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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Bibliographic Details
Published in:	Soft computing (Berlin, Germany) Vol. 27; no. 15; pp. 10665 - 10686
Main Authors:	Wang, Wenxin, Dong, Huachao, Wang, Peng, Wang, Xinjing, Shen, Jiangtao
Format:	Journal Article
Language:	English
Published:	Berlin/Heidelberg Springer Berlin Heidelberg 01.08.2023 Springer Nature B.V
Subjects:	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
Online Access:	Get full text
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