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dc.contributor.authorLi, Qingyaen
dc.contributor.authorZou, Juanen
dc.contributor.authorYang, Shengxiangen
dc.contributor.authorZheng, Jinhuaen
dc.contributor.authorRuan, Ganen
dc.date.accessioned2018-02-06T15:25:30Z
dc.date.available2018-02-06T15:25:30Z
dc.date.issued2018-01-27
dc.identifier.citationLi, Q. et al. (2018) A predictive strategy based on special points for evolutionary dynamic multi-objective optimization. Soft Computing, 23 (11), pp. 3723-3739en
dc.identifier.issn1432-7643
dc.identifier.issn1433-7479
dc.identifier.urihttp://hdl.handle.net/2086/15159
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 linken
dc.description.abstractThere are some real-world problems in which multiple objectives conflict with each other and the objectives change with time. These problems require an optimization algorithm to track the moving Pareto front or Pareto set over time. In this paper, we propose a predictive strategy based on special points (SPPS) which consists of three mechanisms. The first one is that the non-dominated set is predicted directly by feed-forward center points, which can eliminate many useless individuals predicted by traditional prediction using feed-forward center points. The second one is that a special point set(such as boundary point, knee point, etc.) is introduced into the predicted population which can track Pareto front or Pareto set more accurately. The third one is the adaptive diversity maintenance mechanism based on boundary points and center points. The mechanism can introduce diverse individuals of the corresponding number according to the degree of difficulty of the problem to keep the diversity of the population. The number of these diverse individuals is strongly related to the center points. Then, they are generated evenly throughout the decision space between the boundary points. The proposed strategy is compared with the four other state-of-the-art strategies. The experimental results show that SPPS can do well for dynamic multi-objective optimization.en
dc.language.isoen_USen
dc.publisherSpringeren
dc.subjectEvolutionary dynamic multi-objective optimizationen
dc.subjectPredictionen
dc.subjectBoundary pointen
dc.subjectKnee pointen
dc.subjectAdaptive diversity maintenance strategyen
dc.titleA predictive strategy based on special points for evolutionary dynamic multi-objective optimizationen
dc.typeArticleen
dc.identifier.doihttps://doi.org/10.1007/s00500-018-3033-0
dc.researchgroupCentre for Computational Intelligenceen
dc.peerreviewedYesen
dc.funderNational Natural Science Foundation of Chinaen
dc.funderNational Natural Science Foundation of Chinaen
dc.projectid61502408en
dc.projectid61673331en
dc.cclicenceN/Aen
dc.date.acceptance2018-01-12en
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en


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