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dc.contributor.authorJun Zhang-
dc.date.accessioned2024-07-03T02:30:08Z-
dc.date.available2024-07-03T02:30:08Z-
dc.date.issued2021-08-16-
dc.identifier.citationIEEE TRANSACTIONS ON CYBERNETICS, v. 52, no 12, page. 13654-13668en_US
dc.identifier.issn2168-2267en_US
dc.identifier.issn2168-2275en_US
dc.identifier.urihttps://ieeexplore.ieee.org/document/9514364en_US
dc.identifier.urihttps://repository.hanyang.ac.kr/handle/20.500.11754/191122-
dc.description.abstractIt is known that many-objective optimization problems (MaOPs) often face the difficulty of maintaining good diversity and convergence in the search process due to the high-dimensional objective space. To address this issue, this article proposes a novel multiobjective framework for many-objective optimization (Mo4Ma), which transforms the many-objective space into multiobjective space. First, the many objectives are transformed into two indicative objectives of convergence and diversity. Second, a clustering-based sequential selection strategy is put forward in the transformed multiobjective space to guide the evolutionary search process. Specifically, the selection is circularly performed on the clustered subpopulations to maintain population diversity. In each round of selection, solutions with good performance in the transformed multiobjective space will be chosen to improve the overall convergence. The Mo4Ma is a generic framework that any type of evolutionary computation algorithm can incorporate compatibly. In this article, the differential evolution (DE) is adopted as the optimizer in the Mo4Ma framework, thus resulting in an Mo4Ma-DE algorithm. Experimental results show that the Mo4Ma-DE algorithm can obtain well-converged and widely distributed Pareto solutions along with the many-objective Pareto sets of the original MaOPs. Compared with seven state-of-the-art MaOP algorithms, the proposed Mo4Ma-DE algorithm shows strong competitiveness and general better performance.en_US
dc.languageen_USen_US
dc.publisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INCen_US
dc.relation.ispartofseriesv. 52, no 12;13654-13668-
dc.subjectClustering-based sequential selection (CSS)en_US
dc.subjectdifferential evolution (DE)en_US
dc.subjectmany-objective optimization problem (MaOP)en_US
dc.subjectmultiobjective frameworken_US
dc.titleA Multiobjective Framework for Many-Objective Optimizationen_US
dc.typeArticleen_US
dc.relation.no12-
dc.relation.volume52-
dc.identifier.doi10.1109/TCYB.2021.3082200en_US
dc.relation.page13654-13668-
dc.relation.journalIEEE TRANSACTIONS ON CYBERNETICS-
dc.contributor.googleauthorLiu, Si-Chen-
dc.contributor.googleauthorZhan, Zhi-Hui-
dc.contributor.googleauthorTan, Kay Chen-
dc.contributor.googleauthorZhang, Jun-
dc.relation.code2022044361-
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
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