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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Vydáno v:Information sciences Ročník 530; s. 128 - 147
Hlavní autoři: Zhang, Zhechen, Liu, Sanyang, Gao, Weifeng, Xu, Jingwei, Zhu, Shengqi
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Inc 01.08.2020
Témata:
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
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Shrnutí: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