A decomposition-based evolutionary algorithm with clustering and hierarchical estimation for multi-objective fuzzy flexible jobshop scheduling

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dc.contributor.authorZhang, Xuwei
dc.contributor.authorLiu, Shixin
dc.contributor.authorZhao, Ziyan
dc.contributor.authorYang, Shengxiang
dc.date.acceptance2024-01-20
dc.date.accessioned2024-05-02T12:53:49Z
dc.date.available2024-05-02T12:53:49Z
dc.date.issued2024-01-26
dc.descriptionThe file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.
dc.description.abstractAs an effective approximation algorithm for multi-objective jobshop scheduling, multi-objective evolutionary algorithms (MOEAs) have received extensive attention. However, maintaining a balance between the diversity and convergence of non-dominated solutions while ensuring overall convergence is an open problem in the context of solving Multi-objective Fuzzy Flexible Jobshop Scheduling Problems (MFFJSPs). To address it, we propose a new MOEA named MOEA/DCH by introducing a hierarchical estimation method, a clustering-based adaptive decomposition strategy, and a heuristic-based initialization method into a basic MOEA based on decomposition. Specifically, a hierarchical estimation method balances the convergence and diversity of non-dominant solutions by integrating Pareto dominance and scalarization function information. A clustering-based adaptive decomposition strategy is constructed to enhance the population's ability to approximate a complex Pareto front. A heuristic-based initialization method is developed to provide high-quality initial solutions. The performance of MOEA/DCH is verified and compared with five competitive MOEAs on widely-tested benchmark datasets. Empirical results demonstrate the effectiveness of MOEA/DCH in balancing the diversity and convergence of non-dominated solutions while ensuring overall convergence.
dc.exception.reasonnot deposited within three months of publication
dc.funderOther external funder (please detail below)
dc.funder.otherNational Natural Science Foundation of China
dc.funder.otherNational Key R&D Program of China
dc.identifier.citationZhang, X., Liu, S., Zhao, Z. and Yang, S. (2024) A decomposition-based evolutionary algorithm with clustering and hierarchical estimation for multi-objective fuzzy flexible jobshop scheduling. IEEE Transactions on Evolutionary Computation,
dc.identifier.doihttps://doi.org/10.1109/TEVC.2024.3359120
dc.identifier.urihttps://hdl.handle.net/2086/23755
dc.language.isoen
dc.peerreviewedYes
dc.projectid62073069, 62203093
dc.projectid2021YFB3301200
dc.publisherIEEE
dc.researchinstituteInstitute of Artificial Intelligence (IAI)
dc.subjectMulti-objective evolutionary algorithm
dc.subjecthierarchical estimation
dc.subjectclustering-based adaptive decomposition
dc.subjectfuzzy flexible jobshop scheduling
dc.titleA decomposition-based evolutionary algorithm with clustering and hierarchical estimation for multi-objective fuzzy flexible jobshop scheduling
dc.typeArticle

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