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Application of Evolutionary Algorithms in the Study of Fractional Order Electromagnetic Phenomena

dc.contributor.authorJesus, Isabel S.
dc.contributor.authorTenreiro Machado, J. A.
dc.date.accessioned2019-04-18T11:15:26Z
dc.date.available2019-04-18T11:15:26Z
dc.date.issued2009
dc.description.abstractThe application of evolutionary algorithms in optimization is currently receiving growing interest from researchers with various backgrounds. A genetic algorithm (GA) is a search technique based on the natural selection process that provides a unique flexibility and robustness for process optimization. Recently, a closer look of some phenomena present in electrical systems and the motivation towards the development of comprehensive models, pointed out the requirement for a fractional calculus approach. In this work, it is applied the concept of fractional calculus to define, and to evaluate, the electrical potential of fractional order. Bearing these ideas in mind, the paper addresses the analysis and the synthesis of fractional-order multipoles, based in a GA optimization scheme.pt_PT
dc.description.versionN/Apt_PT
dc.identifier.urihttp://hdl.handle.net/10400.22/13650
dc.language.isoengpt_PT
dc.titleApplication of Evolutionary Algorithms in the Study of Fractional Order Electromagnetic Phenomenapt_PT
dc.typeconference object
dspace.entity.typePublication
oaire.citation.endPage14pt_PT
oaire.citation.startPage1pt_PT
person.familyNameJesus
person.familyNameTenreiro Machado
person.givenNameIsabel S.
person.givenNameJ. A.
person.identifier.ciencia-id131A-D722-8D60
person.identifier.ciencia-id7A18-4935-5B29
person.identifier.orcid0000-0002-7545-5822
person.identifier.orcid0000-0003-4274-4879
person.identifier.ridM-2173-2013
person.identifier.scopus-author-id9333348500
person.identifier.scopus-author-id55989030100
rcaap.rightsopenAccesspt_PT
rcaap.typeconferenceObjectpt_PT
relation.isAuthorOfPublicatione7f8ed34-e3e4-49f5-815b-861c7f28936e
relation.isAuthorOfPublication82cd5c17-07b6-492b-b3e3-ecebdad1254f
relation.isAuthorOfPublication.latestForDiscoverye7f8ed34-e3e4-49f5-815b-861c7f28936e

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