A constrained multi-objective evolutionary strategy based on population state detection

dc.cclicenceN/Aen
dc.contributor.authorTang, Huanrong
dc.contributor.authorYu, Fan
dc.contributor.authorZou, Juan
dc.contributor.authorYang, Shengxiang
dc.contributor.authorZheng, Jinhua
dc.date.acceptance2021-08-30
dc.date.accessioned2021-10-07T08:54:33Z
dc.date.available2021-10-07T08:54:33Z
dc.date.issued2021-09-14
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.abstractThe difficulty of solving constrained multi-objective optimization problems (CMOPs) using evolutionary algorithms is to balance constraint satisfaction and objective optimization while fully considering the diversity of the solution set. Many CMOPs with disconnected feasible subregions make it difficult for algorithms to search for all feasible nondominated solutions. To address these issues, we propose a population state detection strategy (PSDS) and a restart scheme to determine whether the environmental selection strategy needs to be changed based on the situation of population. When the population converges in the feasible region, the unconstrained environmental selection allows the population to cross the current feasible region. When the population converging outside the feasible region, all constraints will be considered in the environmental selection to select the population for the feasible region. In addition, the restart scheme will use reinitialization to make the population jump out of unprofitable iterations. The proposed algorithm enhances the search ability through the detection strategy and provides more diversity by reinitializing the population. The experimental results on four constraint test suites with various features have demonstrated that the proposed algorithm had better or competitive performance against other state-of-the-art constrained multi-objective algorithms.en
dc.funderOther external funder (please detail below)en
dc.funder.otherNational Natural Science Foundation of Chinaen
dc.identifier.citationTang, H., Yu, F., Zou, J., Yang, S. and Zheng, J. (2021) A constrained multi-objective evolutionary strategy based on population state detection. Swarm and Evolutionary Computation, 100978.en
dc.identifier.doihttps://doi.org/10.1016/j.swevo.2021.100978
dc.identifier.issn2210-6502
dc.identifier.urihttps://dora.dmu.ac.uk/handle/2086/21320
dc.language.isoen_USen
dc.peerreviewedYesen
dc.projectid61876164 and 61772178en
dc.publisherElsevieren
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en
dc.subjectConstrained multi-objective optimizationen
dc.subjectEvolutionary algorithmen
dc.subjectState detectionen
dc.subjectConstraint handlingen
dc.subjectRestart schemeen
dc.titleA constrained multi-objective evolutionary strategy based on population state detectionen
dc.typeArticleen

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