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dc.contributor.authorFernandes, Carlos M.
dc.contributor.authorFachada, Nuno
dc.contributor.authorMerelo Guervos, Juan Julián 
dc.contributor.authorRosa, Agostinho C.
dc.date.accessioned2020-04-22T09:08:14Z
dc.date.available2020-04-22T09:08:14Z
dc.date.issued2019-08-26
dc.identifier.citationFernandes CM, Fachada N, Merelo J-J, Rosa AC. 2019. Steady state particle swarm. PeerJ Comput. Sci. 5:e202 [DOI 10.7717/peerj-cs.202]es_ES
dc.identifier.urihttp://hdl.handle.net/10481/61466
dc.descriptionThe following grant information was disclosed by the authors: Fundação para a Ciência e Tecnologia (FCT), Research Fellowship: SFRH/BPD/66876/2009. FCT PROJECT: UID/EEA/50009/2013. EPHEMECH: TIN2014-56494-C4-3-P, Spanish Ministry of Economy and Competitivity. PROY-PP2015-06: Plan Propio 2015 UGR. CEI2015-MP-V17 of the Microprojects program 2015 from CEI BioTIC Granada.es_ES
dc.description.abstractThis paper investigates the performance and scalability of a new update strategy for the particle swarm optimization (PSO) algorithm. The strategy is inspired by the Bak–Sneppen model of co-evolution between interacting species, which is basically a network of fitness values (representing species) that change over time according to a simple rule: the least fit species and its neighbors are iteratively replaced with random values. Following these guidelines, a steady state and dynamic update strategy for PSO algorithms is proposed: only the least fit particle and its neighbors are updated and evaluated in each time-step; the remaining particles maintain the same position and fitness, unless they meet the update criterion. The steady state PSO was tested on a set of unimodal, multimodal, noisy and rotated benchmark functions, significantly improving the quality of results and convergence speed of the standard PSOs and more sophisticated PSOs with dynamic parameters and neighborhood. A sensitivity analysis of the parameters confirms the performance enhancement with different parameter settings and scalability tests show that the algorithm behavior is consistent throughout a substantial range of solution vector dimensions.es_ES
dc.description.sponsorshipThis work was supported by Fundação para a Ciência e Tecnologia (FCT) Research Fellowship SFRH/BPD/66876/2009 and FCT Project (UID/EEA/50009/2013), EPHEMECH (TIN2014-56494-C4-3-P, Spanish Ministry of Economy and Competitivity), PROY-PP2015-06 (Plan Propio 2015 UGR), project CEI2015-MP-V17 of the Microprojects program 2015 from CEI BioTIC Granada.es_ES
dc.language.isoenges_ES
dc.publisherPeerJes_ES
dc.rightsAtribución 3.0 España*
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subjectBak–Sneppen modeles_ES
dc.subjectParticle swarm optimizationes_ES
dc.subjectVelocity update strategyes_ES
dc.subjectAlgorithms es_ES
dc.subjectArtificial intelligence es_ES
dc.subjectDistributed Computinges_ES
dc.subjectParallel Computinges_ES
dc.titleSteady state particle swarmes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.identifier.doi10.7717/peerj-cs.202


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Atribución 3.0 España
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