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Maximin spreading algorithm

2010-07Conference paper Restricted access
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datacite.subject.fosCiências Naturais::Ciências da Computação e da Informação
datacite.subject.fosCiências Naturais::Matemáticas
datacite.subject.sdg03:Saúde de Qualidade
datacite.subject.sdg10:Reduzir as Desigualdades
datacite.subject.sdg11:Cidades e Comunidades Sustentáveis
dc.contributor.authorPires, E. J. Solteiro
dc.contributor.authorMendes, Luís
dc.contributor.authorLopes, António M.
dc.contributor.authorOliveira, P. B. de Moura
dc.contributor.authorMachado, J. A. Tenreiro
dc.contributor.authorVaz, João
dc.contributor.authorRosário, Maria J.
dc.date.accessioned2025-11-28T17:36:15Z
dc.date.available2025-11-28T17:36:15Z
dc.date.issued2010-07
dc.descriptionEISBN - 978-1-4244-6911-6
dc.description.abstractThis paper presents a genetic algorithm to optimize uni-objective problems with an infinite number of optimal solutions. The algorithm uses the maximin concept and e-dominance to promote diversity over the admissible space. The proposed algorithm is tested with two well-known functions. The practical results of the algorithm are in good agreement with the optimal solutions of these functions. Moreover, the proposed optimization method is also applied in two practical real-world engineering optimization problems, namely, in radio frequency circuit design and in kinematic optimization of a parallel robot. In all the cases, the algorithm draws a set of optimal solutions. This means that each problem can be solved in several different ways, all with the same maximum performance.eng
dc.identifier.citationE. J. S. Pires et al., "Maximin spreading algorithm," IEEE Congress on Evolutionary Computation, Barcelona, Spain, 2010, pp. 1-8, doi: https://doi.org/10.1109/CEC.2010.5586236.
dc.identifier.doi10.1109/cec.2010.5586236
dc.identifier.isbn978-1-4244-6909-3
dc.identifier.isbn978-1-4244-6911-6
dc.identifier.urihttp://hdl.handle.net/10400.8/14798
dc.language.isoeng
dc.peerreviewedyes
dc.publisherIEEE Canada
dc.relation.hasversionhttps://ieeexplore.ieee.org/document/5586236
dc.relation.ispartofIEEE Congress on Evolutionary Computation
dc.rights.uriN/A
dc.subjectOptimization
dc.subjectAlgorithm design and analysis
dc.subjectKinematics
dc.subjectManipulators
dc.subjectHeuristic algorithms
dc.subjectSwitching circuits
dc.subjectElectronic mail
dc.titleMaximin spreading algorithmeng
dc.typeconference paper
dspace.entity.typePublication
oaire.citation.conferenceDate2010-07
oaire.citation.conferencePlaceBarcelona, Spain
oaire.citation.title2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameMoreira Mendes
person.givenNameLuís Miguel
person.identifier.ciencia-id0A11-CBD0-48A2
relation.isAuthorOfPublication6651dc04-a958-4198-a5fa-a5b665e08656
relation.isAuthorOfPublication.latestForDiscovery6651dc04-a958-4198-a5fa-a5b665e08656

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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 e-dominance to promote diversity over the admissible space. The proposed algorithm is tested with two well-known functions. The practical results of the algorithm are in good agreement with the optimal solutions of these functions. Moreover, the proposed optimization method is also applied in two practical real-world engineering optimization problems, namely, in radio frequency circuit design and in kinematic optimization of a parallel robot. In all the cases, the algorithm draws a set of optimal solutions. This means that each problem can be solved in several different ways, all with the same maximum performance.
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