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Particle swarm and simulated annealing for multi-global optimization

dc.contributor.authorVaz, Ismael
dc.contributor.authorPereira, Ana I.
dc.contributor.authorFernandes, Edite M.G.P.
dc.date.accessioned2011-12-12T10:47:44Z
dc.date.available2011-12-12T10:47:44Z
dc.date.issued2005
dc.description.abstractParticle swarm and simulated annealing optimization algorithms proved to be valid in finding a global optimum in the bound constrained optimization context. However, their original versions can only detect one global optimum even if the problem has more than one solution. In this paper we propose modifications to both algorithms. In the particle swarm optimization algorithm we introduce gradient information to enable the computation of all the global and local optima. The simulated annealing algorithm is combined with a stretching technique to be able to compute all global optima. The numerical experiments carried out with a set of well-known test problems illustrate the effectiveness of the proposed algorithms.por
dc.identifier.citationVaz, Ismael; Pereira, Ana I.; Fernandes, Edite M.G.P. (2005). Particle swarm and simulated annealing for multi-global optimization. In Proceedings of the 6th WSEAS Internacional Conference on Evolutionary Computing. Lisboapor
dc.identifier.urihttp://hdl.handle.net/10198/6423
dc.language.isoengpor
dc.peerreviewedyespor
dc.publisherWSEASpor
dc.subjectMulti-global optimizationpor
dc.subjectParticle swarm optimizationpor
dc.subjectSimulated Annealingpor
dc.titleParticle swarm and simulated annealing for multi-global optimizationpor
dc.typeconference paper
dspace.entity.typePublication
oaire.citation.conferencePlaceLisbonpor
oaire.citation.title6th WSEAS Internacional Conference on Evolutionary Computing.por
person.familyNamePereira
person.givenNameAna I.
person.identifier.ciencia-id0716-B7C2-93E4
person.identifier.orcid0000-0003-3803-2043
person.identifier.ridF-3168-2010
person.identifier.scopus-author-id15071961600
rcaap.rightsopenAccesspor
rcaap.typeconferenceObjectpor
relation.isAuthorOfPublicatione9981d62-2a2b-4fef-b75e-c2a14b0e7846
relation.isAuthorOfPublication.latestForDiscoverye9981d62-2a2b-4fef-b75e-c2a14b0e7846

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