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dc.contributor.authorLiu, J.
dc.contributor.authorTeo, Kok Lay
dc.contributor.authorWang, Xiangyu
dc.contributor.authorWu, Changzhi
dc.date.accessioned2017-01-30T14:53:01Z
dc.date.available2017-01-30T14:53:01Z
dc.date.created2015-10-29T04:09:28Z
dc.date.issued2015
dc.identifier.citationLiu, J. and Teo, K.L. and Wang, X. and Wu, C. 2015. An exact penalty function-based differential search algorithm for constrained global optimization. Soft Computing. 20 (4): pp. 1305-1313.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/41557
dc.identifier.doi10.1007/s00500-015-1588-6
dc.description.abstract

Differential search (DS) is a recently developed derivative-free global heuristic optimization algorithm for solving unconstrained optimization problems. In this paper, by applying the idea of exact penalty function approach, a DS algorithm, where an S-type dynamical penalty factor is introduced so as to achieve a better balance between exploration and exploitation, is developed for constrained global optimization problems. To illustrate the applicability and effectiveness of the proposed approach, a comparison study is carried out by applying the proposed algorithm and other widely used evolutionary methods on 24 benchmark problems. The results obtained clearly indicate that the proposed method is more effective and efficient over the other widely used evolutionary methods for most these benchmark problems.

dc.publisherSpringer Verlag
dc.titleAn exact penalty function-based differential search algorithm for constrained global optimization
dc.typeJournal Article
dcterms.source.issn1432-7643
dcterms.source.titleSoft Computing
curtin.departmentDepartment of Mathematics and Statistics
curtin.accessStatusFulltext not available


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