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dc.contributor.authorFu, Liuweien
dc.contributor.authorZou, Juanen
dc.contributor.authorYang, Shengxiangen
dc.contributor.authorRuan, Ganen
dc.contributor.authorZheng, Jinhuaen
dc.contributor.authorMa, Zhongweien
dc.date.accessioned2017-10-31T10:08:29Z
dc.date.available2017-10-31T10:08:29Z
dc.date.issued2018-02-08
dc.identifier.citationFu, L. et al. (2017) A proportion-based selection scheme for multi-objective optimization. Proceedings of the 2017 IEEE Symposium Series on Computational Intelligence,en
dc.identifier.urihttp://hdl.handle.net/2086/14765
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.abstractClassical multi-objective evolutionary algorithms (MOEAs) have been proven to be inefficient for solving multiobjective optimizations problems when the number of objectives increases due to the lack of sufficient selection pressure towards the Pareto front (PF). This poses a great challenge to the design of MOEAs. To cope with this problem, researchers have developed reference-point based methods, where some well-distributed points are produced to assist in maintaining good diversity in the optimization process. However, the convergence speed of the population may be severely affected during the searching procedure. This paper proposes a proportion-based selection scheme (denoted as PSS) to strengthen the convergence to the PF as well as maintain a good diversity of the population. Computational experiments have demonstrated that PSS is significantly better than three peer MOEAs on most test problems in terms of diversity and convergence.en
dc.language.isoen_USen
dc.publisherIEEE Pressen
dc.subjectMulti-objective evolutionary algorithmsen
dc.subjectMultiobjective optimizations problemen
dc.subjectProportion-based selectionen
dc.titleA proportion-based selection scheme for multi-objective optimizationen
dc.typeConferenceen
dc.identifier.doihttps://dx.doi.org/10.1109/SSCI.2017.8285266
dc.researchgroupCentre for Computational Intelligenceen
dc.peerreviewedYesen
dc.funderNational Natural Science Foundation of Chinaen
dc.projectid61502408en
dc.projectid61673331en
dc.cclicenceCC-BY-NCen
dc.date.acceptance2017-09-15en
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en


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