diff --git a/.github/workflows/check_python_package.yaml b/.github/workflows/check_python_package.yaml
index 5bd82ab..33262cb 100644
--- a/.github/workflows/check_python_package.yaml
+++ b/.github/workflows/check_python_package.yaml
@@ -14,7 +14,7 @@ jobs:
python-version: ["3.10", "3.11", "3.12", "3.13"]
runs-on: ${{matrix.runs-on}}
steps:
- - uses: actions/checkout@v6
+ - uses: actions/checkout@v7
with:
fetch-depth: 0
diff --git a/.github/workflows/new_problem_check.yml b/.github/workflows/new_problem_check.yml
index ebc9ba2..7bd1cbf 100644
--- a/.github/workflows/new_problem_check.yml
+++ b/.github/workflows/new_problem_check.yml
@@ -21,10 +21,10 @@ jobs:
runs-on: ubuntu-latest
steps:
- name: Checkout repository
- uses: actions/checkout@v5
+ uses: actions/checkout@v7
- name: Set up Python
- uses: actions/setup-python@v4
+ uses: actions/setup-python@v6
with:
python-version: '3.x'
@@ -35,5 +35,5 @@ jobs:
- name: Run New Problem Check
run: |
- python utils/validate_yaml.py problems.yaml
+ uv run utils/validate_yaml.py problems.yaml
diff --git a/.github/workflows/run_to_html.yml b/.github/workflows/run_to_html.yml
index 2d28e87..cf449e1 100644
--- a/.github/workflows/run_to_html.yml
+++ b/.github/workflows/run_to_html.yml
@@ -13,7 +13,7 @@ jobs:
contents: write
steps:
- name: Checkout repository
- uses: actions/checkout@v5
+ uses: actions/checkout@v7
with:
ref: ${{ github.event.pull_request.head.ref }}
persist-credentials: true
diff --git a/OPL_form.csv b/OPL_form.csv
deleted file mode 100644
index 841b7d7..0000000
--- a/OPL_form.csv
+++ /dev/null
@@ -1,3 +0,0 @@
-Timestamp,Submitter Name,Submitter Email,Short name of Suite / Problem / Generator,Type,Types of input variables,Number of Input variables (number or range or 'scalable'),Number of Objectives (number or range or 'scalable'),Problem Characteristics [Constrained],Problem Characteristics [Dynamic],Problem Characteristics [Noisy],Problem Characteristics [Multi-modal],Problem Characteristics [Partial evaluations possible],Problem Characteristics [Multiple fidelities],Problem Source,Reference,Short description of problem(s),Do you wish to provide some more detailed information about the proposed problems?,Full name of suite,Constraint Properties,Number of constraints,Type of Dynamicism,Form of noise model,Type of noise space,Other noise properties,Description of multimodality,Citation / reference,Key challenges / characteristics,Scientific motivation for the proposed suite / problem / generator,Limitations of the proposed suite / problem / generator,Implemenation languages,Approximate time to evaluate a single solution (or times if e.g. multi-fidelity),Links to examples of usage of the proposed suite / problem / generator,Other relevant information,Feedback about the form
-16/10/2025 11:17:36,,,test,Single Problem,Continuous,1,1,Multi-modal,,,,,,Real-World Application,test,test,Yes (go to detailed information),,,,,,,,,,,,,,,,,
-26/11/2025 12:05:57,Anonymized,Anonymized,MECHBench,Problem Suite,Continuous,scalable',1,Present,Not Present,Not Present,Present,Not Present,Not Present,Real-World Application,https://github.com/BayesOptApp/MECHBench,"This is a set of problems with inspiration from Structural Mechanics Design Optimization. The suite comprises three physical models, from which the user may define different kind of problems which impact the final design output.",Yes (go to detailed information),MECHBench,Hard Constraints,1 or 2,None,None,None,None,Unstructured or non isotropic multimodality,https://arxiv.org/abs/2511.10821,Embeds physical simulations and is flexible and modular,Bridge the black-box optimization techniques to a Mechanical Design Problem which require these kinds of algorithms,"The models do not include fracture or damage mechanics, just plasticity.",Python,Times -> from 1 minute to 7 minutes,,,
\ No newline at end of file
