17 0

Full metadata record

DC FieldValueLanguage
dc.contributor.authorJun Zhang-
dc.date.accessioned2024-04-16T23:28:56Z-
dc.date.available2024-04-16T23:28:56Z-
dc.date.issued2023-02-
dc.identifier.citationIEEE TRANSACTIONS ON CYBERNETICS, v. 53, NO 2, Page. 1000-1011en_US
dc.identifier.issn2168-2267en_US
dc.identifier.issn2168-2275en_US
dc.identifier.urihttps://information.hanyang.ac.kr/#/eds/detail?an=000842051500001&dbId=edswscen_US
dc.identifier.urihttps://repository.hanyang.ac.kr/handle/20.500.11754/189803-
dc.description.abstractAlthough cooperative coevolutionary algorithms are developed for large-scale dynamic optimization via subspace decomposition, they still face difficulties in reacting to environmental changes, in the presence of multiple peaks in the fitness functions and unevenness of subproblems. The resource allocation mechanisms among subproblems in the existing algorithms rely mainly on the fitness improvements already made but not potential ones. On the one hand, there is a lack of sufficient computing resources to achieve potential fitness improvements for some hard subproblems. On the other hand, the existing algorithms waste computing resources aiming to find most of the local optima of problems. In this article, we propose a cooperative particle swarm optimization algorithm to address these issues by introducing a bilevel balanceable resource allocation mechanism. A search strategy in the lower level is introduced to select some promising solutions from an archive based on solution diversity and quality to identify new peaks in every subproblem. A resource allocation strategy in the upper level is introduced to balance the coevolution of multiple subproblems by referring to their historical improvements and more computing resources are allocated for solving the subproblems that perform poorly but are expected to make great fitness improvements. Experimental results demonstrate that the proposed algorithm is competitive with the state-of-the-art algorithms in terms of objective function values and response efficiency with respect to environmental changes.en_US
dc.languageen_USen_US
dc.publisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INCen_US
dc.relation.ispartofseriesv. 53, NO 2;-
dc.subjectBalanced resource allocationen_US
dc.subjectcooperative coevolutionen_US
dc.subjectlarge-scale dynamic optimizationen_US
dc.subjectparticle swarm optimization (PSO)en_US
dc.titleCooperative Particle Swarm Optimization With a Bilevel Resource Allocation Mechanism for Large-Scale Dynamic Optimizationen_US
dc.typeArticleen_US
dc.relation.no2-
dc.relation.volume53-
dc.identifier.doi10.1109/TCYB.2022.3193888en_US
dc.relation.page1000-1011-
dc.relation.journalIEEE TRANSACTIONS ON CYBERNETICS-
dc.contributor.googleauthorLiu, Xiao-Fang-
dc.contributor.googleauthorZhang, Jun-
dc.contributor.googleauthorWang, Jun-
dc.relation.code2023036155-
dc.sector.campusE-
dc.sector.daehakCOLLEGE OF ENGINEERING SCIENCES[E]-
dc.sector.departmentSCHOOL OF ELECTRICAL ENGINEERING-
dc.identifier.pidjunzhanghk-
Appears in Collections:
COLLEGE OF ENGINEERING SCIENCES[E](공학대학) > ELECTRICAL ENGINEERING(전자공학부) > Articles
Files in This Item:
There are no files associated with this item.
Export
RIS (EndNote)
XLS (Excel)
XML


qrcode

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

BROWSE