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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Bibliographic Details

Published in:	IEEE transactions on evolutionary computation Vol. 29; no. 1; pp. 61 - 75
Main Authors:	Zeng, Yong, Cheng, Yuansheng, Liu, Jun
Format:	Journal Article
Language:	English
Published:	IEEE 01.02.2025
Subjects:	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
Online Access:	Get full text
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