diff --git a/docs/index.html b/docs/index.html
index d0e1c9b..ba068bc 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -33,7 +33,7 @@ OPL – Optimisation problem library
Hide all
- ID Name Type Variable Types Total Variables Objectives Properties Constraint Types Total Constraints Dynamics Noise Partial Evaluations Independent Objectives Fidelity Levels Full Name Description Tags References Implementations Modality Evaluation Time Examples Source Binary Vars Categorical Vars Continuous Vars Integer Vars Implementation Names Implementation Languages Implementation Evaluation Times Implementation Links Implementation Descriptions Implementation Requirements Hard Box Constraints Soft Box Constraints Hard Linear Constraints Soft Linear Constraints Hard Function Constraints Soft Function Constraints ID Name Type Variable Types Total Variables Objectives Properties Constraint Types Total Constraints Dynamics Noise Partial Evaluations Independent Objectives Fidelity Levels Full Name Description Tags References Implementations Modality Examples Source Binary Vars Categorical Vars Continuous Vars Integer Vars Implementation Names Implementation Languages Implementation Evaluation Times Implementation Links Implementation Descriptions Implementation Requirements Hard Box Constraints Soft Box Constraints Hard Linear Constraints Soft Linear Constraints Hard Function Constraints Soft Function Constraints
@@ -61,7 +61,6 @@ OPL – Optimisation problem library
References
Implementations
Modality
- Evaluation Time
Examples
Source
Binary Vars
@@ -105,7 +104,6 @@ OPL – Optimisation problem library
unimodal
real-world
OPL – Optimisation problem library
>=2
2
unknown | box
+ box | unknown
>=2
OPL – Optimisation problem library
Building spatial design, https://hdl.handle.net/1887/81789
impl_bso_toolbox
["1 second", "40 seconds"]
real-world
>=1
@@ -154,7 +151,7 @@ OPL – Optimisation problem library
BSO-toolbox
C++
- {'40 seconds', '1 second'}
+ ~1s (smallest) to ~40s (larger)
https://github.com/TUe-excellent-buildings/BSO-toolbox Building Spatial Design toolbox (TU/e)
OPL – Optimisation problem library
>=1
OPL – Optimisation problem library
fn_emdo
Electric Motor Design Optimization
Problem
- integer | continuous
+ continuous | integer
26
1
noisy
- unknown | box
+ box | unknown
>=14
noisy
@@ -227,7 +223,6 @@ OPL – Optimisation problem library
A Multi-Step Evaluation Process in Electric Motor Design, Tea Tušar; Peter Korošec; Bogdan Filipič, https://dis.ijs.si/tea/Publications/Tusar23Multistep.pdf
impl_emdo
multimodal
- 8 minutes
real-world
OPL – Optimisation problem library
13
Electric Motor Design Optimization
Python
- {'8 minutes'}
+ 8 minutes
Not publicly available
OPL – Optimisation problem library
real-world
OPL – Optimisation problem library
fn_gasoline
Gasoline direct injection engine design
Problem
- integer | continuous
+ continuous | integer
14
2
multi-fidelity
@@ -309,7 +303,6 @@ OPL – Optimisation problem library
Gasoline direct injection engine design, https://doi.org/10.1016/j.ejor.2022.08.032
impl_gasoline
[]
real-world
OPL – Optimisation problem library
artificial
>=1
OPL – Optimisation problem library
>=1
OPL – Optimisation problem library
JPNSEC EC-Symposium 2019 competition, http://www.jpnsec.org/files/competition2019/EC-Symposium-2019-Competition-English.html
impl_jsec2019
3s
real-world
OPL – Optimisation problem library
JSEC 2019 competition
{'3s'}
+ 3s
http://www.jpnsec.org/files/competition2019/EC-Symposium-2019-Competition-English.html JPNSEC EC-Symposium 2019 competition problem
OPL – Optimisation problem library
artificial
>=1
OPL – Optimisation problem library
artificial
>=1
OPL – Optimisation problem library
Radar waveform design, https://doi.org/10.1007/978-3-540-70928-2_53
impl_radar_waveform
[]
real-world
OPL – Optimisation problem library
BEACON, https://dl.acm.org/doi/10.1145/3712255.3734303
