An efficient surrogate-assisted hybrid optimization algorithm for expensive optimization problems

Surrogate-assisted evolutionary algorithms (SAEAs) are potential approaches to solve computationally expensive optimization problems. The critical idea of SAEAs is to combine the powerful searching capabilities of evolutionary algorithms with the predictive capabilities of surrogate models. In this...

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Veröffentlicht in:	Information sciences Jg. 561; S. 304 - 325
Hauptverfasser:	Pan, Jeng-Shyang, Liu, Nengxian, Chu, Shu-Chuan, Lai, Taotao
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Inc 01.06.2021
Schlagworte:	Differential evolution Expensive problems Hybrid optimization algorithm Surrogate-assisted Teaching-learning-based optimization Differential evolution Hybrid optimization algorithm Surrogate-assisted Teaching-learning-based optimization Expensive problems
ISSN:	0020-0255, 1872-6291
Online-Zugang:	Volltext
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