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Spreading Algorithm for Single-Objective Problems

dc.contributor.authorPires, E. J. Solteiro
dc.contributor.authorMendes, Luís
dc.contributor.authorLopes, António M.
dc.contributor.authorOliveira, P. B. de Moura
dc.contributor.authorTenreiro Machado, J. A.
dc.date.accessioned2019-04-16T09:38:54Z
dc.date.available2019-04-16T09:38:54Z
dc.date.issued2011-11
dc.description.abstractThis paper addresses the problem of finding several different solutions with the same optimum performance in single objective real-world engineering problems. In this paper a parallel robot design was proposed. Thereby, this paper presents a genetic algorithm to optimize uni-objective problems with an infinite number of optimal solutions. The algorithm uses the maximin concept and -dominance to promote diversity over the admissible space. The performance of the proposed algorithm is analyzed with three well-known test functions and one function obtained from practical realworld engineering optimization problems. A spreading analysis is performed showing that the solutions drawn by the algorithm are well dispersed.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.isbn978-989-8331-12-0
dc.identifier.urihttp://hdl.handle.net/10400.22/13573
dc.language.isoengpt_PT
dc.subjectSpreading techniquept_PT
dc.subjectGenetic algorithmpt_PT
dc.titleSpreading Algorithm for Single-Objective Problemspt_PT
dc.typeconference object
dspace.entity.typePublication
oaire.citation.conferencePlaceISEC - Coimbrapt_PT
oaire.citation.endPage9pt_PT
oaire.citation.startPage1pt_PT
oaire.citation.titleISCIES'11 - 2nd International Symposium on Computational Intelligence for Engineering Systemspt_PT
person.familyNameTenreiro Machado
person.givenNameJ. A.
person.identifier.orcid0000-0003-4274-4879
person.identifier.ridM-2173-2013
person.identifier.scopus-author-id55989030100
rcaap.rightsopenAccesspt_PT
rcaap.typeconferenceObjectpt_PT
relation.isAuthorOfPublication82cd5c17-07b6-492b-b3e3-ecebdad1254f
relation.isAuthorOfPublication.latestForDiscovery82cd5c17-07b6-492b-b3e3-ecebdad1254f

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