impl_beacon
multimodal
- negligible
artificial
OPL – Optimisation problem library
BEACON
Python
- {'negligible'}
+ negligible
https://github.com/Stebbet/BEACON/ Continuous Bi-objective Benchmark with Explicit Adjustable COrrelatioN control
OPL – Optimisation problem library
impl_bonobench
multimodal
- []
artificial
OPL – Optimisation problem library
Ealain, https://doi.org/10.1145/3638530.3654299
impl_ealain
[]
real-world-like
>=1
@@ -719,7 +703,6 @@ OPL – Optimisation problem library
GNBG, https://arxiv.org/abs/2312.07083
impl_gnbg
[]
artificial
OPL – Optimisation problem library
GNBG-II, https://dl.acm.org/doi/pdf/10.1145/3712255.3734271
["impl_gnbg_ii", "impl_iohgnbg"]
[]
artificial
>=1
IOHGNBG | GNBG-II
+ GNBG-II | IOHGNBG
https://github.com/IOHprofiler/IOHGNBG | https://github.com/rohitsalgotra/GNBG-II IOHprofiler version of GNBG | Generalized Numerical Benchmark Generator version 2
+ https://github.com/rohitsalgotra/GNBG-II | https://github.com/IOHprofiler/IOHGNBG Generalized Numerical Benchmark Generator version 2 | IOHprofiler version of GNBG
OPL – Optimisation problem library
>=1
OPL – Optimisation problem library
IOHClustering, https://arxiv.org/pdf/2505.09233
impl_iohclustering
multimodal
- []
artificial-from-real-data
OPL – Optimisation problem library
MA-BBOB, https://doi.org/10.1145/3673908
["impl_iohexperimenter", "impl_ma_bbob"]
multimodal
- []
artificial
>=1
MA-BBOB (IOHexperimenter) | IOHexperimenter
+ IOHexperimenter | MA-BBOB (IOHexperimenter)
C++/Python
https://github.com/IOHprofiler/IOHexperimenter/blob/master/example/Competitions/MA-BBOB/Example_MABBOB.ipynb | https://github.com/IOHprofiler/IOHexperimenter Example notebook for MA-BBOB in IOHexperimenter | IOHprofiler experimenter framework
+ https://github.com/IOHprofiler/IOHexperimenter | https://github.com/IOHprofiler/IOHexperimenter/blob/master/example/Competitions/MA-BBOB/Example_MABBOB.ipynb IOHprofiler experimenter framework | Example notebook for MA-BBOB in IOHexperimenter
OPL – Optimisation problem library
MPM2 technical report TR15-01, https://ls11-www.cs.tu-dortmund.de/_media/techreports/tr15-01.pdf
impl_mpm2
multimodal
- []
OPL – Optimisation problem library
mUBQP benchmark, https://doi.org/10.1016/j.asoc.2013.11.008
impl_mocobench
["multimodal", "quadratic"]
- []
>=1
@@ -1006,7 +983,6 @@ OPL – Optimisation problem library
PUBOi, https://link.springer.com/chapter/10.1007/978-3-031-04148-8_12
impl_puboi
[]
artificial
>=1
@@ -1047,7 +1023,6 @@ OPL – Optimisation problem library
impl_randoptgen
multimodal
- milliseconds
artificial
>=1
@@ -1056,7 +1031,7 @@ OPL – Optimisation problem library
>=1
RandOptGen
Python
- {'milliseconds'}
+ milliseconds
https://github.com/MALEO-research-group/RandOptGen | https://doi.org/10.1145/3712256.3726478 Unified Random Problem Generator for Single- and Multi-Objective Optimization with Mixed-Variable Input Spaces
OPL – Optimisation problem library
On the design of multi-objective evolutionary algorithms based on NK-landscapes, https://doi.org/10.1016/j.ejor.2012.12.019
impl_mocobench
multimodal
- []
>=1
@@ -1129,7 +1103,6 @@ OPL – Optimisation problem library
On the impact of multi-objective scalability for the ρmTSP, https://doi.org/10.1007/978-3-319-45823-6_40
impl_mocobench
["multimodal", "quadratic"]
- []
OPL – Optimisation problem library
W-model, https://dl.acm.org/doi/abs/10.1145/3205651.3208240
impl_wmodel
[]
artificial
>=1
@@ -1214,7 +1186,6 @@ OPL – Optimisation problem library
>=1
>=1
>=1
@@ -1256,7 +1227,6 @@ OPL – Optimisation problem library
>=1
COCO framework
@@ -1297,7 +1267,6 @@ OPL – Optimisation problem library
2-40
COCO framework
@@ -1317,7 +1286,7 @@ OPL – Optimisation problem library
suite_bbob_biobj_mixint
BBOB-biobj-mixint
Suite
- integer | continuous
+ continuous | integer
10-320
2
OPL – Optimisation problem library
5-160
