Fuzzy-Adaptive Matrix-Based PSO with Group Learning
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Li, Mingyu | - |
dc.contributor.author | Sun, Peifa | - |
dc.contributor.author | Gao, Zhanyang | - |
dc.contributor.author | Zhao, Xiaoyan | - |
dc.contributor.author | Jin, Hu | - |
dc.date.accessioned | 2023-07-24T09:34:29Z | - |
dc.date.available | 2023-07-24T09:34:29Z | - |
dc.date.created | 2023-07-19 | - |
dc.date.issued | 2023-05 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/187384 | - |
dc.description.abstract | The particle swarm optimization (PSO) algorithm is a random search algorithm that simulates biological activities in nature and swarm intelligence. Traditional PSO algorithms still suffer from their inherent problems such as long computing time and premature convergence when dealing with a large number of decision variables. How to reduce the computing time while ensuring its accuracy is one of the main research issues in the literature. This paper proposes a PSO algorithm with matrix-based fuzzy adaptation and group learning. Firstly, to reduce the computing time, we propose a strategy to balance the global search ability and the local search ability by controlling the number of iterations adaptively. Second, to accelerate the convergence speed of the algorithm, we further propose a group learning mechanism. Along with the above two considerations, the proposed algorithm is able to achieve a promising balance between exploration and exploitation and, consequently, improves the performance. Through extensive comparison experiments on six classic functions, we show that our proposed PSO algorithm can achieve competitive or even better performance than several state-of-the-art methods. © 2023 IEEE. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | - |
dc.title | Fuzzy-Adaptive Matrix-Based PSO with Group Learning | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Jin, Hu | - |
dc.identifier.doi | 10.1109/ICACI58115.2023.10146137 | - |
dc.identifier.scopusid | 2-s2.0-85163303371 | - |
dc.identifier.bibliographicCitation | 2023 15th International Conference on Advanced Computational Intelligence, ICACI 2023, pp.1 - 7 | - |
dc.relation.isPartOf | 2023 15th International Conference on Advanced Computational Intelligence, ICACI 2023 | - |
dc.citation.title | 2023 15th International Conference on Advanced Computational Intelligence, ICACI 2023 | - |
dc.citation.startPage | 1 | - |
dc.citation.endPage | 7 | - |
dc.type.rims | ART | - |
dc.type.docType | Conference Paper | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordAuthor | fuzzy-weight | - |
dc.subject.keywordAuthor | group learning | - |
dc.subject.keywordAuthor | matrix-based | - |
dc.subject.keywordAuthor | Particle swarm optimization | - |
dc.identifier.url | https://ieeexplore.ieee.org/document/10146137?arnumber=10146137&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.