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dc.contributor.authorZou, Juanen
dc.contributor.authorZhang, Yupingen
dc.contributor.authorYang, Shengxiangen
dc.contributor.authorLiu, Yuanen
dc.contributor.authorZheng, Jinhuaen
dc.date.accessioned2018-02-06T11:58:19Z
dc.date.available2018-02-06T11:58:19Z
dc.date.issued2017-12-06
dc.identifier.citationZou, J. et al. (2018) Adaptive neighborhood selection for many-objective optimization problems. Applied Soft Computing, 64, pp.186-198.en
dc.identifier.issn1568-4946
dc.identifier.urihttp://hdl.handle.net/2086/15153
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.abstractIt is generally accepted that conflicts between convergence and distribution deteriorate with an increase in the number of objectives. Furthermore, Pareto dominance loses its effectiveness in many-objectives optimization problems (MaOPs), which have more than three objectives. Therefore, a more valid selection method is needed to balance convergence and distribution. This paper presents a many-objective evolutionary algorithm, called Adaptive Neighborhood Selection for Many-objective evolutionary algorithm(ANS-MOEA), to deal with MaOPs. This method defines the performance of each individual by two types of information, convergence information (CI) and distribution information (DI). In the critical layer, a well-converged individual is selected first from the population, and its neighbors, calculated by DI, are pushed into neighbor collection (NC) soon afterwards. Then, the proper distribution of the population is ensured by competition individuals with large DI go back to the population and individuals with small DI remain in the collection. Four state-of-the-art MaOEAs are selected as the competitive algorithms to validate ANS-MOEA. The experimental results show that ANS-MOEA can solve a MaOP and generate a set of remarkable solutions to balance convergence and distribution.en
dc.language.isoen_USen
dc.publisherElsevieren
dc.subjectMany-objective optimization problemsen
dc.subjectCritical layeren
dc.subjectDistributionen
dc.subjectConvergenceen
dc.subjectNeighborhoodsen
dc.subjectSelection mechanismen
dc.titleAdaptive neighborhood selection for many-objective optimization problemsen
dc.typeArticleen
dc.identifier.doihttps://doi.org/10.1016/j.asoc.2017.11.041
dc.researchgroupCentre for Computational Intelligenceen
dc.peerreviewedYesen
dc.funderNational Natural Science Foundation of Chinaen
dc.funderNational Natural Science Foundation of Chinaen
dc.projectid61673331en
dc.projectid61502408en
dc.cclicenceN/Aen
dc.date.acceptance2017-11-24en
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en


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