A subspace-knowledge transfer based dynamic constrained multiobjective evolutionary algorithm

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dc.contributor.authorChen, Guoyu
dc.contributor.authorGuo, Yinan
dc.contributor.authorJiang, Min
dc.contributor.authorYang, Shengxiang
dc.contributor.authorZhao, Xiaoxiao
dc.contributor.authorGong, Dunwei
dc.date.acceptance2023-11
dc.date.accessioned2023-12-18T16:49:08Z
dc.date.available2023-12-18T16:49:08Z
dc.date.issued2023-12-12
dc.description.abstractDynamic constrained multiobjective optimization problems (DCMOPs) have gained increasing attention in the evolutionary computation field during the past years. Among the existing studies, it is a significant challenge to rationally utilize historical knowledge to track the changing Pareto optima in DCMOPs. To address this issue, a subspace-knowledge transfer based dynamic constrained multiobjective evolutionary algorithm is proposed in this article, termed SKTEA. Once a new environment appears, objective space is partitioned into a series of subspaces by a set of uniformly-distributed reference points. Following that, a subspace that has complete time series under certain number of historical environments is regarded as the feasible subspace by the subspace classification method. Otherwise, it is the infeasible one. Based on the classification results, a subspace-driven initialization strategy is designed. In each feasible subspace, Kalman filter is introduced to predict an individual in terms of historical solutions preserved in external storage. The predicted individuals of feasible neighbors are transferred into the infeasible subspace to generate the one, and then an initial population at the new time is formed by integrating predicted and transferred individuals. Intensive experiments on 10 test benchmarks verify that SKTEA outperforms several state-of-the-art DCMOEAs, achieving good performance in solving DCMOPs.
dc.funderOther external funder (please detail below)
dc.funder.otherNational Natural Science Foundation of China
dc.funder.otherNational Key R&D Program of China
dc.identifier.citationChen, G., Guo, Y., Jiang, M., Yang, S., Zhao, X. and Gong, D. (2023) A subspace-knowledge transfer based dynamic constrained multiobjective evolutionary algorithm. IEEE Transactions on Emerging Topics in Computational Intelligence,
dc.identifier.doihttps://doi.org/10.1109/TETCI.2023.3336918
dc.identifier.issn2471-285X
dc.identifier.urihttps://hdl.handle.net/2086/23411
dc.language.isoen
dc.peerreviewedYes
dc.projectid61973305
dc.projectidU23A20340
dc.projectid61573361
dc.projectid52121003
dc.projectid2022YFB4703701
dc.publisherIEEE Press
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 constrained multiobjective optimization
dc.subjectEvolutionary algorithm
dc.subjectObjective subspace
dc.subjectKnowledge transfer
dc.titleA subspace-knowledge transfer based dynamic constrained multiobjective evolutionary algorithm
dc.typeArticle

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