Algorithm data sets for the bbob-constrained test suite

In the table below, you will find all official algorithm data sets on the bbob-constrained 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.

Note that a manual download of the data is not necessary if you use the displaying results module (cocopp) of COCO.

Algorithm	Year	Author(s)	Dataset	Comment
RandomSearch-5	2022	Dufossé	tgz	source code
AL1-CMA-ES	2022	Dufossé and Atamna	tgz	CMA-ES with Augmented Lagrangian fitness, pycma version, parameter setting 1 as compared by Dufosse and Atamna for BBOB-2022
AL2-CMA-ES	2022	Dufossé and Atamna	tgz	CMA-ES with Augmented Lagrangian fitness, pycma version, parameter setting 2 as compared by Dufosse and Atamna for BBOB-2022
AL3-CMA-ES	2022	Dufossé and Atamna	tgz	CMA-ES with Augmented Lagrangian fitness, pycma version, parameter setting 3 (default) as compared by Dufosse and Atamna for BBOB-2022
AL4-CMA-ES	2022	Dufossé and Atamna	tgz	CMA-ES with Augmented Lagrangian fitness, pycma version, parameter setting 4 as compared by Dufosse and Atamna for BBOB-2022
BPepsMAg	2022	Hellwig and Beyer	tgz	Matrix Adaptation Evolution Strategy with restarts and BIPOP strategy, using up to three different constraint handling techniques, as compared by Hellwig and Beyer for BBOB-2022
COBYLA	2022	Dufossé and Atamna	tgz	Constrained Optimization BY Linear Approximation (implemented in SciPy as a wrapper around Powell’s fortran code) compared by Dufosse and Atamna for BBOB-2022
epsMAg	2022	Hellwig and Beyer	tgz	Matrix Adaptation Evolution Strategy, using up to three different constraint handling techniques, as compared by Hellwig and Beyer for BBOB-2022
fmincon	2022	Hellwig and Beyer	tgz	default fmincon from Matlab2021b, as compared by Hellwig and Beyer for BBOB-2022