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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Veröffentlicht in:	Applied soft computing Jg. 163; S. 111857
Hauptverfasser:	Long, Wenyi, Wang, Peng, Dong, Huachao, Li, Jinglu, Fu, Chongbo
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier B.V 01.09.2024
Schlagworte:	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-Zugang:	Volltext
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