Performance Analysis of Simulated Annealing Using Adaptive Markov Chain Length

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 adap...

Descripción completa

Guardado en:
Detalles Bibliográficos
Autores principales: Bermúdez, Carlos, Alfonso, Hugo, Minetti, Gabriela F., Salto, Carolina
Formato: Objeto de conferencia
Lenguaje:Inglés
Publicado: 2021
Materias:
Ciencias Informáticas
Simulated annealing
Markov chain length
Water distribution network design
Optimization
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/130311
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-130311
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ciencias Informáticas
Simulated annealing
Markov chain length
Water distribution network design
Optimization
spellingShingle Ciencias Informáticas
Simulated annealing
Markov chain length
Water distribution network design
Optimization
Bermúdez, Carlos
Alfonso, Hugo
Minetti, Gabriela F.
Salto, Carolina
Performance Analysis of Simulated Annealing Using Adaptive Markov Chain Length
topic_facet Ciencias Informáticas
Simulated annealing
Markov chain length
Water distribution network design
Optimization
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.
format Objeto de conferencia
Objeto de conferencia
author Bermúdez, Carlos
Alfonso, Hugo
Minetti, Gabriela F.
Salto, Carolina
author_facet Bermúdez, Carlos
Alfonso, Hugo
Minetti, Gabriela F.
Salto, Carolina
author_sort Bermúdez, Carlos
title Performance Analysis of Simulated Annealing Using Adaptive Markov Chain Length
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
publishDate 2021
url http://sedici.unlp.edu.ar/handle/10915/130311
work_keys_str_mv AT bermudezcarlos performanceanalysisofsimulatedannealingusingadaptivemarkovchainlength
AT alfonsohugo performanceanalysisofsimulatedannealingusingadaptivemarkovchainlength
AT minettigabrielaf performanceanalysisofsimulatedannealingusingadaptivemarkovchainlength
AT saltocarolina performanceanalysisofsimulatedannealingusingadaptivemarkovchainlength
bdutipo_str Repositorios
_version_ 1764820453267865604

Ejemplares similares