A Probabilistic Niching Evolutionary Computation Framework Based on Binary Space Partitioning
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Huang, Ting | - |
dc.contributor.author | Gong, Yue-Jiao | - |
dc.contributor.author | Chen, Wei-Neng | - |
dc.contributor.author | Wang, Hua | - |
dc.contributor.author | Zhang, Jun | - |
dc.date.accessioned | 2023-11-24T02:38:26Z | - |
dc.date.available | 2023-11-24T02:38:26Z | - |
dc.date.issued | 2022-01 | - |
dc.identifier.issn | 2168-2267 | - |
dc.identifier.issn | 2168-2275 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/115758 | - |
dc.description.abstract | Multimodal optimization problems have multiple satisfactory solutions to identify. Most of the existing works conduct the search based on the information of the current population, which can be inefficient. This article proposes a probabilistic niching evolutionary computation framework that guides the future search based on more sufficient historical information, in order to locate diverse and high-quality solutions. A binary space partition tree is built to structurally organize the space visiting information. Based on the tree, a probabilistic niching strategy is defined to reinforce exploration and exploitation by making full use of the structural historical information. The proposed framework is universal for incorporating various baseline niching algorithms. In this article, we integrate the proposed framework with two niching algorithms: 1) a distance-based differential evolution algorithm and 2) a topology-based particle swarm optimization algorithm. The two new algorithms are evaluated on 20 multimodal optimization test functions. The experimental results show that the proposed framework helps the algorithms obtain competitive performance. They outperform a number of state-of-the-art niching algorithms on most of the test functions. © 2013 IEEE. | - |
dc.format.extent | 14 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | IEEE Advancing Technology for Humanity | - |
dc.title | A Probabilistic Niching Evolutionary Computation Framework Based on Binary Space Partitioning | - |
dc.type | Article | - |
dc.publisher.location | 미국 | - |
dc.identifier.doi | 10.1109/TCYB.2020.2972907 | - |
dc.identifier.scopusid | 2-s2.0-85123648754 | - |
dc.identifier.wosid | 000742182700009 | - |
dc.identifier.bibliographicCitation | IEEE Transactions on Cybernetics, v.52, no.1, pp 51 - 64 | - |
dc.citation.title | IEEE Transactions on Cybernetics | - |
dc.citation.volume | 52 | - |
dc.citation.number | 1 | - |
dc.citation.startPage | 51 | - |
dc.citation.endPage | 64 | - |
dc.type.docType | 정기학술지(Article(Perspective Article포함)) | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Automation & Control Systems | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalWebOfScienceCategory | Automation & Control Systems | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Cybernetics | - |
dc.subject.keywordPlus | DIFFERENTIAL EVOLUTION | - |
dc.subject.keywordPlus | MULTIMODAL OPTIMIZATION | - |
dc.subject.keywordPlus | GLOBAL OPTIMIZATION | - |
dc.subject.keywordPlus | TRUSS-STRUCTURES | - |
dc.subject.keywordPlus | ALGORITHM | - |
dc.subject.keywordPlus | DESIGN | - |
dc.subject.keywordAuthor | Binary space partition (BSP) | - |
dc.subject.keywordAuthor | evolutionary algorithm (EA) | - |
dc.subject.keywordAuthor | multimodal optimization | - |
dc.subject.keywordAuthor | probabilistic niching computation | - |
dc.identifier.url | https://ieeexplore.ieee.org/document/9032378?arnumber=9032378&SID=EBSCO:edseee | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
55 Hanyangdeahak-ro, Sangnok-gu, Ansan, Gyeonggi-do, 15588, Korea+82-31-400-4269 sweetbrain@hanyang.ac.kr
COPYRIGHT © 2021 HANYANG UNIVERSITY. ALL RIGHTS RESERVED.
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.