Benchmarking optimization algorithms is a crucial aspect for their design and practical application. Since 2009, the Black Box Optimization Benchmarking Workshop has served as a place for discussing general recent advances in benchmarking practices and concrete results from benchmarking experiments with a large variety of (black box) optimizers.

The Comparing Continuous Optimizers platform (COCO, 1, https://coco-platform.org/) was developed in this context to support algorithm developers and practitioners alike by automating benchmarking experiments for black box optimization algorithms in single-
and bi-objective, unconstrained and constrained, continuous and mixed-integer problems in exact and noisy, as well as expensive and non-expensive scenarios.

We welcome *all contributions to black box optimization benchmarking* for the 2025 edition of the workshop, although we would like to put a particular emphasis on:

1) Benchmarking algorithms for problems with underexplored properties (for
example mixed integer, noisy, constrained, multiobjective, ...)
2) Reproducing previous benchmarking results as well as examining performance
improvements or degradations in algorithm implementations over time
(for example with the help of results from earlier BBOB submissions).

Submissions are not limited to the test suites provided by COCO. For convenience, the source code in various languages (C/C++, Matlab/Octave, Java, Python, and Rust) together with all data sets from previous BBOB contributions are provided as an automatized benchmarking pipeline to reduce the time spent for producing the results for:

- single-objective unconstrained problems (the "bbob" test suite)
- single-objective unconstrained problems with noise ("bbob-noisy")
- biobjective unconstrained problems ("bbob-biobj")
- large-scale single-objective problems ("bbob-largescale")
- mixed-integer single- and bi-objective problems ("bbob-mixint" and
"bbob-biobj-mixint")
- almost linearly constrained single-objective problems ("bbob-constrained")
- box-constrained problems ("sbox-cost")

We especially encourage submissions exploring algorithms from beyond the evolutionary computation community, as well as papers analyzing COCO’s extensive, publicly available algorithm datasets (see https://coco-platform.org/data-archive/).

For details, please see the separate BBOB-2025 web page at https://coco-platform.org/workshops/bbob2025.html

1 Nikolaus Hansen, Anne Auger, Raymond Ros, Olaf Mersmann, Tea Tušar,
and Dimo Brockhoff. "COCO: A platform for comparing continuous
optimizers in a black-box setting." Optimization Methods and
Software (2020): 1-31.