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dc.contributor.authorJiang, Shouyongen
dc.contributor.authorYang, Shengxiangen
dc.date.accessioned2016-09-20T14:10:34Z
dc.date.available2016-09-20T14:10:34Z
dc.date.issued2016-08-31
dc.identifier.citationJiang, S. and Yang, S. (2016) Convergence versus diversity in multiobjective optimization. Proceedings of the 14th International Conference on Parallel Problems Solving from Nature (PPSN XIV), Lecture Notes in Computer Science, 9921, pp. 984-993en
dc.identifier.isbn9783319458229
dc.identifier.urihttp://hdl.handle.net/2086/12623
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.abstractConvergence and diversity are two main goals in multiobjective optimization. In literature, most existing multiobjective optimization evolutionary algorithms (MOEAs) adopt a convergence-first-and-diversity-second environmental selection which prefers nondominated solutions to dominated ones, as is the case with the popular nondominated sorting based selection method. While convergence-first sorting has continuously shown effectiveness for handling a variety of problems, it faces challenges to maintain well population diversity due to the overemphasis of convergence. In this paper, we propose a general diversity-first sorting method for multiobjective optimization. Based on the method, a new MOEA, called DBEA, is then introduced. DBEA is compared with the recently-developed nondominated sorting genetic algorithm III (NSGA-III) on different problems. Experimental studies show that the diversity-first method has great potential for diversity maintenance and is very competitive for many-objective optimization.en
dc.language.isoen_USen
dc.publisherSpringeren
dc.subjectMultiobjective optimization problemsen
dc.subjectEvolutionary Computationen
dc.subjectConvergenceen
dc.subjectDiversityen
dc.titleConvergence versus diversity in multiobjective optimizationen
dc.typeConferenceen
dc.identifier.doihttp://dx.doi.org/10.1007/978-3-319-45823-6_92
dc.researchgroupCentre for Computational Intelligenceen
dc.peerreviewedYesen
dc.explorer.multimediaNoen
dc.funderEPSRC (Engineering and Physical Sciences Research Council)en
dc.projectidEP/K001310/1en
dc.cclicenceCC-BY-NC-NDen
dc.date.acceptance2016-05-30en
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en


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