5-160
COCO framework
@@ -1379,7 +1347,6 @@ OPL – Optimisation problem library
2-40
COCO framework
@@ -1420,7 +1387,6 @@ OPL – Optimisation problem library
20-640
COCO framework
@@ -1440,7 +1406,7 @@ OPL – Optimisation problem library
suite_bbob_mixint
BBOB-mixint
Suite
- integer | continuous
+ continuous | integer
10-320
1
OPL – Optimisation problem library
5-160
5-160
COCO framework
@@ -1502,7 +1467,6 @@ OPL – Optimisation problem library
>=1
COCO legacy (bbob-noisy)
@@ -1525,7 +1489,7 @@ OPL – Optimisation problem library
continuous
>=1
[10, 2, 3, 4, 5, 6, 7, 8, 9]
- noisy
+ OPL – Optimisation problem library
>=1
OPL – Optimisation problem library
multimodal
real-world
OPL – Optimisation problem library
impl_car_structure
real-world
OPL – Optimisation problem library
continuous
>=1
[10, 2, 3, 4, 5, 6, 7, 8, 9]
- dynamic | noisy
+ unknown
>=1
unknown
@@ -1666,7 +1627,6 @@ OPL – Optimisation problem library
>=1
OPL – Optimisation problem library
["impl_cec2013", "impl_iohexperimenter"]
artificial
OPL – Optimisation problem library
0
OPL – Optimisation problem library
impl_pymoo
artificial
OPL – Optimisation problem library
["impl_cec2022", "impl_iohexperimenter"]
artificial
OPL – Optimisation problem library
impl_cfd
real-world
OPL – Optimisation problem library
CFD test problem suite
{'15m', '30s'}
+ 30s-15m
https://bitbucket.org/arahat/cfd-test-problem-suite Expensive real-world CFD-based test problems
OPL – Optimisation problem library
suite_cre
CRE
Suite
- integer | continuous
+ continuous | integer
6-14
[2, 3, 4, 5]
OPL – Optimisation problem library
impl_reproblems
real-world-like
OPL – Optimisation problem library
artificial
>=1
OPL – Optimisation problem library
>=3
1
unknown | box
+ box | unknown
>=2
OPL – Optimisation problem library
impl_pycutest
multimodal
artificial
>=1
OPL – Optimisation problem library
>=1
pymoo
@@ -2073,7 +2024,6 @@ OPL – Optimisation problem library
impl_iohexperimenter
artificial
>=1
OPL – Optimisation problem library
impl_emo2017
real-world
OPL – Optimisation problem library
25-10000
ETMOF
@@ -2178,11 +2126,11 @@ OPL – Optimisation problem library
suite_expobench
EXPObench
Suite
- continuous | categorical | integer
+ categorical | continuous | integer
30-405
1
noisy
- unknown | box
+ box | unknown
>=2
["observational", "real-life"]
@@ -2196,7 +2144,6 @@ OPL – Optimisation problem library
impl_expobench
real-world
10-135
@@ -2204,7 +2151,7 @@ OPL – Optimisation problem library
10-135
EXPObench
Python
- {'2 seconds', '80 seconds'}
+ 2 to 80 seconds
https://github.com/AlgTUDelft/ExpensiveOptimBenchmark EXPensive Optimization benchmark library (wind farm layout, gas filter design, pipe shape, hyperparameter tuning, hospital simulation)
OPL – Optimisation problem library
impl_gbea
multimodal
real-world
OPL – Optimisation problem library
coco-gbea
{'34 seconds', '5 seconds'}
+ 5s-35s
https://github.com/ttusar/coco-gbea Game-Benchmark for Evolutionary Algorithms (COCO fork)
OPL – Optimisation problem library
impl_gnbg
artificial
OPL – Optimisation problem library
["impl_gnbg_ii", "impl_iohgnbg"]
artificial
>=1
IOHGNBG | GNBG-II
+ GNBG-II | IOHGNBG
https://github.com/IOHprofiler/IOHGNBG | https://github.com/rohitsalgotra/GNBG-II IOHprofiler version of GNBG | Generalized Numerical Benchmark Generator version 2
+ https://github.com/rohitsalgotra/GNBG-II | https://github.com/IOHprofiler/IOHGNBG Generalized Numerical Benchmark Generator version 2 | IOHprofiler version of GNBG
OPL – Optimisation problem library
impl_iohclustering
multimodal
artificial-from-real-data
OPL – Optimisation problem library
impl_transfer_rf_bbob_rw
unimodal
real-world
OPL – Optimisation problem library
>=1
>=1
@@ -2486,7 +2427,6 @@ OPL – Optimisation problem library
>=1
OPL – Optimisation problem library
>=1
>=1
@@ -2568,7 +2507,6 @@ OPL – Optimisation problem library
>=1
