Detail publikace
Performance Comparison of Surrogate-Assisted Evolutionary Algorithms on Computational Fluid Dynamics Problems
KŮDELA, J. DOBROVSKÝ, L.
Anglický název
Performance Comparison of Surrogate-Assisted Evolutionary Algorithms on Computational Fluid Dynamics Problems
Typ
článek ve sborníku ve WoS nebo Scopus
Jazyk
en
Originální abstrakt
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.
Anglický abstrakt
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.
Klíčová slova anglicky
Expensive optimization; evolutionary algorithm; surrogate model; computational fluid dynamics; benchmarking
Vydáno
07.09.2024
Nakladatel
Springer Science and Business Media Deutschland GmbH
ISBN
978-3-031-70068-2
Kniha
18th International Conference on Parallel Problem Solving from Nature
Strany od–do
303–321
Počet stran
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"
}