Weak relationship indicator-based evolutionary algorithm for multimodal multi-objective optimization

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dc.contributor.authorXiang, Yi
dc.contributor.authorZheng, Jinhua
dc.contributor.authorHu, Yaru
dc.contributor.authorLiu, Yuan
dc.contributor.authorZou, Juan
dc.contributor.authorDeng, Qi
dc.contributor.authorYang, Shengxiang
dc.date.acceptance2023-09-30
dc.date.accessioned2023-10-16T15:18:47Z
dc.date.available2023-10-16T15:18:47Z
dc.date.issued2023-10-05
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.abstractMultimodal multi-objective problems (MMOPs) have multiple equivalent Pareto sets (PSs) that map to the same Pareto optimal front (PF). Traditional multimodal multiobjective algorithms (MMEAs) use strong relationships to guide population convergence, but this can lead to two problems: the population may explore easier-to-search PSs and lose more difficult-to-search PSs, and it may not retain local PSs well. To address these issues, we propose a weak relationship indicator-based MMEA that includes weak convergence indicators and density evaluation indicators. The weak convergence indicator considers the relationship between an individual and its neighbors, while the density evaluation indicator considers the density information of the individual and its neighbors. This allows the population to retain solutions from different PSs during exploration. An archive based on weak convergence indicators also retains excellent solutions generated during the evolution of the population. Experimental results show that our algorithm ranked first in terms of overall score when compared with seven state-of-the-art algorithms using the Friedman Test.
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.citationYi Xiang, Jinhua Zheng, Yaru Hu, Yuan Liu, Juan Zou, Qi Deng, and Shengxiang Yang. (2023) Weak relationship indicator-based evolutionary algorithm for multimodal multi-objective optimization. Information Sciences, 652, 119755
dc.identifier.doihttps://doi.org/10.1016/j.ins.2023.119755
dc.identifier.urihttps://hdl.handle.net/2086/23276
dc.language.isoen
dc.peerreviewedYes
dc.projectid62176228, 62276224
dc.projectid2022JJ40452, 2023JJ40637
dc.publisherElsevier
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.subjectEvolutionary algorithms
dc.subjectMultimodal
dc.subjectMulti-objective optimization
dc.subjectWeak relationship indicators
dc.titleWeak relationship indicator-based evolutionary algorithm for multimodal multi-objective optimization
dc.typeArticle

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