Dynamic multiobjective optimization via an improved r-dominance relation and a novel prediction approach

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dc.contributor.authorHu, Yaru
dc.contributor.authorWang, Huibing
dc.contributor.authorOu, Junwei
dc.contributor.authorZou, Juan
dc.contributor.authorZheng, Jinhua
dc.contributor.authorYang, Shengxiang
dc.date.acceptance2024-11-09
dc.date.accessioned2024-11-25T17:03:37Z
dc.date.available2024-11-25T17:03:37Z
dc.date.issued2024-11-22
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.abstractWhen decision makers are only interested in a portion of the Pareto optimal front (PF) in dynamic multiobjective optimization problems (DMOPs), dynamic multiobjective evolutionary algorithms (DMOEAs) need to search only for the PF portion of interest to decision makers. However, this is challenging for most existing DMOEAs, as they are designed to search the entire PF, overlooking the preferences of decision makers. Therefore, we present a novel dynamic multiobjective optimization algorithm based on decision makers’ preference information, involving an improved r-dominance relation and a response strategy. It focuses on searching for the preference solutions (the region of interest) according to the decision makers’ preference information in dynamic multiobjective optimization. The improved r-dominance relation adapts the angle to measure the closeness between the solution and the preference information, which solves the convergence problem of the r-dominance relation since the original r-dominance relation has difficulty converging to the true PF when the preference information is located in the feasible objective region. The prediction mechanism is based on the movement of the population’s special points; it helps the population make adjustments in its moving direction and step size towards the new PF when a change is detected. Experimental results show that the proposed algorithm is efficient for dynamic multiobjective optimization and is competitive compared to state-of-the-art methods.
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.citationHu, Y. et al. (2025) Dynamic multiobjective optimization via an improved r-dominance relation and a novel prediction approach. Expert Systems with Applications, 263, 125765
dc.identifier.doihttps://doi.org/10.1016/j.eswa.2024.125765
dc.identifier.urihttps://hdl.handle.net/2086/24572
dc.language.isoen
dc.peerreviewedYes
dc.projectid62176228, 62276224, 62306262
dc.projectid2023JJ40637
dc.publisherElsevier
dc.researchinstitute.instituteDigital Future Institute
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectPreference information
dc.subjectDynamic multi-objective optimization
dc.subjectDecision makers
dc.titleDynamic multiobjective optimization via an improved r-dominance relation and a novel prediction approach
dc.typeArticle

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