Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/831
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dc.contributorDepartment of Electrical Engineering-
dc.creatorHo, SL-
dc.creatorYang, S-
dc.creatorNi, G-
dc.creatorWong, KF-
dc.date.accessioned2014-12-11T08:27:16Z-
dc.date.available2014-12-11T08:27:16Z-
dc.identifier.issn0018-9464-
dc.identifier.urihttp://hdl.handle.net/10397/831-
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineersen_US
dc.rights© 2007 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.en_US
dc.rightsThis material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.en_US
dc.subjectApproximation techniqueen_US
dc.subjectEvolutionary computationen_US
dc.subjectGenetic algorithm (GA)en_US
dc.subjectMultiobjective optimizationen_US
dc.titleAn efficient multiobjective optimizer based on genetic algorithm and approximation techniques for electromagnetic designen_US
dc.typeJournal/Magazine Articleen_US
dc.description.otherinformationAuthor name used in this publication: S. L. Hoen_US
dc.description.otherinformationAuthor name used in this publication: G. Z. Nien_US
dc.identifier.spage1605-
dc.identifier.epage1608-
dc.identifier.volume43-
dc.identifier.issue4-
dc.identifier.doi10.1109/TMAG.2006.892113-
dcterms.abstractTo provide an efficient multiobjective optimizer, an approximation technique based on the moving least squares approximation is integrated into an improved genetic algorithm. In order to use fully, both the a posteriori information gathered from the latest searched nondominated solutions and the a priori knowledge about the search space and individuals, in guiding the search towards more and better Pareto solutions, a gradient direction based perturbation search strategy and a preference function based fitness penalization scheme are proposed. Numerical results are reported to validate the proposed work.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationIEEE transactions on magnetics, Apr. 2007, v. 43, no. 4, p. 1605-1608-
dcterms.isPartOfIEEE transactions on magnetics-
dcterms.issued2007-04-
dc.identifier.scopus2-s2.0-33947687739-
dc.identifier.eissn1941-0069-
dc.identifier.rosgroupidr34769-
dc.description.ros2006-2007 > Academic research: refereed > Publication in refereed journal-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_IR/PIRAen_US
dc.description.pubStatusPublisheden_US
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