Multi-population evolution based dynamic constrained multiobjective optimization under diverse changing environments

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dc.cclicenceN/Aen
dc.contributor.authorChen, Qingda
dc.contributor.authorDing, Jinliang
dc.contributor.authorYen, Gary G.
dc.contributor.authorYang, Shengxiang
dc.contributor.authorChai, Tianyou
dc.date.acceptance2022-12
dc.date.accessioned2023-02-09T14:14:00Z
dc.date.available2023-02-09T14:14:00Z
dc.date.issued2023-02-02
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.abstractDynamic constrained multiobjective optimization involves irregular changes in the distribution of the true Pareto-optimal fronts, drastic changes in the feasible region caused by constraints, and the movement directions and magnitudes of the optimal distance variables due to diverse changing environments. To solve these problems, we propose a multi-population evolution based dynamic constrained multiobjective optimization algorithm. In this algorithm, we design a tribe classification operator to divide the population into different tribes according to a feasibility check and the objective values, which is beneficial for driving the population toward the feasible region and Pareto-optimal fronts. Meanwhile, a population selection strategy is proposed to identify promising solutions from tribes and exploit them to update the population. The optimal values of the distance variables vary differently with dynamic environments, thus, we design a dynamic response strategy for solutions in different tribes that estimates their distances to approach the Pareto-optimal fronts and regenerates a promising population when detecting environmental changes. In addition, a scalable generator is designed to simulate diverse movement directions and magnitudes of the optimal distance variables in real-world problems under dynamic environments, obtaining a set of improved test problems. Experimental results show the effectiveness of test problems, and the proposed algorithm is impressively competitive with several chosen state-of-the-art competitors.en
dc.funderOther external funder (please detail below)en
dc.funder.otherNational Natural Science Foundation of Chinaen
dc.identifier.citationQ. Chen, J. Ding, Gary G. Yen, S. Yang, and T. Chai. (2023) Multi-population evolution based dynamic constrained multiobjective optimization under diverse changing environments. IEEE Transactions on Evolutionary Computation,en
dc.identifier.doihttps://doi.org/10.1109/TEVC.2023.3241762
dc.identifier.urihttps://hdl.handle.net/2086/22500
dc.language.isoen_USen
dc.peerreviewedYesen
dc.projectid62203101, 61988101, 61991400, 61991403en
dc.publisherIEEEen
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en
dc.subjectDynamic constrained multiobjective optimizationen
dc.subjecttribe classification operatoren
dc.subjectpopulation selectionen
dc.subjectdynamic responseen
dc.titleMulti-population evolution based dynamic constrained multiobjective optimization under diverse changing environmentsen
dc.typeArticleen

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