An enhanced multi-objective evolutionary optimization algorithm with inverse model

Multi-objective evolutionary algorithm based on the inverse model (IM-MOEA) is a popular method to solve multi-objective optimization problems (MOPs). However, IM-MOEA has some drawbacks such as low accuracy and difficulty in dealing with MOPs with irregular PFs. To address these issues, adaptive re...

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Bibliographic Details
Published in:	Information sciences Vol. 530; pp. 128 - 147
Main Authors:	Zhang, Zhechen, Liu, Sanyang, Gao, Weifeng, Xu, Jingwei, Zhu, Shengqi
Format:	Journal Article
Language:	English
Published:	Elsevier Inc 01.08.2020
Subjects:	Adaptive reference vector Estimation of distribution algorithms (EDAs) Gaussian processes (GPs) Inverse modelling Multi-objective optimization Nonrandom grouping Nonrandom grouping Multi-objective optimization Adaptive reference vector Inverse modelling Gaussian processes (GPs) Estimation of distribution algorithms (EDAs)
ISSN:	0020-0255, 1872-6291
Online Access:	Get full text
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Summary:	Multi-objective evolutionary algorithm based on the inverse model (IM-MOEA) is a popular method to solve multi-objective optimization problems (MOPs). However, IM-MOEA has some drawbacks such as low accuracy and difficulty in dealing with MOPs with irregular PFs. To address these issues, adaptive reference vector mechanism and nonrandom grouping strategy are employed in IM-MOEA, which enhances the reliability of the inverse model. In addition, a modified selection mechanism is used to choose candidate solutions. Further, an enhanced IM-MOEA with adaptive reference vectors and nonrandom grouping (AN-IMMOEA) is proposed in this paper. The experimental results on 27 MOPs indicate that the proposed method has a better performance than other MOEAs.
ISSN:	0020-0255 1872-6291
DOI:	10.1016/j.ins.2020.03.111