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dc.contributor.authorQiao, Junfeien
dc.contributor.authorZhou, Hongbiaoen
dc.contributor.authorYang, Cuilien
dc.contributor.authorYang, Shengxiangen
dc.date.accessioned2018-11-08T09:35:22Z
dc.date.available2018-11-08T09:35:22Z
dc.date.issued2018-10-23
dc.identifier.citationQiao, J., Zhou, H., Yang, C., and Yang, S. (2019) A decomposition-based multiobjective evolutionary algorithm with angle-based adaptive penalty. Applied Soft Computing, 74, pp. 190–205.en
dc.identifier.issn1568-4946
dc.identifier.urihttp://hdl.handle.net/2086/17110
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.abstractA multiobjective evolutionary algorithm based on decomposition (MOEA/D) decomposes a multiobjective optimization problem (MOP) into a number of scalar optimization subproblems and optimizes them in a collaborative manner. In MOEA/D, decomposition mechanisms are used to push the population to approach the Pareto optimal front (POF), while a set of uniformly distributed weight vectors are applied to maintain the diversity of the population. Penalty-based boundary intersection (PBI) is one of the approaches used frequently in decomposition. In PBI, the penalty factor plays a crucial role in balancing convergence and diversity. However, the traditional PBI approach adopts a fixed penalty value, which will significantly degrade the performance of MOEA/D on some MOPs with complicated POFs. This paper proposes an angle-based adaptive penalty (AAP) scheme for MOEA/D, called MOEA/D-AAP, which can dynamically adjust the penalty value for each weight vector during the evolutionary process. Six newly designed benchmark MOPs and an MOP in the wastewater treatment process are used to test the effectiveness of the proposed MOEA/D-AAP. Comparison experiments demonstrate that the AAP scheme can significantly improve the performance of MOEA/D.en
dc.language.isoen_USen
dc.publisherElsevieren
dc.subjectMultiobjective evolutionary algorithmen
dc.subjectDecompositionen
dc.subjectPenalty boundary intersectionen
dc.subjectAngle-based adaptive penaltyen
dc.titleA decomposition-based multiobjective evolutionary algorithm with angle-based adaptive penaltyen
dc.typeArticleen
dc.identifier.doihttps://doi.org/10.1016/j.asoc.2018.10.028
dc.researchgroupCentre for Computational Intelligenceen
dc.peerreviewedYesen
dc.funderNational Science Foun- dation for Distinguished Young Scholars of Chinaen
dc.projectid61225016en
dc.cclicenceN/Aen
dc.date.acceptance2018-10-16en
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en


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