Multi-constructor CMSA for the maximum disjoint dominating sets problem

We propose the Multi-Constructor CMSA, a Construct, Merge, Solve and Adapt (CMSA) algorithm that employs multiple heuristic procedures, respectively solution constructors, for the Maximum Disjoint Dominating Sets Problem (MDDSP). At every iteration of the search procedure, the solution components bu...

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Detalhes bibliográficos
Autores: Rosati, Roberto Maria, Bouamama, Salim, Blum, Christian
Formato: artículo
Estado:Versión publicada
Fecha de publicación:2024
País:España
Recursos:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/378073
Acesso em linha:http://hdl.handle.net/10261/378073
https://api.elsevier.com/content/abstract/scopus_id/85175185549
Access Level:acceso abierto
Palavra-chave:CMSA
Domatic partition problem
Instance reduction
Maximum disjoint dominating sets problem
Reinforcement learning
Wireless sensor network
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spelling Multi-constructor CMSA for the maximum disjoint dominating sets problemRosati, Roberto MariaBouamama, SalimBlum, ChristianCMSADomatic partition problemInstance reductionMaximum disjoint dominating sets problemReinforcement learningWireless sensor networkWe propose the Multi-Constructor CMSA, a Construct, Merge, Solve and Adapt (CMSA) algorithm that employs multiple heuristic procedures, respectively solution constructors, for the Maximum Disjoint Dominating Sets Problem (MDDSP). At every iteration of the search procedure, the solution components built by the constructors are merged into a sub-instance, which is subsequently solved by an exact solver and then adapted to keep only beneficial solution components. In our CMSA the solution constructors are chosen at random according to their relative probabilities, which are adapted during the search, through a mechanism based on reinforcement learning. We test two variants of the new Multi-Constructor CMSA that employ, respectively, two and six solution constructors, on a new set of 3600 problem instances, encompassing random graphs, Watts–Strogatz networks and Barabási-Albert networks, generated through a Hammersley sampling procedure on the instance space. We compare our algorithm against six heuristics from the literature, as well as with the standard version of CMSA. Furthermore, we employ an integer linear programming (ILP) model that is able to achieve a good performance for small, sparse graphs. Overall, the experimental results show that all versions of CMSA outperform by a large margin the previous state of the art and that, among the variants of CMSA, the novel version that combines two constructors provides slightly better results than the other ones, more prominently on larger graphs.Additionally, the authors acknowledge TAILOR, a project funded by EU Horizon 2020 research and innovation programme under GA No 952215, for the support provided for this research. Furthermore, this work was supported by grant PID2022-136787NB-I00 funded by MCIN/AEI/10.13039/501100011033. Finally, the support provided by CINECA through grant ISCRA C IA4EVRP HP10CE285L is gratefully acknowledged by the authors.Peer reviewedElsevierEuropean CommissionMinisterio de Ciencia, Innovación y Universidades (España)Agencia Estatal de Investigación (España)CINECARosati, Roberto Maria [0000-0001-9560-6301]Bouamama, Salim [0000-0002-5842-2850]Blum, Christian [0000-0002-1736-3559]Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202520252024info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/10261/378073https://api.elsevier.com/content/abstract/scopus_id/85175185549reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE#info:eu-repo/grantAgreement/EC/H2020/952215info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2022-136787NB-I00The underlying dataset has been published as supplementary material of the article in the publisher platform at DOI https://doi.org/10.1016/j.cor.2023.106450https://doi.org/10.1016/j.cor.2023.106450Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/3780732026-05-22T06:33:51Z
dc.title.none.fl_str_mv Multi-constructor CMSA for the maximum disjoint dominating sets problem
title Multi-constructor CMSA for the maximum disjoint dominating sets problem
spellingShingle Multi-constructor CMSA for the maximum disjoint dominating sets problem
Rosati, Roberto Maria
CMSA
Domatic partition problem
Instance reduction
Maximum disjoint dominating sets problem
Reinforcement learning
Wireless sensor network
title_short Multi-constructor CMSA for the maximum disjoint dominating sets problem
title_full Multi-constructor CMSA for the maximum disjoint dominating sets problem
title_fullStr Multi-constructor CMSA for the maximum disjoint dominating sets problem
title_full_unstemmed Multi-constructor CMSA for the maximum disjoint dominating sets problem
title_sort Multi-constructor CMSA for the maximum disjoint dominating sets problem
dc.creator.none.fl_str_mv Rosati, Roberto Maria
Bouamama, Salim
Blum, Christian
author Rosati, Roberto Maria
author_facet Rosati, Roberto Maria
Bouamama, Salim
Blum, Christian
author_role author
author2 Bouamama, Salim
Blum, Christian
author2_role author
author
dc.contributor.none.fl_str_mv European Commission
Ministerio de Ciencia, Innovación y Universidades (España)
Agencia Estatal de Investigación (España)
CINECA
Rosati, Roberto Maria [0000-0001-9560-6301]
Bouamama, Salim [0000-0002-5842-2850]
Blum, Christian [0000-0002-1736-3559]
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv CMSA
Domatic partition problem
Instance reduction
Maximum disjoint dominating sets problem
Reinforcement learning
Wireless sensor network
topic CMSA
Domatic partition problem
Instance reduction
Maximum disjoint dominating sets problem
Reinforcement learning
Wireless sensor network
description We propose the Multi-Constructor CMSA, a Construct, Merge, Solve and Adapt (CMSA) algorithm that employs multiple heuristic procedures, respectively solution constructors, for the Maximum Disjoint Dominating Sets Problem (MDDSP). At every iteration of the search procedure, the solution components built by the constructors are merged into a sub-instance, which is subsequently solved by an exact solver and then adapted to keep only beneficial solution components. In our CMSA the solution constructors are chosen at random according to their relative probabilities, which are adapted during the search, through a mechanism based on reinforcement learning. We test two variants of the new Multi-Constructor CMSA that employ, respectively, two and six solution constructors, on a new set of 3600 problem instances, encompassing random graphs, Watts–Strogatz networks and Barabási-Albert networks, generated through a Hammersley sampling procedure on the instance space. We compare our algorithm against six heuristics from the literature, as well as with the standard version of CMSA. Furthermore, we employ an integer linear programming (ILP) model that is able to achieve a good performance for small, sparse graphs. Overall, the experimental results show that all versions of CMSA outperform by a large margin the previous state of the art and that, among the variants of CMSA, the novel version that combines two constructors provides slightly better results than the other ones, more prominently on larger graphs.
publishDate 2024
dc.date.none.fl_str_mv 2024
2025
2025
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/10261/378073
https://api.elsevier.com/content/abstract/scopus_id/85175185549
url http://hdl.handle.net/10261/378073
https://api.elsevier.com/content/abstract/scopus_id/85175185549
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv #PLACEHOLDER_PARENT_METADATA_VALUE#
#PLACEHOLDER_PARENT_METADATA_VALUE#
info:eu-repo/grantAgreement/EC/H2020/952215
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2022-136787NB-I00
The underlying dataset has been published as supplementary material of the article in the publisher platform at DOI https://doi.org/10.1016/j.cor.2023.106450
https://doi.org/10.1016/j.cor.2023.106450
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
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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score 15.812455

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