A New Hybrid Mutation Operator for Multiobjective Optimization with Differential Evolution

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dc.contributor.author Sindhya, Karthik
dc.contributor.author Ruuska, Sauli
dc.contributor.author Haanpää, Tomi
dc.contributor.author Miettinen, Kaisa
dc.date.accessioned 2011-10-13T05:08:38Z
dc.date.available 2011-10-13T05:08:38Z
dc.date.issued 2011
dc.identifier.citation Sindhya, K., Ruuska, S., Haanpää, T. & Miettinen, K. (2011). A New Hybrid Mutation Operator for Multiobjective Optimization with Differential Evolution. Soft Computing, 15 (10), 2041-2055. Retrieved from http://www.springerlink.com/content/dh056511337w452r
dc.identifier.issn 1432-7643
dc.identifier.other TUTKAID_47218
dc.identifier.uri http://hdl.handle.net/123456789/36798
dc.description.abstract Differential evolution has become one of the most widely used evolution- ary algorithms in multiobjective optimization. Its linear mutation operator is a sim- ple and powerful mechanism to generate trial vectors. However, the performance of the mutation operator can be improved by including a nonlinear part. In this pa- per, we propose a new hybrid mutation operator consisting of a polynomial based operator with nonlinear curve tracking capabilities and the differential evolution’s original mutation operator, to be efficiently able to handle various interdependencies between decision variables. The resulting hybrid operator is straightforward to implement and can be used within most evolutionary algorithms. Particularly, it can be used as a replacement in all algorithms utilizing the original mutation operator of differential evolution. We demonstrate how the new hybrid operator can be used by incorporating it into MOEA/D, a winning evolutionary multiobjective algorithm in a recent competition. The usefulness of the hybrid operator is demonstrated with extensive numerical experiments showing improvements in performance compared to the previous state of the art. en
dc.language.iso eng
dc.relation.ispartof Soft Computing
dc.relation.uri http://www.springerlink.com/content/dh056511337w452r/
dc.rights © Springer. This is an electronic final draft version of an article whose final and definitive form has been published in the Soft Computing published by Springer. en
dc.subject.other monitavoiteoptimointi fi
dc.subject.other Pareto-optimaalisuus fi
dc.subject.other DE en
dc.subject.other MOEA/D en
dc.subject.other Evolutionary algorithms en
dc.subject.other Nonlinear en
dc.subject.other Multi-criteria optimization en
dc.subject.other Polynomial en
dc.subject.other Pareto optimality en
dc.title A New Hybrid Mutation Operator for Multiobjective Optimization with Differential Evolution
dc.type Article en
dc.identifier.urn URN:NBN:fi:jyu-2011101211532
dc.subject.kota 111
dc.contributor.laitos Tietotekniikan laitos fi
jyx.tutka.volyme 15
jyx.tutka.mnumber 10
jyx.tutka.pagetopage 2041-2055
dc.type.uri http://purl.org/eprint/type/JournalArticle
dc.identifier.doi DOI:10.1007/s00500-011-0704-5
dc.date.updated 2011-10-12T07:02:30Z
dc.description.version Final draft
dc.contributor.publisher Springer
eprint.status http://purl.org/eprint/type/status/PeerReviewed

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