A Surrogate-Assisted Constrained Optimization Evolutionary Algorithm by Searching Multiple Kinds of Global and Local Regions

This article proposes a surrogate-assisted evolutionary algorithm to tackle expensive inequality-constrained optimization problems through global exploration and local exploitation. The algorithm begins with an exploration stage that involves sampling in three kinds of global regions: 1) the feasibl...

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Vydané v:	IEEE transactions on evolutionary computation Ročník 29; číslo 1; s. 61 - 75
Hlavní autori:	Zeng, Yong, Cheng, Yuansheng, Liu, Jun
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	IEEE 01.02.2025
Predmet:	Differential evolution (DE) Evolutionary computation expensive constrained optimization global and local search Linear programming Optimization Search problems Sociology Statistics surrogate assisted evolutionary algorithm Training
ISSN:	1089-778X, 1941-0026
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