中文版 | English
Title

Differential evolution guided by approximated Pareto set for multiobjective optimization

Author
Corresponding AuthorZhou,Aimin
Publication Years
2023-06-01
DOI
Source Title
ISSN
0020-0255
EISSN
1872-6291
Volume630Pages:669-687
Abstract
Differential evolution (DE), as an efficient evolutionary optimizer, has been widely applied to deal with multiobjective optimization problems. In DE generation operations, appropriate guiding solutions, the “best” solutions (denoted as x), will be in favor of the search for generating promising new trial solutions. However, it is still a challenge to define and select such x due to the Pareto property of multiobjective optimization. Facing this challenge, we propose a regularity model guided differential evolution (RMDE) for multiobjective optimization. Different from the existing studies that select x from non-dominated solutions or predefined preference solutions, the proposed RMDE aims to sample the guiding solutions from the regularity models that are built to approximate Pareto optimal set explicitly. In this way, four alternative RMDE mutation strategies with the sampled x are developed and investigated, including the search efficiency and parameter settings. Empirical studies are conducted to validate the performance of RMDE on 51 test instances. The experimental results demonstrate the advantages of the proposed method over seven other classical or newly developed algorithms from the literature.
Keywords
URL[Source Record]
Indexed By
Language
English
SUSTech Authorship
Others
Funding Project
Scientific and Technological Innovation 2030 Major Projects[2018AAA0100902] ; Science and Technology Commission of Shanghai Municipality[19511120601]
WOS Research Area
Computer Science
WOS Subject
Computer Science, Information Systems
WOS Accession No
WOS:000949422000001
Publisher
ESI Research Field
COMPUTER SCIENCE
Scopus EID
2-s2.0-85149058864
Data Source
Scopus
Citation statistics
Cited Times [WOS]:1
Document TypeJournal Article
Identifierhttp://kc.sustech.edu.cn/handle/2SGJ60CL/497225
DepartmentDepartment of Computer Science and Engineering
Affiliation
1.Shanghai Institute of AI for Education,School of Computer Science and Technology,East China Normal University,Shanghai,200062,China
2.Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation,Department of Computer Science and Engineering,Southern University of Science and Technology,Shenzhen,518055,China
Recommended Citation
GB/T 7714
Wang,Shuai,Zhou,Aimin,Li,Bingdong,et al. Differential evolution guided by approximated Pareto set for multiobjective optimization[J]. INFORMATION SCIENCES,2023,630:669-687.
APA
Wang,Shuai,Zhou,Aimin,Li,Bingdong,&Yang,Peng.(2023).Differential evolution guided by approximated Pareto set for multiobjective optimization.INFORMATION SCIENCES,630,669-687.
MLA
Wang,Shuai,et al."Differential evolution guided by approximated Pareto set for multiobjective optimization".INFORMATION SCIENCES 630(2023):669-687.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Export to Excel
Export to Csv
Altmetrics Score
Google Scholar
Similar articles in Google Scholar
[Wang,Shuai]'s Articles
[Zhou,Aimin]'s Articles
[Li,Bingdong]'s Articles
Baidu Scholar
Similar articles in Baidu Scholar
[Wang,Shuai]'s Articles
[Zhou,Aimin]'s Articles
[Li,Bingdong]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Wang,Shuai]'s Articles
[Zhou,Aimin]'s Articles
[Li,Bingdong]'s Articles
Terms of Use
No data!
Social Bookmark/Share
No comment.

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.