| Title | New Solution Creation Operator in MOEA/D for Faster Convergence |
| Author | |
| Corresponding Author | Ishibuchi，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 Type | Conference paper |
| Identifier | http://kc.sustech.edu.cn/handle/2SGJ60CL/401663 |
| Department | Department 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 Affilication | Department of Computer Science and Engineering |
| Corresponding Author Affilication | Department of Computer Science and Engineering |
| First Author's First Affilication | Department 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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