中文版 | English
Title

New Solution Creation Operator in MOEA/D for Faster Convergence

Author
Corresponding AuthorIshibuchi,Hisao
DOI
Publication Years
2022
Conference Name
17th International Conference on Parallel Problem Solving from Nature (PPSN)
ISSN
0302-9743
EISSN
1611-3349
ISBN
978-3-031-14720-3
Source Title
Volume
13399 LNCS
Pages
234-246
Conference Date
SEP 10-14, 2022
Conference Place
null,Dortmund,GERMANY
Publication Place
GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND
Publisher
Abstract
This paper introduces a novel solution generation strategy for MOEA/D. MOEA/D decomposes a multi/many-objective optimization problem into several single-objective sub-problems using a set of weight vectors and a scalarizing function. When a better solution is generated for one sub-problem, it is likely that a further better solution will appear in the improving direction. Examination of such a promising solution may improve the convergence speed of MOEA/D. Our idea is to use the improved directions in the current and previous populations to generate new solutions in addition to the standard genetic operators. To assess the usefulness of the proposed idea, we integrate it into MOEA/D-PBI and use a distance minimization problem to visually examine its behavior. Furthermore, the proposed idea is evaluated on some large-scale multi-objective optimization problems. It is demonstrated that the proposed idea drastically improves the convergence ability of MOEA/D.
Keywords
SUSTech Authorship
First ; Corresponding
Language
English
URL[Source Record]
Indexed By
Funding Project
National Natural Science Foundation of China[61876075] ; Guangdong Provincial Key Laboratory[2020B121201001] ; Program for Guangdong Introducing Innovative and Enterpreneurial Teams[2017ZT07X386] ; Stable Support Plan Program of Shenzhen Natural Science Fund[20200925174447003] ; Shenzhen Science and Technology Program[KQTD2016112514355531]
WOS Research Area
Computer Science
WOS Subject
Computer Science, Artificial Intelligence
WOS Accession No
WOS:000871753400017
EI Accession Number
20223712707327
ESI Classification Code
Optimization Techniques:921.5
Scopus EID
2-s2.0-85137266468
Data Source
Scopus
Citation statistics
Cited Times [WOS]:2
Document TypeConference paper
Identifierhttp://kc.sustech.edu.cn/handle/2SGJ60CL/401663
DepartmentDepartment of Computer Science and Engineering
Affiliation
Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation,Department of Computer Science and Engineering,Southern University of Science and Technology,Shenzhen,518055,China
First Author AffilicationDepartment of Computer Science and Engineering
Corresponding Author AffilicationDepartment of Computer Science and Engineering
First Author's First AffilicationDepartment of Computer Science and Engineering
Recommended Citation
GB/T 7714
Chen,Longcan,Pang,Lie Meng,Ishibuchi,Hisao. New Solution Creation Operator in MOEA/D for Faster Convergence[C]. GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND:SPRINGER INTERNATIONAL PUBLISHING AG,2022:234-246.
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