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dc.contributor.convenorPier Luca Lanzien_US
dc.contributor.authorMontgomery, Jamesen_US
dc.contributor.authorRandall, Marcusen_US
dc.contributor.authorLewis, Andrewen_US
dc.contributor.editorNatalio Krasnogor, Pier Luca Lanzien_US
dc.date.accessioned2017-04-24T09:53:05Z
dc.date.available2017-04-24T09:53:05Z
dc.date.issued2011en_US
dc.date.modified2013-03-19T22:49:14Z
dc.identifier.refurihttp://www.sigevo.org/gecco-2011/index.htmlen_US
dc.identifier.doi10.1145/2001576.2001669en_US
dc.identifier.urihttp://hdl.handle.net/10072/43581
dc.description.abstractDifferential evolution (DE) has been traditionally applied to solving benchmark continuous optimisation functions. To enable it to solve a combinatorially oriented design problem, such as the construction of effective radio frequency identification antennas, requires the development of a suitable encoding of the discrete decision variables in a continuous space. This study introduces an encoding that allows the algorithm to construct antennas of varying complexity and length. The DE algorithm developed is a multiobjective approach that maximises antenna efficiency and minimises resonant frequency. Its results are compared with those generated by a family of ant colony optimisation (ACO) metaheuristics that have formed the standard in this area. Results indicate that DE can work well on this problem and that the proposed solution encoding is suitable. On small antenna grid sizes (hence, smaller solution spaces) DE performs well in comparison to ACO, while as the solution space increases its relative performance decreases. However, as the ACO employs a local search operator that the DE currently does not, there is scope for further improvement to the DE approach.en_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_US
dc.format.extent851541 bytes
dc.format.mimetypeapplication/pdf
dc.languageEnglishen_US
dc.language.isoen_US
dc.publisherAssociation for Computing Machineryen_US
dc.publisher.placeUnited Statesen_US
dc.publisher.urihttp://www.sigevo.org/gecco-2011/index.htmlen_US
dc.relation.ispartofstudentpublicationNen_US
dc.relation.ispartofconferencename13th Annual Genetic and Evolutionary Computation Conference (GECCO'11)en_US
dc.relation.ispartofconferencetitleProceedings of the 13th Annual Conference on Genetic and Evolutionary Computation (GECCO'11)en_US
dc.relation.ispartofdatefrom2011-07-12en_US
dc.relation.ispartofdateto2011-07-16en_US
dc.relation.ispartoflocationDublin, Irelanden_US
dc.rights.retentionYen_US
dc.subject.fieldofresearchOptimisationen_US
dc.subject.fieldofresearchComputer Software not elsewhere classifieden_US
dc.subject.fieldofresearchAntennas and Propagationen_US
dc.subject.fieldofresearchcode010303en_US
dc.subject.fieldofresearchcode080399en_US
dc.subject.fieldofresearchcode100501en_US
dc.titleDifferential evolution for RFID antenna design: A comparison with ant colony optimisationen_US
dc.typeConference outputen_US
dc.type.descriptionE1 - Conference Publications (HERDC)en_US
dc.type.codeE - Conference Publicationsen_US
gro.rights.copyrightCopyright ACM 2011. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in GECCO '11 Proceedings of the 13th annual conference on Genetic and evolutionary computation , ISBN 978-1-4503-0557-0, dx.doi.org/10.1145/2001576.2001669en_US
gro.date.issued2011
gro.hasfulltextFull Text


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