Fitness and Distance Based Local Search With Adaptive Differential Evolution for Multimodal Optimization Problems
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Wang, Zi-Jia | - |
dc.contributor.author | Zhan, Zhi-Hui | - |
dc.contributor.author | Li, Yun | - |
dc.contributor.author | Kwong, Sam | - |
dc.contributor.author | Jeon, Sang-Woon | - |
dc.contributor.author | Zhang, Jun | - |
dc.date.accessioned | 2023-07-27T12:06:41Z | - |
dc.date.available | 2023-07-27T12:06:41Z | - |
dc.date.created | 2023-06-16 | - |
dc.date.issued | 2023-06 | - |
dc.identifier.issn | 2471-285X | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/188219 | - |
dc.description.abstract | Local search has been regarded as a promising technique in multimodal algorithms to refine the accuracy of found multiple optima. However, how to execute the local search operations precisely on the found global optima and avoid the meaningless local search operations on local optima or found similar areas is still a challenge. In this paper, we propose a novel local search technique based on the individual information from two aspects, termed as fitness and distance based local search (FDLS). The fitness information can avoid the ineffective local search operations on the local optima, while the distance information can avoid the meaningless local search operations on the similar areas. These two kinds of information act in different roles and complement each other, which ensures that the local search is executed in different (ensured by distance information) and promising (ensured by fitness information) areas, leading to successful local search. Based on this, we design an adaptive DE (ADE) with adaptive parameters scheme and apply FDLS to ADE, termed as FDLS-ADE. Experimental results on the CEC2015 multimodal competition show the effectiveness and superiority of the FDLS-ADE, including comparisons with the winner of the CEC2015 multimodal competition. Furthermore, compared with other multimodal algorithms, the performance of the FDLS-ADE is seen relatively insensitive to niching parameters. Besides, experiments conducted also show that the FDLS can be applied to other multimodal algorithms easily and can further improve their performance. Finally, an application to a real-world nonlinear equations system further illustrates the applicability of the FDLS-ADE. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | - |
dc.title | Fitness and Distance Based Local Search With Adaptive Differential Evolution for Multimodal Optimization Problems | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Jeon, Sang-Woon | - |
dc.identifier.doi | 10.1109/TETCI.2023.3234575 | - |
dc.identifier.scopusid | 2-s2.0-85147278850 | - |
dc.identifier.wosid | 000994632000005 | - |
dc.identifier.bibliographicCitation | IEEE Transactions on Emerging Topics in Computational Intelligence, v.7, no.3, pp.684 - 699 | - |
dc.relation.isPartOf | IEEE Transactions on Emerging Topics in Computational Intelligence | - |
dc.citation.title | IEEE Transactions on Emerging Topics in Computational Intelligence | - |
dc.citation.volume | 7 | - |
dc.citation.number | 3 | - |
dc.citation.startPage | 684 | - |
dc.citation.endPage | 699 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.subject.keywordPlus | PARTICLE SWARM OPTIMIZATION | - |
dc.subject.keywordPlus | MULTIOBJECTIVE OPTIMIZATION | - |
dc.subject.keywordPlus | GLOBAL OPTIMIZATION | - |
dc.subject.keywordPlus | GENETIC ALGORITHM | - |
dc.subject.keywordPlus | FRAMEWORK | - |
dc.subject.keywordPlus | STRATEGY | - |
dc.subject.keywordAuthor | Iron | - |
dc.subject.keywordAuthor | Search problems | - |
dc.subject.keywordAuthor | Sociology | - |
dc.subject.keywordAuthor | Optimization | - |
dc.subject.keywordAuthor | Clustering algorithms | - |
dc.subject.keywordAuthor | Transforms | - |
dc.subject.keywordAuthor | Sensitivity | - |
dc.subject.keywordAuthor | Fitness and distance | - |
dc.subject.keywordAuthor | local search | - |
dc.subject.keywordAuthor | differential evolution | - |
dc.subject.keywordAuthor | multimodal optimization problems | - |
dc.subject.keywordAuthor | evolutionary computation | - |
dc.identifier.url | https://ieeexplore.ieee.org/document/10021865?arnumber=10021865&SID=EBSCO:edseee | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
222, Wangsimni-ro, Seongdong-gu, Seoul, 04763, Korea+82-2-2220-1365
COPYRIGHT © 2021 HANYANG UNIVERSITY.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.