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dc.contributor.authorReyes-Fernández-de-Bulnes, Darian
dc.contributor.authorBolufé-Röhler, Antonio
dc.contributor.authorTamayo-Vera, Dania
dc.date.accessioned2021-05-25T11:27:42Z
dc.date.available2021-05-25T11:27:42Z
dc.date.issued2019-05-03
dc.identifier.citationGECONTEC: revista Internacional de Gestión del Conocimiento y la Tecnología, ISSN-e 2255-5684, Vol. 7, Nº. 2, 2019, págs. 1-19es_ES
dc.identifier.issn2255-5684
dc.identifier.urihttp://hdl.handle.net/10433/10338
dc.descriptionURL del artículo en la web de la Revista: https://www.upo.es/revistas/index.php/gecontec/article/view/4049es_ES
dc.description.abstractMinimum Population Search is a recently developed metaheuristic for optimization of mono-objective continuous problems, which has proven to be a very effective optimizing large scale and multi-modal problems. One of its key characteristic is the ability to perform an efficient exploration of large dimensional spaces. We assume that this feature may prove useful when optimizing multi-objective problems, thus this paper presents a study of how it can be adapted to a multi-objective approach. We performed experiments and comparisons with five multi-objective selection processes and we test the effectiveness of Thresheld Convergence on this class of problems. Following this analysis we suggest a Multi-objective variant of the algorithm. The proposed algorithm is compared with multi-objective evolutionary algorithms IBEA, NSGA2 and SPEA2 on several well-known test problems. Subsequently, we present two hybrid approaches with the IBEA and NSGA-II, these hybrids allow to further improve the achieved results.es_ES
dc.description.sponsorshipUniversidad Pablo de Olavidees_ES
dc.format.mimetypeapplication/pdf
dc.language.isoenes_ES
dc.publisherUniversidad Pablo de Olavidees_ES
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectEvolutionary Algorithmes_ES
dc.subjectMinimum Population Searches_ES
dc.subjectThresheld Convergencees_ES
dc.subjectMulti-objective Optimizationes_ES
dc.titleMulti-objective optimization approach based on Minimum Population Search algorithmes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessRightsopenAccesses_ES
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersiones_ES


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