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dc.contributor.authorHu, Yaru
dc.contributor.authorOu, Junwei
dc.contributor.authorZheng, Jinhua
dc.contributor.authorZou, Juan
dc.contributor.authorYang, Shengxiang
dc.contributor.authorRuan, Gan
dc.date.accessioned2019-11-12T10:21:39Z
dc.date.available2019-11-12T10:21:39Z
dc.date.issued2019-11-08
dc.identifier.citationHu, Y., Ou, J., Zheng, J., Zou, J., Yang, S. and Ruan, G. (2019) Solving dynamic multi-objective problems with an evolutionary multi-directional search approach. Knowledge-Based Systems,en
dc.identifier.urihttps://dora.dmu.ac.uk/handle/2086/18768
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.abstractThe challenge of solving dynamic multi-objective optimization problems is to effectively and efficiently trace the varying Pareto optimal front and/or Pareto optimal set. To this end, this paper proposes a multi-direction search strategy, aimed at finding the dynamic Pareto optimal front and/or Pareto optimal set as quickly and accurately as possible before the next environmental change occurs. The proposed method adopts a multi-directional search approach which mainly includes two parts: an improved local search and a global search. The first part uses individuals from the current population to produce solutions along each decision variable’s direction within a certain range and updates the population using the generated solutions. As a result, the first strategy enhances the convergence of the population. In part two, individuals are generated in a specific random method along every dimension’s orientation in the decision variable space, so as to achieve good diversity as well as guarantee the avoidance of local optimal solutions. The proposed algorithm is measured on several benchmark test suites with various dynamic characteristics and different difficulties. Experimental results show that this algorithm is very competitive in dealing with dynamic multi-objective optimization problems when compared with four state-of-the-art approaches.en
dc.language.isoenen
dc.publisherElsevieren
dc.subjectDynamic multi-objective optimizationen
dc.subjectLocal searchen
dc.subjectMulti-directional search strategyen
dc.titleSolving dynamic multi-objective problems with an evolutionary multi-directional search approachen
dc.typeArticleen
dc.identifier.doihttps://doi.org/10.1016/j.knosys.2019.105175
dc.peerreviewedYesen
dc.funderOther external funder (please detail below)en
dc.projectid61502408, 61673331, 61379062 and 61772178en
dc.cclicenceN/Aen
dc.date.acceptance2019-10-30
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en
dc.funder.otherNational Natural Science Foundation of Chinaen


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