A Parameter Control Strategy for Parallel Island-Based Metaheuristics

In the field of optimisation, the accurate configuration of parameters in metaheuristic algorithms is a critical yet often arduous task that significantly impacts the efficiency and efficacy of the search process. This study was motivated by the need to address the inefficiencies and limitations ass...

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Detalles Bibliográficos
Autores: Prado-Rodríguez, Roberto, González, Patricia, Banga, Julio R.
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2025
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:dnet:digitalcsic_::c5e06fe16a0528484e37269f81c596ab
Acceso en línea:http://hdl.handle.net/10261/428686
Access Level:acceso abierto
Palabra clave:Ant Colony Optimisation
Binary Combinatorial Optimisation
Metaheuristics
Parallel strategies
Parameter control
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spelling A Parameter Control Strategy for Parallel Island-Based MetaheuristicsPrado-Rodríguez, RobertoGonzález, PatriciaBanga, Julio R.Ant Colony OptimisationBinary Combinatorial OptimisationMetaheuristicsParallel strategiesParameter controlIn the field of optimisation, the accurate configuration of parameters in metaheuristic algorithms is a critical yet often arduous task that significantly impacts the efficiency and efficacy of the search process. This study was motivated by the need to address the inefficiencies and limitations associated with conventional methods of parameter configuration, which typically involve manual, trial-and-error approaches. These traditional methods can lead to suboptimal performance and increased computational overhead. To tackle these challenges, this study introduces a novel adaptive parameter control strategy for parallel island-based metaheuristics, with a particular emphasis on the ant colony optimisation (ACO) algorithm. Our research process involved extensive experimentation to evaluate the effectiveness of this adaptive strategy. We conducted a series of tests to enable real-time adjustment of key parameters based on the performance of ACO colonies, thereby enhancing both exploration and exploitation capabilities. The results indicate that the adaptive strategy consistently outperforms offline manual and automated tuning configurations, particularly in larger and more complex problem instances, providing a more efficient solution for parameter optimisation in metaheuristics. These findings highlight the potential of dynamic parameter control to reduce dependency on expert knowledge and manual tuning while improving algorithmic performance.This work was supported by Ministerio de Ciencia, Innovación y Universidades and European Regional Development Fund (PID2022-136435NB-I00, PID2020-117271RB-C22, PID2023-146275NB-C22), and by Centro Superior de Investigaciones Científicas (PIE-202470E108).John Wiley & SonsMinisterio de Ciencia, Innovación y Universidades (España)Agencia Estatal de Investigación (España)European CommissionConsejo Superior de Investigaciones Científicas (España)Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]2026202620252026info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionhttp://hdl.handle.net/10261/428686reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)#PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE#info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2022-136435NB-I00info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-117271RB-C22info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2023-146275NB-C22info:eu-repo/grantAgreement/CSIC//PIE-202470E108http://dx.doi.org/10.1111/exsy.70061Síinfo:eu-repo/semantics/openAccessoai:dnet:digitalcsic_::c5e06fe16a0528484e37269f81c596ab2026-05-22T06:33:51Z
dc.title.none.fl_str_mv A Parameter Control Strategy for Parallel Island-Based Metaheuristics
title A Parameter Control Strategy for Parallel Island-Based Metaheuristics
spellingShingle A Parameter Control Strategy for Parallel Island-Based Metaheuristics
Prado-Rodríguez, Roberto
Ant Colony Optimisation
Binary Combinatorial Optimisation
Metaheuristics
Parallel strategies
Parameter control
title_short A Parameter Control Strategy for Parallel Island-Based Metaheuristics
title_full A Parameter Control Strategy for Parallel Island-Based Metaheuristics
title_fullStr A Parameter Control Strategy for Parallel Island-Based Metaheuristics
title_full_unstemmed A Parameter Control Strategy for Parallel Island-Based Metaheuristics
title_sort A Parameter Control Strategy for Parallel Island-Based Metaheuristics
dc.creator.none.fl_str_mv Prado-Rodríguez, Roberto
González, Patricia
Banga, Julio R.
author Prado-Rodríguez, Roberto
author_facet Prado-Rodríguez, Roberto
González, Patricia
Banga, Julio R.
author_role author
author2 González, Patricia
Banga, Julio R.
author2_role author
author
dc.contributor.none.fl_str_mv Ministerio de Ciencia, Innovación y Universidades (España)
Agencia Estatal de Investigación (España)
European Commission
Consejo Superior de Investigaciones Científicas (España)
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Ant Colony Optimisation
Binary Combinatorial Optimisation
Metaheuristics
Parallel strategies
Parameter control
topic Ant Colony Optimisation
Binary Combinatorial Optimisation
Metaheuristics
Parallel strategies
Parameter control
description In the field of optimisation, the accurate configuration of parameters in metaheuristic algorithms is a critical yet often arduous task that significantly impacts the efficiency and efficacy of the search process. This study was motivated by the need to address the inefficiencies and limitations associated with conventional methods of parameter configuration, which typically involve manual, trial-and-error approaches. These traditional methods can lead to suboptimal performance and increased computational overhead. To tackle these challenges, this study introduces a novel adaptive parameter control strategy for parallel island-based metaheuristics, with a particular emphasis on the ant colony optimisation (ACO) algorithm. Our research process involved extensive experimentation to evaluate the effectiveness of this adaptive strategy. We conducted a series of tests to enable real-time adjustment of key parameters based on the performance of ACO colonies, thereby enhancing both exploration and exploitation capabilities. The results indicate that the adaptive strategy consistently outperforms offline manual and automated tuning configurations, particularly in larger and more complex problem instances, providing a more efficient solution for parameter optimisation in metaheuristics. These findings highlight the potential of dynamic parameter control to reduce dependency on expert knowledge and manual tuning while improving algorithmic performance.
publishDate 2025
dc.date.none.fl_str_mv 2025
2026
2026
2026
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Publisher's version
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/428686
url http://hdl.handle.net/10261/428686
dc.relation.none.fl_str_mv #PLACEHOLDER_PARENT_METADATA_VALUE#
#PLACEHOLDER_PARENT_METADATA_VALUE#
#PLACEHOLDER_PARENT_METADATA_VALUE#
#PLACEHOLDER_PARENT_METADATA_VALUE#
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2022-136435NB-I00
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-117271RB-C22
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2023-146275NB-C22
info:eu-repo/grantAgreement/CSIC//PIE-202470E108
http://dx.doi.org/10.1111/exsy.70061
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv John Wiley & Sons
publisher.none.fl_str_mv John Wiley & Sons
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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