Näytä suppeat kuvailutiedot

dc.contributor.authorSindhya, Karthik
dc.contributor.authorRuuska, Sauli
dc.contributor.authorHaanpää, Tomi
dc.contributor.authorMiettinen, Kaisa
dc.date.accessioned2011-10-13T05:08:38Z
dc.date.available2011-10-13T05:08:38Z
dc.date.issued2011
dc.identifier.citationSindhya, K., Ruuska, S., Haanpää, T., & Miettinen, K. (2011). A New Hybrid Mutation Operator for Multiobjective Optimization with Differential Evolution. <em>Soft Computing</em>, 15 (10), 2041-2055. <a href="http://dx.doi.org/10.1007/s00500-011-0704-5">doi:10.1007/s00500-011-0704-5</a> Retrieved from <a href="http://www.springerlink.com/content/dh056511337w452r/">http://www.springerlink.com/content/dh056511337w452r/</a>
dc.identifier.otherTUTKAID_47218
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/36798
dc.description.abstractDifferential 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.isoeng
dc.relation.ispartofseriesSoft Computing
dc.relation.urihttp://www.springerlink.com/content/dh056511337w452r/
dc.subject.otherDEen
dc.subject.otherMOEA/Den
dc.subject.otherEvolutionary algorithmsen
dc.subject.otherNonlinearen
dc.subject.otherMulti-criteria optimizationen
dc.subject.otherPolynomialen
dc.subject.otherPareto optimalityen
dc.subject.othermonitavoiteoptimointifi
dc.subject.otherPareto-optimaalisuusfi
dc.titleA New Hybrid Mutation Operator for Multiobjective Optimization with Differential Evolution
dc.typeArticle
dc.identifier.urnURN:NBN:fi:jyu-2011101211532
dc.contributor.laitosTietotekniikan laitosfi
dc.type.urihttp://purl.org/eprint/type/JournalArticle
dc.identifier.doi10.1007/s00500-011-0704-5
dc.date.updated2011-10-12T07:02:30Z
dc.contributor.publisherSpringer
dc.type.coarjournal article
dc.description.reviewstatuspeerReviewed
dc.format.pagerange2041-2055
dc.relation.issn1432-7643
dc.relation.numberinseries10
dc.relation.volume15
dc.type.versionacceptedVersion
dc.rights.copyright© 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.rights.accesslevelopenAccessfi


Aineistoon kuuluvat tiedostot

Thumbnail

Aineisto kuuluu seuraaviin kokoelmiin

Näytä suppeat kuvailutiedot