Solving dynamic multi-objective problems using polynomial fitting-based prediction algorithm

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dc.cclicenceCC-BY-NC-NDen
dc.contributor.authorZhang, Qingyang
dc.contributor.authorHe, Xiangyu
dc.contributor.authorYang, Shengxiang
dc.contributor.authorDong, Yongquan
dc.contributor.authorSong, Hui
dc.contributor.authorJiang, Shouyong
dc.date.acceptance2022-08-06
dc.date.accessioned2022-08-18T15:27:20Z
dc.date.available2022-08-18T15:27:20Z
dc.date.issued2022-08-11
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.abstractRecently, dynamic multi-objective optimization has received growing attention due to its popularity in real-world applications. Inspired by polynomial fitting, this paper proposes a polynomial fitting-based prediction algorithm (PFPA) and incorporates it into the model-based multi-objective estimation of distribution algorithm (RM-MEDA) for solving dynamic multi-objective optimization problems. When an environment change is detected, the main mission of PFPA is to predict high-quality search populations for tracking the moving Pareto-optimal set effectively. Firstly, the non-dominated solutions obtained in past environments are utilized to predict high-quality solutions based on a multi-step movement strategy. Secondly, a polynomial fitting-based strategy is designed to fit the distribution of variables according to the obtained search populations, and capture the relationship between variables in the new search environment. Thirdly, some effective search agents are generated for improving population convergence and diversity based on characteristics of variables. To evaluate the performance of the proposed algorithm, experimental results on a set of benchmark functions, with a variety of different dynamic characteristics and difficulties, and two classical dynamic engineering design problems show that PFPA is competitive with some state-of-the-art algorithms.en
dc.funderOther external funder (please detail below)en
dc.funder.otherNational Natural Science Foundation of Chinaen
dc.identifier.citationQ. Zhang, X. He, S. Yang, Y. Dong, H. Song, and S. Jiang. (2022) Solving dynamic multi-objective problems using polynomial fitting-based prediction algorithm. Information Sciences, 610, pp. 868-886en
dc.identifier.doihttps://doi.org/10.1016/j.ins.2022.08.020
dc.identifier.issn0020-0255
dc.identifier.urihttps://hdl.handle.net/2086/22123
dc.language.isoen_USen
dc.peerreviewedYesen
dc.projectid62006103, 61872168en
dc.publisherElsevieren
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en
dc.subjectDynamic multi-objective optimizationen
dc.subjectPolynomial fittingen
dc.subjectPrediction mechanismen
dc.subjectDynamic engineering designen
dc.titleSolving dynamic multi-objective problems using polynomial fitting-based prediction algorithmen
dc.typeArticleen

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