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dc.contributor.authorZou, Juanen
dc.contributor.authorLi, Qingyaen
dc.contributor.authorYang, Shengxiangen
dc.contributor.authorZheng, Jinhuaen
dc.contributor.authorPeng, Zhouen
dc.contributor.authorPei, Tingruien
dc.date.accessioned2018-06-07T10:16:00Z
dc.date.available2018-06-07T10:16:00Z
dc.date.issued2018-03-28
dc.identifier.citationZou, J., Li, Q., Yang, S., Zheng, J., Peng, Z. and Pei, T. (2018) A dynamic multiobjective evolutionary algorithm based on a dynamic evolutionary environment model. Swarm and Evolutionary Computation, 44, pp. 247-259en
dc.identifier.issn2210-6502
dc.identifier.urihttp://hdl.handle.net/2086/16263
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.abstractTraditional dynamic multiobjective evolutionary algorithms usually imitate the evolution of nature, maintaining diversity of population through different strategies and making the population track the Pareto optimal solution set efficiently after the environmental change. However, these algorithms neglect the role of the dynamic environment in evolution, leading to the lacking of active guieded search. In this paper, a dynamic multiobjective evolutionary algorithm based on a dynamic evolutionary environment model is proposed (DEE-DMOEA). When the environment has not changed, this algorithm makes use of the evolutionary environment to record the knowledge and information generated in evolution, and in turn, the knowledge and information guide the search. When a change is detected, the algorithm helps the population adapt to the new environment through building a dynamic evolutionary environment model, which enhances the diversity of the population by the guided method, and makes the environment and population evolve simultaneously. In addition, an implementation of the algorithm about the dynamic evolutionary environment model is introduced in this paper. The environment area and the unit area are employed to express the evolutionary environment. Furthermore, the strategies of constraint, facilitation and guidance for the evolution are proposed. Compared with three other state-of-the-art strategies on a series of test problems with linear or nonlinear correlation between design variables, the algorithm has shown its effectiveness for dealing with the dynamic multiobjective problems.en
dc.language.isoen_USen
dc.publisherElsevieren
dc.subjectDynamic multiobjective optmizationen
dc.subjectEvolutionary algorithmsen
dc.subjectEvolutionary environmenten
dc.subjectDynamic evolutionary environment modelen
dc.titleA dynamic multiobjective evolutionary algorithm based on a dynamic evolutionary environment modelen
dc.typeArticleen
dc.identifier.doihttps://doi.org/10.1016/j.swevo.2018.03.010
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.acceptance2018-03-26en
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en


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