A Metaheuristic Search Algorithm based on Sampling and Clustering

As optimization problems become more complicated and extensive, parameterization becomes complex, resulting in a difficult task requiring significant amounts of time and resources. In this paper we propose a heuristic search algorithm we call MCSA (Montecarlo-Clustering Search Algorithm), which is b...

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Autores: Harita Rascon, María de los Ángeles|||0000-0002-3732-4717, Wong, Álvaro|||0000-0002-8394-9478, Suppi, Remo|||0000-0002-0373-8292, Rexachs, Dolores|||0000-0001-5500-850X, Luque, Emilio|||0000-0002-2884-3232
Tipo de documento: artigo
Data de publicação:2024
País:España
Recursos:Universitat Autònoma de Barcelona
Repositório:Dipòsit Digital de Documents de la UAB
Idioma:inglês
OAI Identifier:oai:ddd.uab.cat:306154
Acesso em linha:https://ddd.uab.cat/record/306154
https://dx.doi.org/urn:doi:10.1109/access.2024.3354714
Access Level:Acceso aberto
Palavra-chave:Benchmarks
Heuristic Methods
Knapsack Problem
Montecarlo and Clustering Methods
Optimization
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spelling A Metaheuristic Search Algorithm based on Sampling and ClusteringHarita Rascon, María de los Ángeles|||0000-0002-3732-4717Wong, Álvaro|||0000-0002-8394-9478Suppi, Remo|||0000-0002-0373-8292Rexachs, Dolores|||0000-0001-5500-850XLuque, Emilio|||0000-0002-2884-3232BenchmarksHeuristic MethodsKnapsack ProblemMontecarlo and Clustering MethodsOptimizationAs optimization problems become more complicated and extensive, parameterization becomes complex, resulting in a difficult task requiring significant amounts of time and resources. In this paper we propose a heuristic search algorithm we call MCSA (Montecarlo-Clustering Search Algorithm), which is based on Montecarlo sampling, and a clustering strategy involving two techniques. Our objective is to apply MCSA, an inherently stochastic method, to address optimization problems. To assess its performance, we conducted an evaluation using classical benchmark optimization functions. Additionally, we leveraged the CEC2017 benchmark suite to comprehensively evaluate the algorithm, highlighting the pivotal role of the Exploration stage in our methodology. Subsequently, we extended our methodology to tackle a practical combinatorial problem, the Knapsack problem. This NP-Hard problem holds significant real-world applications in resource allocation, scheduling, planning, logistics, and more. Our contributions lie in parameterizing the Knapsack Problem to align with MCSA's parameters for reference indicators adjustment and achieving high-quality solutions, surpassing 90% in comparison to exhaustive methods such as branch and bound. 22024-01-0120242024-01-01Articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://ddd.uab.cat/record/306154https://dx.doi.org/urn:doi:10.1109/access.2024.3354714reponame:Dipòsit Digital de Documents de la UABinstname:Universitat Autònoma de BarcelonaInglésengAgencia Estatal de Investigación https://doi.org/10.13039/501100011033 PID2020-112496GB-I00open accesshttp://purl.org/coar/access_right/c_abf2Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.https://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:ddd.uab.cat:3061542026-06-06T12:50:31Z
dc.title.none.fl_str_mv A Metaheuristic Search Algorithm based on Sampling and Clustering
title A Metaheuristic Search Algorithm based on Sampling and Clustering
spellingShingle A Metaheuristic Search Algorithm based on Sampling and Clustering
Harita Rascon, María de los Ángeles|||0000-0002-3732-4717
Benchmarks
Heuristic Methods
Knapsack Problem
Montecarlo and Clustering Methods
Optimization
title_short A Metaheuristic Search Algorithm based on Sampling and Clustering
title_full A Metaheuristic Search Algorithm based on Sampling and Clustering
title_fullStr A Metaheuristic Search Algorithm based on Sampling and Clustering
title_full_unstemmed A Metaheuristic Search Algorithm based on Sampling and Clustering
title_sort A Metaheuristic Search Algorithm based on Sampling and Clustering
dc.creator.none.fl_str_mv Harita Rascon, María de los Ángeles|||0000-0002-3732-4717
Wong, Álvaro|||0000-0002-8394-9478
Suppi, Remo|||0000-0002-0373-8292
Rexachs, Dolores|||0000-0001-5500-850X
Luque, Emilio|||0000-0002-2884-3232
author Harita Rascon, María de los Ángeles|||0000-0002-3732-4717
author_facet Harita Rascon, María de los Ángeles|||0000-0002-3732-4717
Wong, Álvaro|||0000-0002-8394-9478
Suppi, Remo|||0000-0002-0373-8292
Rexachs, Dolores|||0000-0001-5500-850X
Luque, Emilio|||0000-0002-2884-3232
author_role author
author2 Wong, Álvaro|||0000-0002-8394-9478
Suppi, Remo|||0000-0002-0373-8292
Rexachs, Dolores|||0000-0001-5500-850X
Luque, Emilio|||0000-0002-2884-3232
author2_role author
author
author
author
dc.subject.none.fl_str_mv Benchmarks
Heuristic Methods
Knapsack Problem
Montecarlo and Clustering Methods
Optimization
topic Benchmarks
Heuristic Methods
Knapsack Problem
Montecarlo and Clustering Methods
Optimization
description As optimization problems become more complicated and extensive, parameterization becomes complex, resulting in a difficult task requiring significant amounts of time and resources. In this paper we propose a heuristic search algorithm we call MCSA (Montecarlo-Clustering Search Algorithm), which is based on Montecarlo sampling, and a clustering strategy involving two techniques. Our objective is to apply MCSA, an inherently stochastic method, to address optimization problems. To assess its performance, we conducted an evaluation using classical benchmark optimization functions. Additionally, we leveraged the CEC2017 benchmark suite to comprehensively evaluate the algorithm, highlighting the pivotal role of the Exploration stage in our methodology. Subsequently, we extended our methodology to tackle a practical combinatorial problem, the Knapsack problem. This NP-Hard problem holds significant real-world applications in resource allocation, scheduling, planning, logistics, and more. Our contributions lie in parameterizing the Knapsack Problem to align with MCSA's parameters for reference indicators adjustment and achieving high-quality solutions, surpassing 90% in comparison to exhaustive methods such as branch and bound.
publishDate 2024
dc.date.none.fl_str_mv 2
2024-01-01
2024
2024-01-01
dc.type.none.fl_str_mv Article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://ddd.uab.cat/record/306154
https://dx.doi.org/urn:doi:10.1109/access.2024.3354714
url https://ddd.uab.cat/record/306154
https://dx.doi.org/urn:doi:10.1109/access.2024.3354714
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv Agencia Estatal de Investigación https://doi.org/10.13039/501100011033 PID2020-112496GB-I00
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
https://creativecommons.org/licenses/by/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
https://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Dipòsit Digital de Documents de la UAB
instname:Universitat Autònoma de Barcelona
instname_str Universitat Autònoma de Barcelona
reponame_str Dipòsit Digital de Documents de la UAB
collection Dipòsit Digital de Documents de la UAB
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,223283

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