A cluster prediction strategy with the induced mutation for dynamic multi-objective optimization

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dc.contributor.authorXu, Kangyu
dc.contributor.authorXia, Yizhang
dc.contributor.authorZou, Juan
dc.contributor.authorHou, Zhanglu
dc.contributor.authorYang, Shengxiang
dc.contributor.authorHu, Yaru
dc.contributor.authorLiu, Yuan
dc.date.acceptance2024-01-18
dc.date.accessioned2024-01-30T10:26:38Z
dc.date.available2024-01-30T10:26:38Z
dc.date.issued2024-01-25
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.abstractDynamic multi-objective optimization problems (DMOPs) are multi-objective optimization problems in which at least one objective and/or related parameter vary over time. The challenge of solving DMOPs is to efficiently and accurately track the true Pareto-optimal set when the environment undergoes changes. However, many existing prediction-based methods overlook the distinct individual movement directions and the available information in the objective space, leading to biased predictions and misleading the subsequent search process. To address this issue, this paper proposes a prediction method called IMDMOEA, which relies on cluster center points and induced mutation. Specifically, employing linear prediction methods based on cluster center points in the decision space enables the algorithm to rapidly capture the population's evolutionary direction and distributional shape. Additionally, to enhance the algorithm's adaptability to significant environmental changes, the induced mutation strategy corrects the population's evolutionary direction by selecting promising individuals for mutation based on the predicted result of the Pareto front in the objective space. These two complementary strategies enable the algorithm to respond faster and more effectively to environmental changes. Finally, the proposed algorithm is evaluated using the JY, dMOP, FDA, and F test suites. The experimental results demonstrate that IMDMOEA competes favorably with other state-of-the-art algorithms.
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.citationXu, K., Xia, Y., Zou, J., Hou, Z., Yang, S., Hu, Y. and Liu, Y. (2024) A cluster prediction strategy with the induced mutation for dynamic multi-objective optimization. Information Sciences, 661, 120193
dc.identifier.doihttps://doi.org/10.1016/j.ins.2024.120193
dc.identifier.urihttps://hdl.handle.net/2086/23494
dc.language.isoen
dc.peerreviewedYes
dc.projectid62276224
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.subjectDynamic multi-objective optimization
dc.subjectEvolutionary algorithms
dc.subjectMulti-objective optimization problems
dc.subjectPrediction-based reaction
dc.titleA cluster prediction strategy with the induced mutation for dynamic multi-objective optimization
dc.typeArticle

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