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dc.contributor.authorPrado-Rodríguez, Roberto
dc.contributor.authorGonzález, Patricia
dc.contributor.authorBanga, Julio R.
dc.contributor.authorDoallo, Ramón
dc.date.accessioned2024-04-18T09:15:23Z
dc.date.available2024-04-18T09:15:23Z
dc.date.issued2024
dc.identifier.citationPrado-Rodríguez, R., González, P., Banga, J. R., & Doallo, R. (2024). Improved cooperative Ant Colony Optimization for the solution of binary combinatorial optimization applications. Expert Systems, e13554. https://doi.org/10.1111/exsyes_ES
dc.identifier.urihttp://hdl.handle.net/2183/36241
dc.descriptionFinanciado para publicación en acceso aberto: Universidade da Coruña/CISUGes_ES
dc.description.abstract[Abstract]: Binary combinatorial optimization plays a crucial role in various scientific and engineering fields. While deterministic approaches have traditionally been used to solve these problems, stochastic methods, particularly metaheuristics, have gained popularity in recent years for efficiently handling large problem instances. Ant Colony Optimization (ACO) is among the most successful metaheuristics and is frequently employed in non-binary combinatorial problems due to its adaptability. Although for binary combinatorial problems ACO can suffer from issues such as rapid convergence to local minima, its eminently parallel structure means that it can be exploited to solve large and complex problems also in this field. In order to provide a versatile ACO implementation that achieves competitive results across a wide range of binary combinatorial optimization problems, we introduce a parallel multicolony strategy with an improved cooperation scheme to maintain search diversity. We evaluate our proposal (Binary Parallel Cooperative ACO, BiPCACO) using a comprehensive benchmark framework, showcasing its performance and, most importantly, its flexibility as a successful all-purpose solver for binary combinatorial problems.es_ES
dc.description.sponsorshipRPR, PGG and RDB acknowledges funding from Grants PID2019-104184RB-I00 and PID2022-136435NB-I00, funded by MCIN/AEI/10.13039/501100011033, PID2022 also funded by ‘ERDF A way of making Europe’, EU; Xunta de Galicia and FEDER funds of the EU (Consolidation Program of Competitive Reference Groups, ref. ED431C 2021/30). JRB acknowledges funding from the Ministry of Science and Innovation of Spain MCIN/AEI/10.13039/501100011033 through grant PID2020-117271RB- C22 (BIODYNAMICS). Authors also acknowledge the Galician Supercom- puting Center (CESGA) for the access to its facilities. Funding for open access charge: Universidade da Coruña/CISUGes_ES
dc.description.sponsorshipXunta de Galicia; ED431C 2021/30es_ES
dc.language.isoenges_ES
dc.publisherJohn Wiley & Sonses_ES
dc.relationinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-104184RB-I00/ES/DESAFÍOS ACTUALES EN HPC: ARQUITECTURAS, SOFTWARE Y APLICACIONESes_ES
dc.relationinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-117271RB-C22/ES/REGULACION DINAMICA EN VARIAS ESCALAS DE INGENIERIA METABOLICA: INFERENCIA MULTIMODELO Y OPTIMALIDAD DINAMICAes_ES
dc.relationinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2022-136435NB-I00/ES/ARQUITECTURAS, FRAMEWORKS Y APLICACIONES DE LA COMPUTACION DE ALTAS PRESTACIONESes_ES
dc.relation.urihttps://doi.org/10.1111/exsy.13554es_ES
dc.rightsAttribution-NonCommercial 4.0 International (CC BY-NC 4.0)es_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc/3.0/es/*
dc.subjectAnt Colony Optimizationes_ES
dc.subjectbinary combinatorial optimizationes_ES
dc.subjectmetaheuristices_ES
dc.subjectparallel strategieses_ES
dc.titleImproved cooperative Ant Colony Optimization for the solution of binary combinatorial optimization applicationses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessinfo:eu-repo/semantics/openAccesses_ES
UDC.journalTitleExpert Systemses_ES
dc.identifier.doi10.1111/exsy.13554


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