A dynamic-niching-based Pareto domination for multimodal multiobjective optimization

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dc.contributor.authorZou, Juan
dc.contributor.authorDeng, Qi
dc.contributor.authorLiu, Yuan
dc.contributor.authorYang, Xinjie
dc.contributor.authorYang, Shengxiang
dc.contributor.authorZheng, Jinhua
dc.date.acceptance2023-09
dc.date.accessioned2024-01-24T13:37:09Z
dc.date.available2024-01-24T13:37:09Z
dc.date.issued2023-09-18
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.abstractMaintaining the diversity of the decision space is of great significance in multimodal multiobjective optimization problems (MMOPs). Since the traditional Pareto-dominance-based algorithms prioritize the convergence of individuals by the Pareto-dominated sorting, it will face a phenomenon that a large number of well-distributed individuals could be dominated by other well-converged individuals during the optimization of MMOPs. To solve this problem, we propose a dynamic-niching-based Pareto domination, called DNPD, which adds a dynamic niche to constrain the tranditional Pareto dominantion to achieve a balance of convergence and diversity of population in the decision space. In the early stage of the algorithm, the smaller niche makes the algorithm retain a large number of well-distributed individuals. In the later stage of the algorithm, the dynamically increased niche accelerates the convergence of the population. DNPD can be integrated into the Pareto-dominance-based algorithms to solve MMOPs. Experimental results show that the DNPD performs well on MMF and IDMP series benchmark functions after comparing the original algorithm with the original algorithm combined with the DNPD.
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.otherNatural Science Foundation of Hunan Province, China
dc.identifier.citationZou, J., Deng, Q., Liu, Y., Yang, X., Yang, S. and Zheng, J. (2023) A dynamic-niching-based Pareto domination for multimodal multiobjective optimization. IEEE Transactions on Evolutionary Computation,
dc.identifier.doihttps://doi.org/10.1109/TEVC.2023.3316723
dc.identifier.urihttps://hdl.handle.net/2086/23485
dc.language.isoen
dc.peerreviewedYes
dc.projectid61876164, 61772178
dc.projectid2020JJ4590
dc.publisherIEEE
dc.researchinstituteInstitute of Artificial Intelligence (IAI)
dc.rightsAttribution-NonCommercial-NoDerivs 2.0 UK: England & Walesen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/2.0/uk/
dc.subjectMultimodal multiobjective optimization problems
dc.subjectDynamic-niching-based Pareto domination
dc.subjectwell-distributed
dc.subjectwell-converged
dc.titleA dynamic-niching-based Pareto domination for multimodal multiobjective optimization
dc.typeArticle

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