COCO postprocessed data archive

COCO (COmparing Continuous Optimizers) is a platform for systematic and sound comparisons of real-parameter global optimizers. Here, we provide postprocessed data from all 300+ officially supported algorithm data sets for the various available test suites. Due to the large amount of algorithms (and the limited space in the figures), we group algorithm data sets by year of publication.

bbob

bbob-noisy

bbob-biobj

bbob-largescale

bbob-mixint

bbob-constrained

24 functions
single-objective
continous domain
250+ algorithm data sets 		30 functions
noisy evaluations
single-objective
45 algorithm data sets 		55 functions
bi-objective
noiseless
30+ algorithm data sets 		24 functions
single-objective
dimensions 20 to 640
16 algorithm data sets 		24 functions
80% discrete variables
single-objective
5 algorithm data sets 		54 functions
from 9 "raw" bbob functions with
1 to (9 + ⌊ 9n/2 ⌋) non-linear constraints
9 algorithm data sets
2009
2010
2012
2013
2014
2015-CEC
2015-GECCO
2016
2017
2018
2019
2020
2021
2022
2023
 2009
2010
2012
2016
 2016
2017
2019
2021
2022
2023
 2019 2019 2022

The Python code to locally generate the first entry, bbob 2009, in the table (other entries work respectively) reads


import cocopp               # see https://pypi.org/project/cocopp
cocopp.main('bbob/2009/*')  # will take several minutes to process

Citation

You may cite this work in a scientific context as

N. Hansen, A. Auger, R. Ros, O. Mersmann, T. TuĊĦar, D. Brockhoff. COCO: A Platform for Comparing Continuous Optimizers in a Black-Box Setting, Optimization Methods and Software, 36(1), pp. 114-144, 2021. [pdf, arXiv]

    @ARTICLE{hansen2021coco,
    author = {Hansen, N. and Auger, A. and Ros, R. and Mersmann, O. and Tu{\v s}ar, T. and Brockhoff, D.},
    title = {{COCO}: A Platform for Comparing Continuous Optimizers in a Black-Box Setting},
    journal = {Optimization Methods and Software},
    doi = {https://doi.org/10.1080/10556788.2020.1808977},
    pages = {114--144},
    issue = {1},
    volume = {36},
    year = 2021
    }