A New Multi-Swarm Particle Swarm Optimization and Its Application to Lennard-Jones Problem
Autor(a) principal: | |
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Data de Publicação: | 2010 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | INFOCOMP: Jornal de Ciência da Computação |
Texto Completo: | https://infocomp.dcc.ufla.br/index.php/infocomp/article/view/312 |
Resumo: | Particle swarm optimization (PSO) algorithm is a modern heuristic technique for global optimization. Due to its ease of implementation, excellent effectiveness, and few parameters to adjust it has gained a lot of attention in the recent years. However, with the increasing size and computational complexity of real life optimization problems it takes long solution times and the solution quality also degrades, so there is a constant need to improve its effectiveness and robustness to find better solution in the shortest possible computational time. Parallel computing is a possible way to fulfill this requirement. In this paper we propose a multi-swarm approach to parallelize PSO algorithm (MSPSO). The performance of the proposed algorithm is evaluated using several well-known numerical test problems taken from literature. Then, it is applied to the challenging problem of finding the minimum energy configuration of a cluster of identical atoms interacting through the Lennard-Jones potential. Finding the global minimum of this function is very difficult because of the presence of a large number of local minima, which grows exponentially with molecule size. Computational results for clusters containing 8 and 9 atoms are obtained. The parallel algorithm shows significant speed-up without compromising the accuracy, when compared to the sequential PSO. |
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A New Multi-Swarm Particle Swarm Optimization and Its Application to Lennard-Jones ProblemLennard-Jones potentialParallel Particle swarm optimizationParallel ComputingParticle swarm optimization (PSO) algorithm is a modern heuristic technique for global optimization. Due to its ease of implementation, excellent effectiveness, and few parameters to adjust it has gained a lot of attention in the recent years. However, with the increasing size and computational complexity of real life optimization problems it takes long solution times and the solution quality also degrades, so there is a constant need to improve its effectiveness and robustness to find better solution in the shortest possible computational time. Parallel computing is a possible way to fulfill this requirement. In this paper we propose a multi-swarm approach to parallelize PSO algorithm (MSPSO). The performance of the proposed algorithm is evaluated using several well-known numerical test problems taken from literature. Then, it is applied to the challenging problem of finding the minimum energy configuration of a cluster of identical atoms interacting through the Lennard-Jones potential. Finding the global minimum of this function is very difficult because of the presence of a large number of local minima, which grows exponentially with molecule size. Computational results for clusters containing 8 and 9 atoms are obtained. The parallel algorithm shows significant speed-up without compromising the accuracy, when compared to the sequential PSO.Editora da UFLA2010-09-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://infocomp.dcc.ufla.br/index.php/infocomp/article/view/312INFOCOMP Journal of Computer Science; Vol. 9 No. 3 (2010): September, 2010; 52-601982-33631807-4545reponame:INFOCOMP: Jornal de Ciência da Computaçãoinstname:Universidade Federal de Lavras (UFLA)instacron:UFLAenghttps://infocomp.dcc.ufla.br/index.php/infocomp/article/view/312/297Copyright (c) 2016 INFOCOMP Journal of Computer Scienceinfo:eu-repo/semantics/openAccessDeep, KusumArya, MadhuriBarak, Shashi2015-07-29T11:44:04Zoai:infocomp.dcc.ufla.br:article/312Revistahttps://infocomp.dcc.ufla.br/index.php/infocompPUBhttps://infocomp.dcc.ufla.br/index.php/infocomp/oaiinfocomp@dcc.ufla.br||apfreire@dcc.ufla.br1982-33631807-4545opendoar:2024-05-21T19:54:31.226615INFOCOMP: Jornal de Ciência da Computação - Universidade Federal de Lavras (UFLA)true |
