A clustering-based surrogate-assisted evolutionary algorithm (CSMOEA) for expensive multi-objective optimization

This paper presents a novel surrogate-assisted evolutionary algorithm, CSMOEA, for multi-objective optimization problems (MOPs) with computationally expensive objectives. Considering most surrogate-assisted evolutionary algorithms (SAEAs) do not make full use of population information and only use p...

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Vydáno v:	Soft computing (Berlin, Germany) Ročník 27; číslo 15; s. 10665 - 10686
Hlavní autoři:	Wang, Wenxin, Dong, Huachao, Wang, Peng, Wang, Xinjing, Shen, Jiangtao
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Berlin/Heidelberg Springer Berlin Heidelberg 01.08.2023 Springer Nature B.V
Témata:	Approximation Artificial Intelligence Classification Clustering Computational Intelligence Control Effectiveness Engineering Evolutionary algorithms Genetic algorithms Mathematical Logic and Foundations Mechatronics Multiple objective analysis Neural networks Optimization Radial basis function Robotics Shape optimization Underwater vehicles Radial basis function Expensive multi-objective optimization Bi-level sampling strategy Clustering algorithm
ISSN:	1432-7643, 1433-7479
On-line přístup:	Získat plný text
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