Publication detail
Performance Comparison of Surrogate-Assisted Evolutionary Algorithms on Computational Fluid Dynamics Problems
KŮDELA, J. DOBROVSKÝ, L.
English title
Performance Comparison of Surrogate-Assisted Evolutionary Algorithms on Computational Fluid Dynamics Problems
Type
conference paper
Language
en
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.
English 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 in English
Expensive optimization; evolutionary algorithm; surrogate model; computational fluid dynamics; benchmarking
Released
07.09.2024
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–to
303–321
Pages count
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",
month="September",
pages="303--321",
publisher="Springer Science and Business Media Deutschland GmbH",
isbn="978-3-031-70068-2"
}