Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/85254
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dc.contributor.authorSuganthan, P. N.en
dc.contributor.authorZhao, Shi-Zheng.en
dc.contributor.authorZhang, Qing Fu.en
dc.date.accessioned2013-10-16T03:00:27Zen
dc.date.accessioned2019-12-06T16:00:28Z-
dc.date.available2013-10-16T03:00:27Zen
dc.date.available2019-12-06T16:00:28Z-
dc.date.copyright2012en
dc.date.issued2012en
dc.identifier.citationZhao, S. Z., Suganthan, P. N., & Zhang, Q. F. (2012). Decomposition-based multiobjective evolutionary algorithm with an ensemble of neighborhood sizes. IEEE transactions on evolutionary computation, 16(3), 442-446.en
dc.identifier.urihttps://hdl.handle.net/10356/85254-
dc.description.abstractThe multiobjective evolutionary algorithm based on decomposition (MOEA/D) has demonstrated superior performance by winning the multiobjective optimization algorithm competition at the CEC 2009. For effective performance of MOEA/D, neighborhood size (NS) parameter has to be tuned. In this letter, an ensemble of different NSs with online self-adaptation is proposed (ENS-MOEA/D) to overcome this shortcoming. Our experimental results on the CEC 2009 competition test instances show that an ensemble of different NSs with online self-adaptation yields superior performance over implementations with only one fixed NS.en
dc.language.isoenen
dc.relation.ispartofseriesIEEE transactions on evolutionary computationen
dc.subjectDRNTU::Engineering::Electrical and electronic engineeringen
dc.titleDecomposition-based multiobjective evolutionary algorithm with an ensemble of neighborhood sizesen
dc.typeJournal Articleen
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen
dc.identifier.doi10.1109/TEVC.2011.2166159en
item.fulltextNo Fulltext-
item.grantfulltextnone-
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