Performance Analysis of Simulated Annealing Using Adaptive Markov Chain Length

Autores
Bermúdez, Carlos; Alfonso, Hugo; Minetti, Gabriela F.; Salto, Carolina
Año de publicación
2021
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
In the Simulated Annealing (SA) algorithm, the Metropolis algorithm is applied to generate a sequence of solutions in the search space, known as the Markov chain. Usually, the algorithms employ the same Markov Chain Length (MCL) in the Metropolis cycle for each temperature. However, SA can use adaptive methods to compute the MCL. This work aims to analyze the effect of using different MCL strategies in SA behavior. This experimentation considers the Water Distribution Network Design (WDND) problem, a multimodal and NP-hard problem interesting to optimize. The results indicate that the use of adaptive MCL strategies improves the solution quality versus the static one.
Workshop: WASI – Agentes y Sistemas Inteligentes
Red de Universidades con Carreras en Informática
Materia
Ciencias Informáticas
Simulated annealing
Markov chain length
Water distribution network design
Optimization
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/130311
Acceder
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network_name_str SEDICI (UNLP)
spelling Performance Analysis of Simulated Annealing Using Adaptive Markov Chain LengthBermúdez, CarlosAlfonso, HugoMinetti, Gabriela F.Salto, CarolinaCiencias InformáticasSimulated annealingMarkov chain lengthWater distribution network designOptimizationIn the Simulated Annealing (SA) algorithm, the Metropolis algorithm is applied to generate a sequence of solutions in the search space, known as the Markov chain. Usually, the algorithms employ the same Markov Chain Length (MCL) in the Metropolis cycle for each temperature. However, SA can use adaptive methods to compute the MCL. This work aims to analyze the effect of using different MCL strategies in SA behavior. This experimentation considers the Water Distribution Network Design (WDND) problem, a multimodal and NP-hard problem interesting to optimize. The results indicate that the use of adaptive MCL strategies improves the solution quality versus the static one.Workshop: WASI – Agentes y Sistemas InteligentesRed de Universidades con Carreras en Informática2021-10info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf21-30http://sedici.unlp.edu.ar/handle/10915/130311enginfo:eu-repo/semantics/altIdentifier/isbn/978-987-633-574-4info:eu-repo/semantics/reference/hdl/10915/129809info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T11:32:47Zoai:sedici.unlp.edu.ar:10915/130311Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 11:32:47.866SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Performance Analysis of Simulated Annealing Using Adaptive Markov Chain Length
title Performance Analysis of Simulated Annealing Using Adaptive Markov Chain Length
spellingShingle Performance Analysis of Simulated Annealing Using Adaptive Markov Chain Length
Bermúdez, Carlos
Ciencias Informáticas
Simulated annealing
Markov chain length
Water distribution network design
Optimization
title_short Performance Analysis of Simulated Annealing Using Adaptive Markov Chain Length
title_full Performance Analysis of Simulated Annealing Using Adaptive Markov Chain Length
title_fullStr Performance Analysis of Simulated Annealing Using Adaptive Markov Chain Length
title_full_unstemmed Performance Analysis of Simulated Annealing Using Adaptive Markov Chain Length
title_sort Performance Analysis of Simulated Annealing Using Adaptive Markov Chain Length
dc.creator.none.fl_str_mv Bermúdez, Carlos
Alfonso, Hugo
Minetti, Gabriela F.
Salto, Carolina
author Bermúdez, Carlos
author_facet Bermúdez, Carlos
Alfonso, Hugo
Minetti, Gabriela F.
Salto, Carolina
author_role author
author2 Alfonso, Hugo
Minetti, Gabriela F.
Salto, Carolina
author2_role author
author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
Simulated annealing
Markov chain length
Water distribution network design
Optimization
topic Ciencias Informáticas
Simulated annealing
Markov chain length
Water distribution network design
Optimization
dc.description.none.fl_txt_mv In the Simulated Annealing (SA) algorithm, the Metropolis algorithm is applied to generate a sequence of solutions in the search space, known as the Markov chain. Usually, the algorithms employ the same Markov Chain Length (MCL) in the Metropolis cycle for each temperature. However, SA can use adaptive methods to compute the MCL. This work aims to analyze the effect of using different MCL strategies in SA behavior. This experimentation considers the Water Distribution Network Design (WDND) problem, a multimodal and NP-hard problem interesting to optimize. The results indicate that the use of adaptive MCL strategies improves the solution quality versus the static one.
Workshop: WASI – Agentes y Sistemas Inteligentes
Red de Universidades con Carreras en Informática
description In the Simulated Annealing (SA) algorithm, the Metropolis algorithm is applied to generate a sequence of solutions in the search space, known as the Markov chain. Usually, the algorithms employ the same Markov Chain Length (MCL) in the Metropolis cycle for each temperature. However, SA can use adaptive methods to compute the MCL. This work aims to analyze the effect of using different MCL strategies in SA behavior. This experimentation considers the Water Distribution Network Design (WDND) problem, a multimodal and NP-hard problem interesting to optimize. The results indicate that the use of adaptive MCL strategies improves the solution quality versus the static one.
publishDate 2021
dc.date.none.fl_str_mv 2021-10
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
Objeto de conferencia
http://purl.org/coar/resource_type/c_5794
info:ar-repo/semantics/documentoDeConferencia
format conferenceObject
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/130311
url http://sedici.unlp.edu.ar/handle/10915/130311
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/isbn/978-987-633-574-4
info:eu-repo/semantics/reference/hdl/10915/129809
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
dc.format.none.fl_str_mv application/pdf
21-30
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
instacron:UNLP
reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
instname_str Universidad Nacional de La Plata
instacron_str UNLP
institution UNLP
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
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