" target="_blank">https://github.com/rohitsalgotra/GNBG-II</a></td>
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<td>artificial</td>
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<td>Generalized Numerical Benchmark Generator (version 2)</td>
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</tr>
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<tr>
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<td>GNBG</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>continuous</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>no</td>
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<td></td>
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<td><a href="https://arxiv.org/abs/2312.07083" target="_blank">https://arxiv.org/abs/2312.07083</a></td>
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<td><a href="https://github.com/Danial-Yazdani/GNBG-Generator" target="_blank">https://github.com/Danial-Yazdani/GNBG-Generator</a></td>
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<td>artificial</td>
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<td>Generalized Numerical Benchmark Generator</td>
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</tr>
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<tr>
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<td>DynamicBinVal</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>binary</td>
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<td>no</td>
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<td>yes</td>
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<td>no</td>
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<td>?</td>
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<td>no</td>
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<td></td>
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<td><a href="https://arxiv.org/pdf/2404.15837" target="_blank">https://arxiv.org/pdf/2404.15837</a></td>
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<td><a href="https://github.com/IOHprofiler/IOHexperimenter" target="_blank">https://github.com/IOHprofiler/IOHexperimenter</a></td>
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<td>artificial</td>
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<td>Four versions of the dynamic binary value problem</td>
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</tr>
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<tr>
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<td>PBO</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>binary</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>no</td>
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<td></td>
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<td><a href="https://dl.acm.org/doi/pdf/10.1145/3319619.3326810" target="_blank">https://dl.acm.org/doi/pdf/10.1145/3319619.3326810</a></td>
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<td><a href="https://github.com/IOHprofiler/IOHexperimenter" target="_blank">https://github.com/IOHprofiler/IOHexperimenter</a></td>
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<td>artificial</td>
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<td>Suite of 25 binary optimization problems</td>
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</tr>
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<tr>
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<td>W-model</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>binary</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>no</td>
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<td></td>
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<td><a href="https://dl.acm.org/doi/abs/10.1145/3205651.3208240?casa_token=S4U_Pi9f6MwAAAAA:U9ztNTPwmupT8K3GamWZfBL7-8fqjxPtr_kprv51vdwA-REsp0EyOFGa99BtbANb0XbqyrVg795hIw" target="_blank">https://dl.acm.org/doi/abs/10.1145/3205651.3208240?casa_token=S4U_Pi9f6MwAAAAA:U9ztNTPwmupT8K3GamWZfBL7-8fqjxPtr_kprv51vdwA-REsp0EyOFGa99BtbANb0XbqyrVg795hIw</a></td>
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<td><a href="https://github.com/thomasWeise/BBDOB_W_Model" target="_blank">https://github.com/thomasWeise/BBDOB_W_Model</a></td>
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<td>artificial</td>
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<td>Tunable generator for binary optimization based on several difficulty features</td>
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</tr>
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<tr>
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<td>Submodular Optimitzation</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>binary</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>no</td>
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<td></td>
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<td><a href="https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10254181" target="_blank">https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10254181</a></td>
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<td><a href="https://github.com/IOHprofiler/IOHexperimenter" target="_blank">https://github.com/IOHprofiler/IOHexperimenter</a></td>
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<td>artificial</td>
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<td>set of graph-based submodular optimization problems from 4 problem types</td>
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</tr>
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<tr>
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<td>CEC2013</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>continuous</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>no</td>
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<td>Implementation available in IOHexperimenter: https://github.com/IOHprofiler/IOHexperimenter</td>
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<td><a href="https://peerj.com/articles/cs-2671/CEC2013.pdf" target="_blank">https://peerj.com/articles/cs-2671/CEC2013.pdf</a></td>
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<td><a href="https://github.com/P-N-Suganthan/CEC2013" target="_blank">https://github.com/P-N-Suganthan/CEC2013</a></td>
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<td>artificial</td>
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<td>suite used for cec2013 competition</td>
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</tr>
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<tr>
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<td>CEC2022</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>continuous</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>no</td>
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<td>Implementation available in IOHexperimenter: https://github.com/IOHprofiler/IOHexperimenter</td>
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<td><a href="https://github.com/P-N-Suganthan/2022-SO-BO/blob/main/CEC2022%20TR.pdf" target="_blank">https://github.com/P-N-Suganthan/2022-SO-BO/blob/main/CEC2022%20TR.pdf</a></td>
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<td><a href="https://github.com/P-N-Suganthan/2022-SO-BO" target="_blank">https://github.com/P-N-Suganthan/2022-SO-BO</a></td>
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<td>artificial</td>
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<td>suite used for cec2022 competition</td>
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</tr>
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</tbody>
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<tfoot><tr><th>name</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>multimodal</th> <th>multi-fidelity</th> <th>other info</th> <th>reference</th> <th>implementation</th> <th>source (real-world/artificial)</th> <th>textual description</th></tr> </tfoot></table>
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‎docs/problems.html‎

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<td>real-world</td>
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<td>Realistic Constrained Multi-Objective Optimization Benchmark Problems from Design</td>
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</tr>
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<tr>
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<td>IOHClustering</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>continuous</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>yes</td>
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<td>no</td>
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<td>Based on ML clustering datasets</td>
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<td><a href="https://arxiv.org/pdf/2505.09233" target="_blank">https://arxiv.org/pdf/2505.09233</a></td>
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<td><a href="https://github.com/IOHprofiler/IOHClustering" target="_blank">https://github.com/IOHprofiler/IOHClustering</a></td>
