A self-learning simulated annealing algorithm for global optimizations of electromagnetic devices

dc.contributorHong Kong Polytechnic University
dc.contributorUniversidade Estadual Paulista (Unesp)
dc.contributorZhejiang University
dc.contributorAnsoft Corporation
dc.date.accessioned2014-05-20T15:23:07Z
dc.date.available2014-05-20T15:23:07Z
dc.date.created2014-05-20T15:23:07Z
dc.date.issued2000-07-01
dc.identifierIEEE Transactions on Magnetics. New York: IEEE-Inst Electrical Electronics Engineers Inc., v. 36, n. 4, p. 1004-1008, 2000.
dc.identifier0018-9464
dc.identifierhttp://hdl.handle.net/11449/130643
dc.identifier10.1109/20.877611
dc.identifierWOS:000090067900084
dc.identifier2-s2.0-0034217702
dc.description.abstractA self-learning simulated annealing algorithm is developed by combining the characteristics of simulated annealing and domain elimination methods. The algorithm is validated by using a standard mathematical function and by optimizing the end region of a practical power transformer. The numerical results show that the CPU time required by the proposed method is about one third of that using conventional simulated annealing algorithm.
dc.languageeng
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relationIEEE Transactions on Magnetics
dc.relation1.467
dc.relation0,488
dc.rightsAcesso restrito
dc.sourceScopus
dc.subjectDomain elimination method
dc.subjectElectromagnetic devices
dc.subjectPower transformer
dc.subjectSelf-learning ability
dc.subjectSimulated annealing algorithms
dc.subjectAlgorithms
dc.subjectAnnealing
dc.subjectOptimization
dc.subjectElectromagnetic fields
dc.titleA self-learning simulated annealing algorithm for global optimizations of electromagnetic devices
dc.typeArtículos de revistas


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