Publication detail

Performance Comparison of Surrogate-Assisted Evolutionary Algorithms on Computational Fluid Dynamics Problems

KŮDELA, J. DOBROVSKÝ, L.

Original Title

Performance Comparison of Surrogate-Assisted Evolutionary Algorithms on Computational Fluid Dynamics Problems

Type

conference paper

Language

English

Original Abstract

Surrogate-assisted evolutionary algorithms (SAEAs) are recently among the most widely studied methods for their capability to solve expensive real-world optimization problems. However, the development of new methods and benchmarking with other techniques still relies almost exclusively on artificially created problems. In this paper, we use two real-world computational fluid dynamics problems to compare the performance of eleven state-of-the-art single-objective SAEAs. We analyze the performance by investigating the quality and robustness of the obtained solutions and the convergence properties of the selected methods. Our findings suggest that the more recently published methods, as well as the techniques that utilize differential evolution as one of their optimization mechanisms, perform significantly better than the other considered methods.

Keywords

Expensive optimization; evolutionary algorithm; surrogate model; computational fluid dynamics; benchmarking

Authors

KŮDELA, J.; DOBROVSKÝ, L.

Released

7. 9. 2024

Publisher

Springer Science and Business Media Deutschland GmbH

ISBN

978-3-031-70068-2

Book

18th International Conference on Parallel Problem Solving from Nature

Pages from

303

Pages to

321

Pages count

19

URL

https://link.springer.com/chapter/10.1007/978-3-031-70068-2_19

BibTex

@inproceedings{BUT196901,
  author="Jakub {Kůdela} and Ladislav {Dobrovský}",
  title="Performance Comparison of Surrogate-Assisted Evolutionary Algorithms on Computational Fluid Dynamics Problems",
  booktitle="18th International Conference on Parallel Problem Solving from Nature",
  year="2024",
  pages="303--321",
  publisher="Springer Science and Business Media Deutschland GmbH",
  doi="10.1007/978-3-031-70068-2\{_}19",
  isbn="978-3-031-70068-2",
  url="https://link.springer.com/chapter/10.1007/978-3-031-70068-2_19"
}