A classification surrogate-assisted multi-objective evolutionary algorithm for expensive optimization

Surrogate-assisted multi-objective evolutionary algorithms (SAMOEAs) have been developed for solving expensive optimization problems. According to the roles that the surrogate models play in SAMOEAs, they can be divided into two categories: prediction-based and classification-based algorithms. Thoug...

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Bibliographic Details
Published in:Knowledge-based systems Vol. 242; p. 108416
Main Authors: Li, Jinglu, Wang, Peng, Dong, Huachao, Shen, Jiangtao, Chen, Caihua
Format: Journal Article
Language:English
Published: Amsterdam Elsevier B.V 22.04.2022
Elsevier Science Ltd
Subjects:
Algorithms
Classification
Classification tree
Competition
Competitiveness
Convergence
Evolutionary algorithms
Expensive multi-objective optimization
Genetic algorithms
Multiple objective analysis
Objectives
Optimization
Pareto dominance
Shape optimization
Surrogate-assisted
Surrogate-assisted
Expensive multi-objective optimization
Classification tree
Pareto dominance
ISSN:0950-7051, 1872-7409
Online Access:Get full text
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Summary:Surrogate-assisted multi-objective evolutionary algorithms (SAMOEAs) have been developed for solving expensive optimization problems. According to the roles that the surrogate models play in SAMOEAs, they can be divided into two categories: prediction-based and classification-based algorithms. Though prediction-based SAMOEAs are the mainstream methods, classification-based ones are gaining their fast developments. In this article, a classification surrogate-assisted multi-objective evolutionary algorithm (CSA-MOEA) is proposed for expensive optimization. The algorithm adopts a classification tree as the surrogate model to predict promising offsprings, which may be non-dominated solutions with good convergence. Then based on two effective infilling strategies, some of these promising individuals are added to the sample archive. By repeating the above steps iteratively, valuable solutions can be obtained. To evaluate the performance of CSA-MOEA, it is compared with several state-of-the-art surrogate-assisted evolutionary algorithms on three sets of multi-objective optimization test problems and an engineering shape optimization problem. The experimental results demonstrate the competitiveness of CSA-MOEA.
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ISSN:0950-7051
1872-7409
DOI:10.1016/j.knosys.2022.108416