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dc.contributor.authorOu, Junwei
dc.contributor.authorZheng, Jinhua
dc.contributor.authorRuan, Gan
dc.contributor.authorHu, Yaru
dc.contributor.authorZou, Juan
dc.contributor.authorLi, Miqing
dc.contributor.authorYang, Shengxiang
dc.contributor.authorTan, Xu
dc.date.accessioned2019-08-27T13:38:38Z
dc.date.available2019-08-27T13:38:38Z
dc.date.issued2019-08-21
dc.identifier.citationJ. Ou, J. Zheng, G. Ruan, Y. Hu, J. Zou, M. Li, S. Yang, and X. Tan. (2019) A Pareto-based evolutionary algorithm using decomposition and truncation for dynamic multi-objective optimization. Applied Soft Computing,en
dc.identifier.urihttps://www.dora.dmu.ac.uk/handle/2086/18356
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.abstractMaintaining a balance between convergence and diversity of the population in the objective space has been widely recognized as the main challenge when solving problems with two or more conflicting objectives. This is added by another difficulty of tracking the Pareto optimal solutions set (POS) and/or the Pareto optimal front (POF) in dynamic scenarios. Confronting these two issues, this paper proposes a Pareto-based evolutionary algorithm using decomposition and truncation to address such dynamic multi-objective optimization problems (DMOPs). The proposed algorithm includes three contributions: a novel mating selection strategy, an efficient environmental selection technique and an effective dynamic response mechanism. The mating selection considers the decomposition-based method to select two promising mating parents with good diversity and convergence. The environmental selection presents a modified truncation method to preserve good diversity. The dynamic response mechanism is evoked to produce some solutions with good diversity and convergence whenever an environmental change is detected. In the experimental studies, a range of dynamic multi-objective benchmark problems with different characteristics were carried out to evaluate the performance of the proposed method. The experimental results demonstrate that the method is very competitive in terms of convergence and diversity, as well as in response speed to the changes, when compared with six other state-of-the-art methods.en
dc.language.isoenen
dc.publisherElsevieren
dc.subjectDynamic multi-objective optimizationen
dc.subjectEvolutionary algorithmsen
dc.subjectDecompositionen
dc.subjectDiversityen
dc.titleA Pareto-based evolutionary algorithm using decomposition and truncation for dynamic multi-objective optimizationen
dc.typeArticleen
dc.identifier.doihttps://doi.org/10.1016/j.asoc.2019.105673
dc.peerreviewedYesen
dc.funderOther external funder (please detail below)en
dc.projectid61502408, 61673331, 61772178 and 61403326en
dc.cclicenceCC-BY-NC-NDen
dc.date.acceptance2019-07-29
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en
dc.funder.otherNational Natural Science Foundation of Chinaen


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