An evolutionary algorithm based on dynamic sparse grouping for sparse large scale multiobjective optimization

dc.cclicenceCC-BY-NC-NDen
dc.contributor.authorZou, Yingjie
dc.contributor.authorLiu, Yuan
dc.contributor.authorZou, Juan
dc.contributor.authorYang, Shengxiang
dc.contributor.authorZheng, Jinhua
dc.date.acceptance2023-02-18
dc.date.accessioned2023-03-09T15:43:24Z
dc.date.available2023-03-09T15:43:24Z
dc.date.issued2023-03-09
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.en
dc.description.abstractSparse large scale multiobjective optimization problems (sparse LSMOPs) contain numerous decision variables, and their Pareto optimal solutions' decision variables are very sparse (i.e., the majority of these solutions' decision variables are zero-valued). This poses grand challenges to an algorithm in converging to the Pareto set. Numerous evolutionary algorithms (EAs) tailored for sparse LSMOPs have been proposed in recent years. However, the final population generated by these EAs is not sparse enough because the location of the nonzero decision variables is difficult to locate accurately and there is insufficient interaction between the nonzero decision variables' locating process and the nonzero decision variables' optimizing process. To address this issue, we propose a dynamic sparse grouping evolutionary algorithm (DSGEA) that dynamically groups decision variables in the population that have a comparable amount of nonzero decision variables. Improved evolutionary operators are introduced to optimize the decision variables in groups. As a result, the population obtained by DSGEA can stably evolve towards the sparser Pareto optimal that has a precise location of nonzero decision variables. The proposed algorithm outperforms existing up-to-date EAs for sparse LSMOPs in experiments on three real-world problems and eight benchmark problems.en
dc.funderOther external funder (please detail below)en
dc.funder.otherNational Natural Science Foundation of Chinaen
dc.funder.otheratural Science Foundation of Hunan Province, Chinaen
dc.identifier.citationY. Zou, Y. Liu, J. Zou, S. Yang, and J. Zheng. (2023) An evolutionary algorithm based on dynamic sparse grouping for sparse large scale multiobjective optimization. Information Sciences, 631, pp. 449-467en
dc.identifier.doihttps://doi.org/10.1016/j.ins.2023.02.062
dc.identifier.urihttps://hdl.handle.net/2086/22588
dc.language.isoen_USen
dc.peerreviewedYesen
dc.projectid62176228, 61876164en
dc.projectid2022JJ40452, 2020JJ4590en
dc.publisherElsevieren
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en
dc.subjectDecision variable groupingen
dc.subjectEvolutionary algorithmen
dc.subjectLarge scale multiobjective optimizationen
dc.subjectSparse multiobjective optimizationen
dc.titleAn evolutionary algorithm based on dynamic sparse grouping for sparse large scale multiobjective optimizationen
dc.typeArticleen

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