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                <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>
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        <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>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>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>continuous | integer</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>categorical | continuous | integer</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></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></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>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>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>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>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></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></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>categorical | continuous | integer</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</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>multi-fidelity | noisy</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>