OPL – Optimisation problem library
>=1
>=1
@@ -2632,7 +2569,7 @@ OPL – Optimisation problem library
continuous
>=1
[10, 2, 3, 4, 5, 6, 7, 8, 9]
- noisy
+ OPL – Optimisation problem library
>=1
OPL – Optimisation problem library
impl_mechbench
multimodal
real-world
OPL – Optimisation problem library
MECHBench
Python
- {'1 minute', '7 minutes'}
+ 1-7 minutes
https://github.com/BayesOptApp/MECHBench Structural mechanics design optimization benchmark
OPL – Optimisation problem library
>=1
mf2
@@ -2773,7 +2707,6 @@ OPL – Optimisation problem library
>=1
OPL – Optimisation problem library
>=1
OPL – Optimisation problem library
MMOPP
OPL – Optimisation problem library
["impl_modact", "impl_pymoo"]
real-world
20
20
- pymoo | modact
+ modact | pymoo
Python
- {'20ms'}
- https://github.com/anyoptimization/pymoo | https://github.com/epfl-lamd/modact Multi-objective optimization in Python | EPFL-LAMD modact package
+ 20ms
+ https://github.com/epfl-lamd/modact | https://github.com/anyoptimization/pymoo EPFL-LAMD modact package | Multi-objective optimization in Python
OPL – Optimisation problem library
unknown
0
2
- dynamic | noisy
+ unknown
>=1
unknown
@@ -2939,7 +2869,6 @@ OPL – Optimisation problem library
MOrepo
OPL – Optimisation problem library
impl_iohexperimenter
artificial
>=1
OPL – Optimisation problem library
impl_transfer_rf_bbob_rw
multimodal
real-world
OPL – Optimisation problem library
suite_re
RE
Suite
- integer | continuous
+ continuous | integer
4-14
[2, 3, 4, 5, 6, 7, 8, 9]
OPL – Optimisation problem library
impl_reproblems
real-world-like
OPL – Optimisation problem library
real-world
>=1
@@ -3142,7 +3067,6 @@ OPL – Optimisation problem library
>=1
IOHexperimenter
@@ -3165,7 +3089,7 @@ OPL – Optimisation problem library
continuous
>=1
[10, 2, 3, 4, 5, 6, 7, 8, 9]
- dynamic | noisy
+ dynamic
dynamic
@@ -3183,7 +3107,6 @@ OPL – Optimisation problem library
>=1
OPL – Optimisation problem library
impl_iohexperimenter
artificial
>=1
OPL – Optimisation problem library
continuous
>=1
1
- noisy | multi-fidelity
+ multi-fidelity | noisy
unknown
>=2
OPL – Optimisation problem library
impl_tulipa
unimodal
real-world
OPL – Optimisation problem library
TulipaEnergyModel.jl
Julia / JuMP
- {'minutes', 'hours'}
+ minutes to hours
https://tulipaenergy.github.io/TulipaEnergyModel.jl/stable/ | https://github.com/TulipaEnergy/Tulipa-OBZ-CaseStudy Large linear program for optimal investment and operation of energy systems
OPL – Optimisation problem library
impl_vehicle_dynamics
multimodal
real-world
OPL – Optimisation problem library
>=1
pymoo
@@ -3386,7 +3305,6 @@ OPL – Optimisation problem library
>=1
>=1
@@ -3405,7 +3323,7 @@ OPL – Optimisation problem library
ID Name Type Variable Types Total Variables Objectives Properties Constraint Types Total Constraints Dynamics Noise Partial Evaluations Independent Objectives Fidelity Levels Full Name Description Tags References Implementations Modality Evaluation Time Examples Source Binary Vars Categorical Vars Continuous Vars Integer Vars Implementation Names Implementation Languages Implementation Evaluation Times Implementation Links Implementation Descriptions Implementation Requirements Hard Box Constraints Soft Box Constraints Hard Linear Constraints Soft Linear Constraints Hard Function Constraints Soft Function Constraints ID Name Type Variable Types Total Variables Objectives Properties Constraint Types Total Constraints Dynamics Noise Partial Evaluations Independent Objectives Fidelity Levels Full Name Description Tags References Implementations Modality Examples Source Binary Vars Categorical Vars Continuous Vars Integer Vars Implementation Names Implementation Languages Implementation Evaluation Times Implementation Links Implementation Descriptions Implementation Requirements Hard Box Constraints Soft Box Constraints Hard Linear Constraints Soft Linear Constraints Hard Function Constraints Soft Function Constraints Hide all
