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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26210%2F24%3APU155644" target="_blank" >RIV/00216305:26210/24:PU155644 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/chapter/10.1007/978-3-031-70068-2_19" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-031-70068-2_19</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-70068-2_19" target="_blank" >10.1007/978-3-031-70068-2_19</a>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/GA24-12474S" target="_blank" >GA24-12474S: Benchmarking globálních optimalizačních metod</a><br>

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

18th International Conference on Parallel Problem Solving from Nature

ISBN

978-3-031-70068-2

ISSN

—

e-ISSN

—

Počet stran výsledku

19

Strana od-do

303-321

Název nakladatele

Springer Science and Business Media Deutschland GmbH

Místo vydání

neuveden

Místo konání akce

Hagenberg, Austria

Datum konání akce

14. 9. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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