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<td>artificial, but based on real data</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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</tr>
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<tr>
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<td>GNBG-II</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>continuous</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>no</td>
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<td>Implementation in IOHexperimenter: https://github.com/IOHprofiler/IOHGNBG</td>
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<td><a href="https://dl.acm.org/doi/pdf/10.1145/3712255.3734271" target="_blank">https://dl.acm.org/doi/pdf/10.1145/3712255.3734271</a></td>
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<td><a href="https://github.com/rohitsalgotra/GNBG-II" target="_blank">https://github.com/rohitsalgotra/GNBG-II</a></td>
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<td>artificial</td>
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<td>Generalized Numerical Benchmark Generator (version 2)</td>
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</tr>
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<tr>
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<td>GNBG</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>continuous</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>no</td>
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<td></td>
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<td><a href="https://arxiv.org/abs/2312.07083" target="_blank">https://arxiv.org/abs/2312.07083</a></td>
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<td><a href="https://github.com/Danial-Yazdani/GNBG-Generator" target="_blank">https://github.com/Danial-Yazdani/GNBG-Generator</a></td>
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<td>artificial</td>
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<td>Generalized Numerical Benchmark Generator</td>
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</tr>
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<tr>
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<td>DynamicBinVal</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>binary</td>
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<td>no</td>
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<td>yes</td>
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<td>no</td>
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<td>?</td>
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<td>no</td>
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<td></td>
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<td><a href="https://arxiv.org/pdf/2404.15837" target="_blank">https://arxiv.org/pdf/2404.15837</a></td>
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<td><a href="https://github.com/IOHprofiler/IOHexperimenter" target="_blank">https://github.com/IOHprofiler/IOHexperimenter</a></td>
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<td>artificial</td>
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<td>Four versions of the dynamic binary value problem</td>
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</tr>
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<tr>
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<td>PBO</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>binary</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>no</td>
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<td></td>
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<td><a href="https://dl.acm.org/doi/pdf/10.1145/3319619.3326810" target="_blank">https://dl.acm.org/doi/pdf/10.1145/3319619.3326810</a></td>
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<td><a href="https://github.com/IOHprofiler/IOHexperimenter" target="_blank">https://github.com/IOHprofiler/IOHexperimenter</a></td>
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<td>artificial</td>
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<td>Suite of 25 binary optimization problems</td>
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</tr>
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<tr>
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<td>W-model</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>binary</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>no</td>
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<td></td>
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<td><a href="https://dl.acm.org/doi/abs/10.1145/3205651.3208240?casa_token=S4U_Pi9f6MwAAAAA:U9ztNTPwmupT8K3GamWZfBL7-8fqjxPtr_kprv51vdwA-REsp0EyOFGa99BtbANb0XbqyrVg795hIw" target="_blank">https://dl.acm.org/doi/abs/10.1145/3205651.3208240?casa_token=S4U_Pi9f6MwAAAAA:U9ztNTPwmupT8K3GamWZfBL7-8fqjxPtr_kprv51vdwA-REsp0EyOFGa99BtbANb0XbqyrVg795hIw</a></td>
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<td><a href="https://github.com/thomasWeise/BBDOB_W_Model" target="_blank">https://github.com/thomasWeise/BBDOB_W_Model</a></td>
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<td>artificial</td>
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<td>Tunable generator for binary optimization based on several difficulty features</td>
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</tr>
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<tr>
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<td>Submodular Optimitzation</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>binary</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>no</td>
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<td></td>
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<td><a href="https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10254181" target="_blank">https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10254181</a></td>
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<td><a href="https://github.com/IOHprofiler/IOHexperimenter" target="_blank">https://github.com/IOHprofiler/IOHexperimenter</a></td>
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<td>artificial</td>
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<td>set of graph-based submodular optimization problems from 4 problem types</td>
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</tr>
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<tr>
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<td>CEC2013</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>continuous</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>no</td>
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<td>Implementation available in IOHexperimenter: https://github.com/IOHprofiler/IOHexperimenter</td>
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<td><a href="https://peerj.com/articles/cs-2671/CEC2013.pdf" target="_blank">https://peerj.com/articles/cs-2671/CEC2013.pdf</a></td>
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<td><a href="https://github.com/P-N-Suganthan/CEC2013" target="_blank">https://github.com/P-N-Suganthan/CEC2013</a></td>
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<td>artificial</td>
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<td>suite used for cec2013 competition</td>
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</tr>
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<tr>
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<td>CEC2022</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>continuous</td>
980+
<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>no</td>
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<td>Implementation available in IOHexperimenter: https://github.com/IOHprofiler/IOHexperimenter</td>
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<td><a href="https://github.com/P-N-Suganthan/2022-SO-BO/blob/main/CEC2022%20TR.pdf" target="_blank">https://github.com/P-N-Suganthan/2022-SO-BO/blob/main/CEC2022%20TR.pdf</a></td>
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<td><a href="https://github.com/P-N-Suganthan/2022-SO-BO" target="_blank">https://github.com/P-N-Suganthan/2022-SO-BO</a></td>
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<td>artificial</td>
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<td>suite used for cec2022 competition</td>
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</tr>
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</tbody>
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<tfoot><tr><th>name</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>multimodal</th> <th>multi-fidelity</th> <th>other info</th> <th>reference</th> <th>implementation</th> <th>source (real-world/artificial)</th> <th>textual description</th></tr> </tfoot></table>

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