Differential Evolution Enhanced by Combining Group Learning and Elite Learning
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Shen, Guang-Xu | - |
dc.contributor.author | Li, Jian-Yu | - |
dc.contributor.author | Sun, Pei-Fa | - |
dc.contributor.author | Jeon, Sang-Woon | - |
dc.contributor.author | Jin, Hu | - |
dc.date.accessioned | 2024-04-09T03:00:51Z | - |
dc.date.available | 2024-04-09T03:00:51Z | - |
dc.date.issued | 2023-10 | - |
dc.identifier.issn | 2162-1233 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/118487 | - |
dc.description.abstract | Differential evolution (DE) is fully validated as a feasible algorithm for solving optimization problems. Additionally, for the complex optimization problems with high dimension, the traditional DE suffers from slow convergence. This paper proposes an enhanced DE algorithm that combines group learning and elite learning. The proposed algorithm improves the global search capability while guaranteeing a certain convergence speed. Through extensive experiments we confirm the superior competitiveness of the proposed DE algorithm compared to the traditional ones. © 2023 IEEE. | - |
dc.format.extent | 3 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | IEEE Computer Society | - |
dc.title | Differential Evolution Enhanced by Combining Group Learning and Elite Learning | - |
dc.type | Article | - |
dc.publisher.location | 미국 | - |
dc.identifier.doi | 10.1109/ICTC58733.2023.10392867 | - |
dc.identifier.scopusid | 2-s2.0-85184595723 | - |
dc.identifier.bibliographicCitation | 2023 14th International Conference on Information and Communication Technology Convergence (ICTC), pp 921 - 923 | - |
dc.citation.title | 2023 14th International Conference on Information and Communication Technology Convergence (ICTC) | - |
dc.citation.startPage | 921 | - |
dc.citation.endPage | 923 | - |
dc.type.docType | Conference Paper | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordAuthor | Differential evolution | - |
dc.subject.keywordAuthor | elite learning | - |
dc.subject.keywordAuthor | group learning | - |
dc.subject.keywordAuthor | mutation strategy | - |
dc.subject.keywordAuthor | optimization | - |
dc.identifier.url | https://ieeexplore.ieee.org/document/10392867 | - |
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.