Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/864
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dc.contributorDepartment of Electrical Engineering-
dc.contributorIndustrial Centre-
dc.creatorHo, SL-
dc.creatorYang, S-
dc.creatorNi, G-
dc.creatorLo, E-
dc.creatorWong, HCC-
dc.date.accessioned2014-12-11T08:25:20Z-
dc.date.available2014-12-11T08:25:20Z-
dc.identifier.issn0018-9464-
dc.identifier.urihttp://hdl.handle.net/10397/864-
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineersen_US
dc.rights© 2005 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.subjectInverse problemen_US
dc.subjectMultiobjective optimal algorithmen_US
dc.subjectOptimal designen_US
dc.subjectParticle swarm optimization (PSO)en_US
dc.titleA particle swarm optimization-based method for multiobjective design optimizationsen_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: Edward W. C. Loen_US
dc.description.otherinformationAuthor name used in this publication: H. C. Wongen_US
dc.identifier.spage1756-
dc.identifier.epage1759-
dc.identifier.volume41-
dc.identifier.issue5-
dc.identifier.doi10.1109/TMAG.2005.846033-
dcterms.abstractA particle swarm optimization (PSO) based algorithm for finding the Pareto solutions of multiobjective design problems is proposed. To enhance the global searching ability of the available PSOs, a novel formula for updating the particles' velocity and position, as well as the introduction of craziness, are reported. To handle a multiobjective design problem using the improved PSO, a new fitness assignment mechanism is proposed. Moreover, two repositories, together with the age variables for their members, are introduced for storing and selecting the previous best positions of the particle as well as that of its companions. Besides, the use of age variables to enhance the diversity of the solutions is also described. The proposed method is tested on two numerical examples with promising results.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationIEEE transactions on magnetics, May 2005, v. 41, no. 5, p. 1756-1759-
dcterms.isPartOfIEEE transactions on magnetics-
dcterms.issued2005-05-
dc.identifier.isiWOS:000229084200101-
dc.identifier.scopus2-s2.0-22044450246-
dc.identifier.eissn1941-0069-
dc.identifier.rosgroupidr23652-
dc.description.ros2004-2005 > 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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