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dc.contributor.authorZou, Juanen
dc.contributor.authorLi, Qingyaen
dc.contributor.authorYang, Shengxiangen
dc.contributor.authorBai, Huien
dc.contributor.authorZheng, Jinhuaen
dc.date.accessioned2017-10-09T14:51:19Z
dc.date.available2017-10-09T14:51:19Z
dc.date.issued2017-08-31
dc.identifier.citationZou, J., Li, Q., Yang, S., Bai, H. and Zheng, J. (2017) A prediction strategy based on center points and knee points for evolutionary dynamic multi-objective optimization. Applied Soft Computing, 61, pp. 806-818en
dc.identifier.urihttp://hdl.handle.net/2086/14579
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.abstractIn real life, there are many dynamic multi-objective optimization problems which vary over time, requiring an optimization algorithm to track the movement of the Pareto front (Pareto set) with time. In this paper, we propose a novel prediction strategy based on center points and knee points (CKPS) consisting of three mechanisms. First, a method of predicting the non-dominated set based on the forward-looking center points is proposed. Second, the knee point set is introduced to the predicted population to predict accurately the location and distribution of the Pareto front after an environmental change. Finally, an adaptive diversity maintenance strategy is proposed, which can generate some random individuals of the corresponding number according to the degree of difficulty of the problem to maintain the diversity of the population. The proposed strategy is compared with four other state-of-the-art strategies. The experimental results show that CKPS is effective for evolutionary dynamic multi-objective optimization.en
dc.language.isoen_USen
dc.publisherElsevieren
dc.subjectEvolutionary dynamic multi-objective optimizationen
dc.subjectPredictionen
dc.subjectCenter pointen
dc.subjectKnee pointen
dc.subjectAdaptive diversity maintenance mechanismen
dc.titleA prediction strategy based on center points and knee points for evolutionary dynamic multi-objective optimizationen
dc.typeArticleen
dc.identifier.doihttps://doi.org/10.1016/j.asoc.2017.08.004
dc.researchgroupCentre for Computational Intelligenceen
dc.peerreviewedYesen
dc.funderNational Natural Science Foundation of Chinaen
dc.projectid61502408en
dc.projectid61673331en
dc.cclicenceCC-BY-NC-NDen
dc.date.acceptance2017-08-03en
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en


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