Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Jun Zhang | - |
dc.date.accessioned | 2024-05-29T04:35:01Z | - |
dc.date.available | 2024-05-29T04:35:01Z | - |
dc.date.issued | 2023-07 | - |
dc.identifier.citation | APPLIED SOFT COMPUTING, v. 141, page. 110-113 | en_US |
dc.identifier.issn | 1568-4946 | en_US |
dc.identifier.uri | https://www.sciencedirect.com/science/article/pii/S1568494623003381 | en_US |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/190424 | - |
dc.description.abstract | Cooperative coevolutionary algorithms have been developed for large-scale dynamic optimization problems via divide-and-conquer mechanisms. Interacting decision variables are divided into the same subproblem for optimization. Their performance greatly depends on problem decomposition and response abilities to environmental changes. However, existing algorithms usually adopt offline decomposition and hence are insufficient to adapt to changes in the underlying interaction structure of decision variables. Quick online decomposition then becomes a crucial issue, along with solution reconstruction for new subproblems. This paper proposes incremental particle swarm optimization to address the two issues. In the proposed method, the incremental differential grouping obtains accurate groupings by iteratively performing edge contractions on the interaction graph of historical groups. A recombination-based sampling strategy is developed to generate high-quality solutions from historical solutions for new subproblems. In order to coordinate with the multimodal property of the problem, swarms are restarted after convergence to search for multiple high-quality solutions. Experimental results on problem instances up to 1000-D show the superiority of the proposed method to state-of -the-art algorithms in terms of solution optimality. The incremental differential grouping can obtain accurate groupings using less function evaluations.& COPY; 2023 Elsevier B.V. All rights reserved. | en_US |
dc.description.sponsorship | This work was supported in part by the National Natural Science Foundation of China (NSFC) under Grant 62103202, in part by the Natural Science Foundation of Tianjin under Grant 21JCQNJC00140. | en_US |
dc.language | en_US | en_US |
dc.publisher | ELSEVIER | en_US |
dc.relation.ispartofseries | v. 141;110-113 | - |
dc.subject | Dynamic optimization | en_US |
dc.subject | Particle swarm optimization | en_US |
dc.subject | Evolutionary computation | en_US |
dc.subject | Information reuse | en_US |
dc.title | Incremental particle swarm optimization for large-scale dynamic optimization with changing variable interactions | en_US |
dc.type | Article | en_US |
dc.relation.volume | 141 | - |
dc.identifier.doi | 10.1016/j.asoc.2023.110320 | en_US |
dc.relation.page | 110-113 | - |
dc.relation.journal | APPLIED SOFT COMPUTING | - |
dc.contributor.googleauthor | Liu, Xiao-Fang | - |
dc.contributor.googleauthor | Zhan, Zhi-Hui | - |
dc.contributor.googleauthor | Zhang, Jun | - |
dc.relation.code | 2023036234 | - |
dc.sector.campus | E | - |
dc.sector.daehak | COLLEGE OF ENGINEERING SCIENCES[E] | - |
dc.sector.department | SCHOOL OF ELECTRICAL ENGINEERING | - |
dc.identifier.pid | junzhanghk | - |
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