dc.title.none.fl_str_mv |
A New Multi-Swarm Particle Swarm Optimization and Its Application to Lennard-Jones Problem |
title |
A New Multi-Swarm Particle Swarm Optimization and Its Application to Lennard-Jones Problem |
spellingShingle |
A New Multi-Swarm Particle Swarm Optimization and Its Application to Lennard-Jones Problem Deep, Kusum Lennard-Jones potential Parallel Particle swarm optimization Parallel Computing |
title_short |
A New Multi-Swarm Particle Swarm Optimization and Its Application to Lennard-Jones Problem |
title_full |
A New Multi-Swarm Particle Swarm Optimization and Its Application to Lennard-Jones Problem |
title_fullStr |
A New Multi-Swarm Particle Swarm Optimization and Its Application to Lennard-Jones Problem |
title_full_unstemmed |
A New Multi-Swarm Particle Swarm Optimization and Its Application to Lennard-Jones Problem |
title_sort |
A New Multi-Swarm Particle Swarm Optimization and Its Application to Lennard-Jones Problem |
author |
Deep, Kusum |
author_facet |
Deep, Kusum Arya, Madhuri Barak, Shashi |
author_role |
author |
author2 |
Arya, Madhuri Barak, Shashi |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Deep, Kusum Arya, Madhuri Barak, Shashi |
dc.subject.por.fl_str_mv |
Lennard-Jones potential Parallel Particle swarm optimization Parallel Computing |
topic |
Lennard-Jones potential Parallel Particle swarm optimization Parallel Computing |
description |
Particle swarm optimization (PSO) algorithm is a modern heuristic technique for global optimization. Due to its ease of implementation, excellent effectiveness, and few parameters to adjust it has gained a lot of attention in the recent years. However, with the increasing size and computational complexity of real life optimization problems it takes long solution times and the solution quality also degrades, so there is a constant need to improve its effectiveness and robustness to find better solution in the shortest possible computational time. Parallel computing is a possible way to fulfill this requirement. In this paper we propose a multi-swarm approach to parallelize PSO algorithm (MSPSO). The performance of the proposed algorithm is evaluated using several well-known numerical test problems taken from literature. Then, it is applied to the challenging problem of finding the minimum energy configuration of a cluster of identical atoms interacting through the Lennard-Jones potential. Finding the global minimum of this function is very difficult because of the presence of a large number of local minima, which grows exponentially with molecule size. Computational results for clusters containing 8 and 9 atoms are obtained. The parallel algorithm shows significant speed-up without compromising the accuracy, when compared to the sequential PSO. |
publishDate |
2010 |
dc.date.none.fl_str_mv |
2010-09-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://infocomp.dcc.ufla.br/index.php/infocomp/article/view/312 |
url |
https://infocomp.dcc.ufla.br/index.php/infocomp/article/view/312 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
https://infocomp.dcc.ufla.br/index.php/infocomp/article/view/312/297 |
dc.rights.driver.fl_str_mv |
Copyright (c) 2016 INFOCOMP Journal of Computer Science info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2016 INFOCOMP Journal of Computer Science |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Editora da UFLA |
publisher.none.fl_str_mv |
Editora da UFLA |
dc.source.none.fl_str_mv |
INFOCOMP Journal of Computer Science; Vol. 9 No. 3 (2010): September, 2010; 52-60 1982-3363 1807-4545 reponame:INFOCOMP: Jornal de Ciência da Computação instname:Universidade Federal de Lavras (UFLA) instacron:UFLA |
instname_str |
Universidade Federal de Lavras (UFLA) |
instacron_str |
UFLA |
institution |
UFLA |
reponame_str |
INFOCOMP: Jornal de Ciência da Computação |
collection |
INFOCOMP: Jornal de Ciência da Computação |
repository.name.fl_str_mv |
INFOCOMP: Jornal de Ciência da Computação - Universidade Federal de Lavras (UFLA) |
repository.mail.fl_str_mv |
infocomp@dcc.ufla.br||apfreire@dcc.ufla.br |
_version_ |
1799874740960100352 |