Enhancing MOEA/D with Escape Mechanisms
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Derbel, Bilel | - |
dc.contributor.author | Pruvost, Geoffrey | - |
dc.contributor.author | Hong, Byung-Woo | - |
dc.date.accessioned | 2021-12-02T07:40:08Z | - |
dc.date.available | 2021-12-02T07:40:08Z | - |
dc.date.issued | 2021-08 | - |
dc.identifier.issn | 0000-0000 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/52197 | - |
dc.description.abstract | In this paper, we investigate the design of escape mechanisms within the state-of-the-art decomposition-based evolutionary multi-objective MOEA/D framework. We propose to track the number of improvements made with respect to the single-objective sub-problems defined by decomposition. This allows us to compute an estimated sub-problem improvement probability which serves as an activation signal for some solution perturbation mechanism to occur. We report the benefits of such an approach by conducting a comprehensive experimental analysis on a broad range of combinatorial bi-objective bit-string landscapes with variable dimensions and ruggedness. Our empirical findings provide evidence on the effectiveness of the proposed escape mechanism and its ability in providing substantial improvement over conventional MOEA/D. Besides, we provide a detailed analysis of parameters impact and anytime behavior in order to better highlight the strength of the proposed techniques as a function of available budget and problem characteristics. | - |
dc.format.extent | 8 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | IEEE | - |
dc.title | Enhancing MOEA/D with Escape Mechanisms | - |
dc.type | Article | - |
dc.identifier.doi | 10.1109/CEC45853.2021.9504957 | - |
dc.identifier.bibliographicCitation | 2021 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC 2021), pp 1163 - 1170 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.wosid | 000703866100147 | - |
dc.identifier.scopusid | 2-s2.0-85124593578 | - |
dc.citation.endPage | 1170 | - |
dc.citation.startPage | 1163 | - |
dc.citation.title | 2021 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC 2021) | - |
dc.type.docType | Proceedings Paper | - |
dc.publisher.location | 미국 | - |
dc.subject.keywordAuthor | Multi-objective optimization | - |
dc.subject.keywordAuthor | decomposition | - |
dc.subject.keywordPlus | ALGORITHM | - |
dc.subject.keywordPlus | SEARCH | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Mathematical & Computational Biology | - |
dc.relation.journalResearchArea | Operations Research & Management Science | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Theory & Methods | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Mathematical & Computational Biology | - |
dc.relation.journalWebOfScienceCategory | Operations Research & Management Science | - |
dc.description.journalRegisteredClass | other | - |
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