- ID Name Type Variable Types Total Variables Objectives Properties Constraint Types Total Constraints Dynamics Noise Partial Evaluations Independent Objectives Fidelity Levels Full Name Description Tags References Implementations Modality Evaluation Time Examples Source Binary Vars Categorical Vars Continuous Vars Integer Vars Implementation Names Implementation Languages Implementation Evaluation Times Implementation Links Implementation Descriptions Implementation Requirements Hard Box Constraints Soft Box Constraints Hard Linear Constraints Soft Linear Constraints Hard Function Constraints Soft Function Constraints ID Name Type Variable Types Total Variables Objectives Properties Constraint Types Total Constraints Dynamics Noise Partial Evaluations Independent Objectives Fidelity Levels Full Name Description Tags References Implementations Modality Examples Source Binary Vars Categorical Vars Continuous Vars Integer Vars Implementation Names Implementation Languages Implementation Evaluation Times Implementation Links Implementation Descriptions Implementation Requirements Hard Box Constraints Soft Box Constraints Hard Linear Constraints Soft Linear Constraints Hard Function Constraints Soft Function Constraints
@@ -35,7 +35,6 @@
References
Implementations
Modality
- Evaluation Time
Examples
Source
Binary Vars
@@ -79,7 +78,6 @@
unimodal
real-world
>=2
2
unknown | box
+ box | unknown
>=2
Building spatial design, https://hdl.handle.net/1887/81789
impl_bso_toolbox
["1 second", "40 seconds"]
real-world
>=1
@@ -128,7 +125,7 @@
BSO-toolbox
C++
- {'40 seconds', '1 second'}
+ ~1s (smallest) to ~40s (larger)
https://github.com/TUe-excellent-buildings/BSO-toolbox Building Spatial Design toolbox (TU/e)
>=1
fn_emdo
Electric Motor Design Optimization
Problem
- integer | continuous
+ continuous | integer
26
1
noisy
- unknown | box
+ box | unknown
>=14
noisy
@@ -201,7 +197,6 @@
A Multi-Step Evaluation Process in Electric Motor Design, Tea Tušar; Peter Korošec; Bogdan Filipič, https://dis.ijs.si/tea/Publications/Tusar23Multistep.pdf
impl_emdo
multimodal
- 8 minutes
real-world
13
Electric Motor Design Optimization
Python
- {'8 minutes'}
+ 8 minutes
Not publicly available
real-world
fn_gasoline
Gasoline direct injection engine design
Problem
- integer | continuous
+ continuous | integer
14
2
multi-fidelity
@@ -283,7 +277,6 @@
Gasoline direct injection engine design, https://doi.org/10.1016/j.ejor.2022.08.032
impl_gasoline
[]
real-world
artificial
>=1
>=1
JPNSEC EC-Symposium 2019 competition, http://www.jpnsec.org/files/competition2019/EC-Symposium-2019-Competition-English.html
impl_jsec2019
3s
real-world
JSEC 2019 competition
{'3s'}
+ 3s
http://www.jpnsec.org/files/competition2019/EC-Symposium-2019-Competition-English.html JPNSEC EC-Symposium 2019 competition problem
artificial
>=1
artificial
>=1
Radar waveform design, https://doi.org/10.1007/978-3-540-70928-2_53
impl_radar_waveform
[]
real-world
BEACON, https://dl.acm.org/doi/10.1145/3712255.3734303
impl_beacon
multimodal
- negligible
artificial
BEACON
Python
- {'negligible'}
+ negligible
https://github.com/Stebbet/BEACON/ Continuous Bi-objective Benchmark with Explicit Adjustable COrrelatioN control
impl_bonobench
multimodal
- []
artificial
Ealain, https://doi.org/10.1145/3638530.3654299
impl_ealain
[]
real-world-like
>=1
@@ -693,7 +677,6 @@
GNBG, https://arxiv.org/abs/2312.07083
impl_gnbg
[]
artificial
GNBG-II, https://dl.acm.org/doi/pdf/10.1145/3712255.3734271
["impl_gnbg_ii", "impl_iohgnbg"]
[]
artificial
>=1
IOHGNBG | GNBG-II
+ GNBG-II | IOHGNBG
https://github.com/IOHprofiler/IOHGNBG | https://github.com/rohitsalgotra/GNBG-II IOHprofiler version of GNBG | Generalized Numerical Benchmark Generator version 2
