Self-adaptive randomized and rank-based differential evolution for multimodal problems

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buir.contributor.authorArıkan, Orhan
buir.contributor.orcidArıkan, Orhan|0000-0002-3698-8888
dc.citation.epage640en_US
dc.citation.issueNumber4en_US
dc.citation.spage607en_US
dc.citation.volumeNumber51en_US
dc.contributor.authorUrfalioglu, O.en_US
dc.contributor.authorArıkan, Orhanen_US
dc.date.accessioned2015-07-28T12:00:58Z
dc.date.available2015-07-28T12:00:58Z
dc.date.issued2011-01-15en_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.description.abstractDifferential Evolution (DE) is a widely used successful evolutionary algorithm (EA) based on a population of individuals, which is especially well suited to solve problems that have non-linear, multimodal cost functions. However, for a given population, the set of possible new populations is finite and a true subset of the cost function domain. Furthermore, the update formula of DE does not use any information about the fitness of the population. This paper presents a novel extension of DE called Randomized and Rank-based Differential Evolution (R2DE) and its self-adaptive version SAR2DE to improve robustness and global convergence speed on multimodal problems by introducing two multiplicative terms in the DE update formula. The first term is based on a random variate of a Cauchy distribution, which leads to a randomization. The second term is based on ranking of individuals, so that R2DE exploits additional information provided by the population fitness. In extensive experiments conducted with a wide range of complexity settings, we show that the proposed heuristics lead to an overall improvement in robustness and speed of convergence compared to several global optimization techniques, including DE, Opposition based Differential Evolution (ODE), DE with Random Scale Factor (DERSF) and the self-adaptive Cauchy distribution based DE (NSDE).en_US
dc.description.provenanceMade available in DSpace on 2015-07-28T12:00:58Z (GMT). No. of bitstreams: 1 10.1007-s10898-011-9646-9.pdf: 1316532 bytes, checksum: 54a6212f033f7da9b2d91106b1f02633 (MD5)en
dc.description.sponsorshipTurkish Scientific and Technical Research Council (TUBITAK)en_US
dc.identifier.doi10.1007/s10898-011-9646-9en_US
dc.identifier.eissn1573-2916
dc.identifier.issn0925-5001
dc.identifier.urihttp://hdl.handle.net/11693/12301
dc.language.isoEnglishen_US
dc.publisherSpringeren_US
dc.relation.isversionofhttp://dx.doi.org/10.1007/s10898-011-9646-9en_US
dc.source.titleJournal of Global Optimizationen_US
dc.subjectDifferential evolutionen_US
dc.subjectCauchy distributionen_US
dc.subjectRankingen_US
dc.subjectRandomizationen_US
dc.subjectOptimizationen_US
dc.titleSelf-adaptive randomized and rank-based differential evolution for multimodal problemsen_US
dc.typeArticleen_US

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