A data-driven co-evolutionary exploration algorithm for computationally expensive constrained multi-objective problems

Surrogate-assisted multi-objective optimization algorithms have attracted widespread attention due to their outstanding performance in computationally expensive real-world problems. However, there is relatively little research about multi-objective optimization with complex and expensive constraints...

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Bibliographic Details

Published in:	Applied soft computing Vol. 163; p. 111857
Main Authors:	Long, Wenyi, Wang, Peng, Dong, Huachao, Li, Jinglu, Fu, Chongbo
Format:	Journal Article
Language:	English
Published:	Elsevier B.V 01.09.2024
Subjects:	Co-evolutionary exploration Computationally expensive Constrained multi-objective Global optimization Reference vector Surrogate model Global optimization Constrained multi-objective Surrogate model Co-evolutionary exploration Reference vector Computationally expensive
ISSN:	1568-4946
Online Access:	Get full text
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