+ https://github.com/rohitsalgotra/GNBG-II | https://github.com/IOHprofiler/IOHGNBG Generalized Numerical Benchmark Generator version 2 | IOHprofiler version of GNBG
>=1
IOHClustering, https://arxiv.org/pdf/2505.09233
impl_iohclustering
multimodal
- []
artificial-from-real-data
MA-BBOB, https://doi.org/10.1145/3673908
["impl_iohexperimenter", "impl_ma_bbob"]
multimodal
- []
artificial
>=1
MA-BBOB (IOHexperimenter) | IOHexperimenter
+ IOHexperimenter | MA-BBOB (IOHexperimenter)
C++/Python
https://github.com/IOHprofiler/IOHexperimenter/blob/master/example/Competitions/MA-BBOB/Example_MABBOB.ipynb | https://github.com/IOHprofiler/IOHexperimenter Example notebook for MA-BBOB in IOHexperimenter | IOHprofiler experimenter framework
+ https://github.com/IOHprofiler/IOHexperimenter | https://github.com/IOHprofiler/IOHexperimenter/blob/master/example/Competitions/MA-BBOB/Example_MABBOB.ipynb IOHprofiler experimenter framework | Example notebook for MA-BBOB in IOHexperimenter
MPM2 technical report TR15-01, https://ls11-www.cs.tu-dortmund.de/_media/techreports/tr15-01.pdf
impl_mpm2
multimodal
- []
mUBQP benchmark, https://doi.org/10.1016/j.asoc.2013.11.008
impl_mocobench
["multimodal", "quadratic"]
- []
>=1
@@ -980,7 +957,6 @@
PUBOi, https://link.springer.com/chapter/10.1007/978-3-031-04148-8_12
impl_puboi
[]
artificial
>=1
@@ -1021,7 +997,6 @@
impl_randoptgen
multimodal
- milliseconds
artificial
>=1
@@ -1030,7 +1005,7 @@
>=1
RandOptGen
Python
- {'milliseconds'}
+ milliseconds
https://github.com/MALEO-research-group/RandOptGen | https://doi.org/10.1145/3712256.3726478 Unified Random Problem Generator for Single- and Multi-Objective Optimization with Mixed-Variable Input Spaces
On the design of multi-objective evolutionary algorithms based on NK-landscapes, https://doi.org/10.1016/j.ejor.2012.12.019
impl_mocobench
multimodal
- []
>=1
@@ -1103,7 +1077,6 @@
On the impact of multi-objective scalability for the ρmTSP, https://doi.org/10.1007/978-3-319-45823-6_40
impl_mocobench
["multimodal", "quadratic"]
- []
W-model, https://dl.acm.org/doi/abs/10.1145/3205651.3208240
impl_wmodel
[]
artificial
>=1
@@ -1188,7 +1160,6 @@
>=1
>=1
>=1
@@ -1230,7 +1201,6 @@
>=1
COCO framework
@@ -1271,7 +1241,6 @@
2-40
COCO framework
@@ -1291,7 +1260,7 @@
suite_bbob_biobj_mixint
BBOB-biobj-mixint
Suite
- integer | continuous
+ continuous | integer
10-320
2
5-160
5-160
COCO framework
@@ -1353,7 +1321,6 @@
2-40
COCO framework
@@ -1394,7 +1361,6 @@
20-640
COCO framework
@@ -1414,7 +1380,7 @@
suite_bbob_mixint
BBOB-mixint
Suite
- integer | continuous
+ continuous | integer
10-320
1
5-160
5-160
COCO framework
@@ -1476,7 +1441,6 @@
>=1
COCO legacy (bbob-noisy)
@@ -1499,7 +1463,7 @@
continuous
>=1
[10, 2, 3, 4, 5, 6, 7, 8, 9]
- noisy
+ >=1
multimodal
real-world
impl_car_structure
real-world
continuous
>=1
[10, 2, 3, 4, 5, 6, 7, 8, 9]
- dynamic | noisy
+ unknown
>=1
unknown
@@ -1640,7 +1601,6 @@
>=1
["impl_cec2013", "impl_iohexperimenter"]
artificial
0
impl_pymoo
artificial
["impl_cec2022", "impl_iohexperimenter"]
artificial
impl_cfd
real-world
CFD test problem suite
{'15m', '30s'}
+ 30s-15m
https://bitbucket.org/arahat/cfd-test-problem-suite Expensive real-world CFD-based test problems
suite_cre
CRE
Suite
- integer | continuous
+ continuous | integer
6-14
[2, 3, 4, 5]
impl_reproblems
real-world-like
artificial
>=1
>=3
1
unknown | box
+ box | unknown
>=2
impl_pycutest
multimodal
artificial
>=1
>=1
pymoo
@@ -2047,7 +1998,6 @@
impl_iohexperimenter
artificial
>=1
impl_emo2017
real-world
25-10000
ETMOF
@@ -2152,11 +2100,11 @@
suite_expobench
EXPObench
Suite
- continuous | categorical | integer
+ categorical | continuous | integer
30-405
1
noisy
- unknown | box
+ box | unknown
>=2
["observational", "real-life"]
@@ -2170,7 +2118,6 @@
impl_expobench
real-world
10-135
@@ -2178,7 +2125,7 @@
10-135
EXPObench
Python
- {'2 seconds', '80 seconds'}
+ 2 to 80 seconds
