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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Vydané v:Soft computing (Berlin, Germany) Ročník 27; číslo 15; s. 10665 - 10686
Hlavní autori: Wang, Wenxin, Dong, Huachao, Wang, Peng, Wang, Xinjing, Shen, Jiangtao
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Berlin/Heidelberg Springer Berlin Heidelberg 01.08.2023
Springer Nature B.V
Predmet:
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
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Shrnutí: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 population information in either the objective space or the design space independently, to address this limitation, we propose a new strategy for comprehensive utilization of population information of objective and design space. The proposed CSMOEA adopts an adaptive clustering strategy to divide the current population into good and bad groups, and the clustering centers in the design space are obtained, respectively. Then, a bi-level sampling strategy is proposed to select the best samples in both the design and objective space, using distance to the clustering centers and approximated objective values of radial basis functions. The effectiveness of CSMOEA is compared with five state-of-the-art algorithms on 21 widely used benchmark problems, and the results show high efficiency and a good balance between convergence and diversity. Additionally, CSMOEA is applied to the shape optimization of blend-wing-body underwater gliders with 14 decision variables and two objectives, demonstrating its effectiveness in solving real-world engineering problems.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-023-08227-4