Global numerical optimization with a bi-population particle swarm optimizer

Autores
Esquivel, Susana Cecilia; Cagnina, Leticia
Año de publicación
2007
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
This paper presents an enhanced Particle Swarm Optimizer approach, which is designed to solve numerical unconstrained optimization problems. The approach incorporates a dual population in an attempt to overcome the problem of premature convergence to local optima. The proposed algorithm is validated using standard test functions (unimodal, multi-modal, separable and nonseparable) taken from the specialized literature. The results are compared with values obtained by an algorithm representative of the state-of-the-art in the area. Our preliminary results indicate that our proposed approach is a competitive alternative to solve global optimization problems.
Este artículo presenta un nuevo algoritmo Particle Swarm Optimizer, diseñado para resolver problemas de optimización numéricos sin restricciones, que incorpora una población dual para intentar solucionar el problema de convergencia prematura en óptimos locales. El algoritmo propuesto es validado usando funciones de prueba estandard (unimodales, multi-modales, separables y no separables) tomadas de la literatura especializada. Los resultados son comparados con los valores obtenidos por un algoritmo representativo del estado del arte en el área. Los resultados preliminares indican que la propuesta es una alternativa competitiva para resolver problemas de optimización global.
VIII Workshop de Agentes y Sistemas Inteligentes
Red de Universidades con Carreras en Informática (RedUNCI)
Materia
Ciencias Informáticas
Informática
Optimization
funciones sin restricciones
ARTIFICIAL INTELLIGENCE
particle swarm optimizer
unconstrained functions
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/23180

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repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling Global numerical optimization with a bi-population particle swarm optimizerEsquivel, Susana CeciliaCagnina, LeticiaCiencias InformáticasInformáticaOptimizationfunciones sin restriccionesARTIFICIAL INTELLIGENCEparticle swarm optimizerunconstrained functionsThis paper presents an enhanced Particle Swarm Optimizer approach, which is designed to solve numerical unconstrained optimization problems. The approach incorporates a dual population in an attempt to overcome the problem of premature convergence to local optima. The proposed algorithm is validated using standard test functions (unimodal, multi-modal, separable and nonseparable) taken from the specialized literature. The results are compared with values obtained by an algorithm representative of the state-of-the-art in the area. Our preliminary results indicate that our proposed approach is a competitive alternative to solve global optimization problems.Este artículo presenta un nuevo algoritmo Particle Swarm Optimizer, diseñado para resolver problemas de optimización numéricos sin restricciones, que incorpora una población dual para intentar solucionar el problema de convergencia prematura en óptimos locales. El algoritmo propuesto es validado usando funciones de prueba estandard (unimodales, multi-modales, separables y no separables) tomadas de la literatura especializada. Los resultados son comparados con los valores obtenidos por un algoritmo representativo del estado del arte en el área. Los resultados preliminares indican que la propuesta es una alternativa competitiva para resolver problemas de optimización global.VIII Workshop de Agentes y Sistemas InteligentesRed de Universidades con Carreras en Informática (RedUNCI)2007-10info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf1452-1464http://sedici.unlp.edu.ar/handle/10915/23180enginfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/2.5/ar/Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T10:55:21Zoai:sedici.unlp.edu.ar:10915/23180Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 10:55:21.85SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Global numerical optimization with a bi-population particle swarm optimizer
title Global numerical optimization with a bi-population particle swarm optimizer
spellingShingle Global numerical optimization with a bi-population particle swarm optimizer
Esquivel, Susana Cecilia
Ciencias Informáticas
Informática
Optimization
funciones sin restricciones
ARTIFICIAL INTELLIGENCE
particle swarm optimizer
unconstrained functions
title_short Global numerical optimization with a bi-population particle swarm optimizer
title_full Global numerical optimization with a bi-population particle swarm optimizer
title_fullStr Global numerical optimization with a bi-population particle swarm optimizer
title_full_unstemmed Global numerical optimization with a bi-population particle swarm optimizer
title_sort Global numerical optimization with a bi-population particle swarm optimizer
dc.creator.none.fl_str_mv Esquivel, Susana Cecilia
Cagnina, Leticia
author Esquivel, Susana Cecilia
author_facet Esquivel, Susana Cecilia
Cagnina, Leticia
author_role author
author2 Cagnina, Leticia
author2_role author
dc.subject.none.fl_str_mv Ciencias Informáticas
Informática
Optimization
funciones sin restricciones
ARTIFICIAL INTELLIGENCE
particle swarm optimizer
unconstrained functions
topic Ciencias Informáticas
Informática
Optimization
funciones sin restricciones
ARTIFICIAL INTELLIGENCE
particle swarm optimizer
unconstrained functions
dc.description.none.fl_txt_mv This paper presents an enhanced Particle Swarm Optimizer approach, which is designed to solve numerical unconstrained optimization problems. The approach incorporates a dual population in an attempt to overcome the problem of premature convergence to local optima. The proposed algorithm is validated using standard test functions (unimodal, multi-modal, separable and nonseparable) taken from the specialized literature. The results are compared with values obtained by an algorithm representative of the state-of-the-art in the area. Our preliminary results indicate that our proposed approach is a competitive alternative to solve global optimization problems.
Este artículo presenta un nuevo algoritmo Particle Swarm Optimizer, diseñado para resolver problemas de optimización numéricos sin restricciones, que incorpora una población dual para intentar solucionar el problema de convergencia prematura en óptimos locales. El algoritmo propuesto es validado usando funciones de prueba estandard (unimodales, multi-modales, separables y no separables) tomadas de la literatura especializada. Los resultados son comparados con los valores obtenidos por un algoritmo representativo del estado del arte en el área. Los resultados preliminares indican que la propuesta es una alternativa competitiva para resolver problemas de optimización global.
VIII Workshop de Agentes y Sistemas Inteligentes
Red de Universidades con Carreras en Informática (RedUNCI)
description This paper presents an enhanced Particle Swarm Optimizer approach, which is designed to solve numerical unconstrained optimization problems. The approach incorporates a dual population in an attempt to overcome the problem of premature convergence to local optima. The proposed algorithm is validated using standard test functions (unimodal, multi-modal, separable and nonseparable) taken from the specialized literature. The results are compared with values obtained by an algorithm representative of the state-of-the-art in the area. Our preliminary results indicate that our proposed approach is a competitive alternative to solve global optimization problems.
publishDate 2007
dc.date.none.fl_str_mv 2007-10
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
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http://purl.org/coar/resource_type/c_5794
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status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/23180
url http://sedici.unlp.edu.ar/handle/10915/23180
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
dc.format.none.fl_str_mv application/pdf
1452-1464
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