https://github.com/AlgTUDelft/ExpensiveOptimBenchmark EXPensive Optimization benchmark library (wind farm layout, gas filter design, pipe shape, hyperparameter tuning, hospital simulation)
impl_gbea
multimodal
real-world
coco-gbea
{'34 seconds', '5 seconds'}
+ 5s-35s
https://github.com/ttusar/coco-gbea Game-Benchmark for Evolutionary Algorithms (COCO fork)
impl_gnbg
artificial
["impl_gnbg_ii", "impl_iohgnbg"]
artificial
>=1
IOHGNBG | GNBG-II
+ GNBG-II | IOHGNBG
https://github.com/IOHprofiler/IOHGNBG | https://github.com/rohitsalgotra/GNBG-II IOHprofiler version of GNBG | Generalized Numerical Benchmark Generator version 2
+ https://github.com/rohitsalgotra/GNBG-II | https://github.com/IOHprofiler/IOHGNBG Generalized Numerical Benchmark Generator version 2 | IOHprofiler version of GNBG
impl_iohclustering
multimodal
artificial-from-real-data
impl_transfer_rf_bbob_rw
unimodal
real-world
>=1
>=1
@@ -2460,7 +2401,6 @@
>=1
>=1
>=1
@@ -2542,7 +2481,6 @@
>=1
>=1
>=1
@@ -2606,7 +2543,7 @@
continuous
>=1
[10, 2, 3, 4, 5, 6, 7, 8, 9]
- noisy
+ >=1
impl_mechbench
multimodal
real-world
MECHBench
Python
- {'1 minute', '7 minutes'}
+ 1-7 minutes
https://github.com/BayesOptApp/MECHBench Structural mechanics design optimization benchmark
>=1
mf2
@@ -2747,7 +2681,6 @@
>=1
>=1
MMOPP
["impl_modact", "impl_pymoo"]
real-world
20
20
- pymoo | modact
+ modact | pymoo
Python
- {'20ms'}
- https://github.com/anyoptimization/pymoo | https://github.com/epfl-lamd/modact Multi-objective optimization in Python | EPFL-LAMD modact package
+ 20ms
+ https://github.com/epfl-lamd/modact | https://github.com/anyoptimization/pymoo EPFL-LAMD modact package | Multi-objective optimization in Python
unknown
0
2
- dynamic | noisy
+ unknown
>=1
unknown
@@ -2913,7 +2843,6 @@
MOrepo
impl_iohexperimenter
artificial
>=1
impl_transfer_rf_bbob_rw
multimodal
real-world
suite_re
RE
Suite
- integer | continuous
+ continuous | integer
4-14
[2, 3, 4, 5, 6, 7, 8, 9]
impl_reproblems
real-world-like
real-world
>=1
@@ -3116,7 +3041,6 @@
>=1
IOHexperimenter
@@ -3139,7 +3063,7 @@
continuous
>=1
[10, 2, 3, 4, 5, 6, 7, 8, 9]
- dynamic | noisy
+ dynamic
dynamic
@@ -3157,7 +3081,6 @@
>=1
impl_iohexperimenter
artificial
>=1
continuous
>=1
1
- noisy | multi-fidelity
+ multi-fidelity | noisy
unknown
>=2
impl_tulipa
unimodal
real-world
TulipaEnergyModel.jl
Julia / JuMP
- {'minutes', 'hours'}
+ minutes to hours
https://tulipaenergy.github.io/TulipaEnergyModel.jl/stable/ | https://github.com/TulipaEnergy/Tulipa-OBZ-CaseStudy Large linear program for optimal investment and operation of energy systems
impl_vehicle_dynamics
multimodal
real-world
>=1
pymoo
@@ -3360,7 +3279,6 @@
>=1
>=1
@@ -3379,7 +3297,7 @@
ID Name Type Variable Types Total Variables Objectives Properties Constraint Types Total Constraints Dynamics Noise Partial Evaluations Independent Objectives Fidelity Levels Full Name Description Tags References Implementations Modality Evaluation Time Examples Source Binary Vars Categorical Vars Continuous Vars Integer Vars Implementation Names Implementation Languages Implementation Evaluation Times Implementation Links Implementation Descriptions Implementation Requirements Hard Box Constraints Soft Box Constraints Hard Linear Constraints Soft Linear Constraints Hard Function Constraints Soft Function Constraints ID Name Type Variable Types Total Variables Objectives Properties Constraint Types Total Constraints Dynamics Noise Partial Evaluations Independent Objectives Fidelity Levels Full Name Description Tags References Implementations Modality Examples Source Binary Vars Categorical Vars Continuous Vars Integer Vars Implementation Names Implementation Languages Implementation Evaluation Times Implementation Links Implementation Descriptions Implementation Requirements Hard Box Constraints Soft Box Constraints Hard Linear Constraints Soft Linear Constraints Hard Function Constraints Soft Function Constraints