Algorithm data sets for the bbob-biobj test suite
In the table below, you will find all official algorithm data sets on the bbob-biobj test suite, together with their year of publication, the authors, and related PDFs for each data set. Links to the source code to run the corresponding experiments/algorithms are provided whenever available.
To sort the table, simply click on the table header of the corresponding column.
| Number | Algorithm Name | Year | Author(s) | link to data | related PDFs, source code, and remarks |
|---|---|---|---|---|---|
| biobj-000 | DEMO | 2016 | Tušar and Filipič | data | doi |
| biobj-001 | HMO-CMA-ES | 2016 | Loshchilov and Glasmachers | data | |
| biobj-002 | MAT-DIRECT | 2016 | Al-Dujaili and Sundaram | data | doi |
| biobj-003 | MAT-SMS | 2016 | Al-Dujaili and Sundaram | data | doi |
| biobj-004 | MO-DIRECT-HV-Rank | 2016 | Wong et al. | data | |
| biobj-005 | MO-DIRECT-ND | 2016 | Wong et al. | data | |
| biobj-006 | MO-DIRECT-Rank | 2016 | Wong et al. | data | |
| biobj-007 | NSGA-II-MATLAB | 2016 | Auger et al. | data | |
| biobj-008 | RANDOMSEARCH-4 | 2016 | Auger et al. | data | |
| biobj-009 | RANDOMSEARCH-5 | 2016 | Auger et al. | data | pdf, comparison pdf |
| biobj-010 | RANDOMSEARCH-100 | 2016 | Auger et al. | data | |
| biobj-011 | RM-MEDA | 2016 | Auger et al. | data | |
| biobj-012 | SMS-EMOA-DE | 2016 | Auger et al. | data | |
| biobj-013 | SMS-EMOA-PM | 2016 | Auger et al. | data | |
| biobj-014 | UP-MO-CMA-ES | 2016 | Krause et al. | data | |
| biobj-015 | SMS-EMOA-SA | 2017 | Wessing | data | doi, link to paper |
| biobj-016 | COMO-1e3 | 2019 | Dufossé and Touré | data | BBOB-2019 paper |
| biobj-017 | COMO-3 | 2019 | Dufossé and Touré | data | BBOB-2019 paper |
| biobj-018 | COMO-10 | 2019 | Dufossé and Touré | data | BBOB-2019 paper |
| biobj-019 | COMO-32 | 2019 | Dufossé and Touré | data | BBOB-2019 paper |
| biobj-020 | COMO-100 | 2019 | Dufossé and Touré | data | BBOB-2019 paper |
| biobj-021 | COMO-316 | 2019 | Dufossé and Touré | data | BBOB-2019 paper |
| biobj-022 | GDE3-platypus | 2019 | Brockhoff and Tušar | data | BBOB-2019 paper |
| biobj-023 | IBEA-platypus | 2019 | Brockhoff and Tušar | data | BBOB-2019 paper |
| biobj-024 | MO-CMA-ES-10 | 2019 | Dufossé and Touré | data | BBOB-2019 paper |
| biobj-025 | MO-CMA-ES-32 | 2019 | Dufossé and Touré | data | BBOB-2019 paper |
| biobj-026 | MO-CMA-ES-100 | 2019 | Dufossé and Touré | data | BBOB-2019 paper |
| biobj-027 | MOEAD-platypus | 2019 | Brockhoff and Tušar | data | BBOB-2019 paper |
| biobj-028 | N-III-11-platypus | 2019 | Brockhoff and Tušar | data | BBOB-2019 paper |
| biobj-029 | N-III-111-platypus | 2019 | Brockhoff and Tušar | data | BBOB-2019 paper |
| biobj-030 | NSGA-II-platypus | 2019 | Brockhoff and Tušar | data | BBOB-2019 paper |
| biobj-031 | SPEA2-platypus | 2019 | Brockhoff and Tušar | data | BBOB-2019 paper |
| biobj-032 | DMS | 2021 | Brockhoff et al. | data | BBOB-2021 paper |
| biobj-033 | MultiGLODS | 2021 | Brockhoff et al. | data | BBOB-2021 paper |
| biobj-034 | K-RVEA | 2022 | Tanabe et al. | data | continuous submission: Kriging-assisted reference vector guided evolutionary algorithm GECCO-2022 paper |
| biobj-035 | MOTPE | 2022 | Tanabe et al. | data | continuous submission: optuna implementation of the Multiobjective Tree Parzen Estimator GECCO-2022 paper |
| biobj-036 | TPB | 2022 | Tanabe et al. | data | continuous submission: two-phase algorithm with a Bézier simplex-based interpolation method GECCO-2022 paper |
| biobj-037 | Borg-adaptive | 2023 | Brockhoff et al. | data | Borg-MOEA with adaptive epsilon |
| biobj-038 | Borg-eps-1e-4 | 2023 | Brockhoff et al. | data | Borg-MOEA with fixed epsilon (= 10**(-4)) |