Cellular gas with active components of PSO: mutation and crossover

Autores
Villagra, Andrea; Leguizamón, Guillermo; Alba Torres, Enrique
Año de publicación
2012
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
Two powerful metaheuristics being used successfully since their creation for the resolution of optimization problems are Cellular Genetic Algorithm (cGA) and Particle Swam Optimization (PSO). Over the last years, interest in hybrid metaheuristics has risen considerably in the field of optimization. Combinations of operators and metaheuristics have provided very powerful search techniques. In this work we incorporate active components of PSO into the cGA. We replace the mutation and the crossover operators by concepts inherited by PSO internal techniques. We present four hybrid algorithms and analyze their performance using a set of different problems. The results obtained are quite satisfactory in efficacy and efficiency.
Eje: Workshop Agentes y sistemas inteligentes (WASI)
Red de Universidades con Carreras en Informática (RedUNCI)
Materia
Ciencias Informáticas
Intelligent agents
Heuristic methods
Optimization
Cellular GAs
Active Components of PSO
Mutation
Crossover
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:10915/23611
Acceder
id SEDICI_b8299fc04738cd7bebdede93c2fe14b1
oai_identifier_str oai:sedici.unlp.edu.ar:10915/23611
network_acronym_str SEDICI
repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling Cellular gas with active components of PSO: mutation and crossoverVillagra, AndreaLeguizamón, GuillermoAlba Torres, EnriqueCiencias InformáticasIntelligent agentsHeuristic methodsOptimizationCellular GAsActive Components of PSOMutationCrossoverTwo powerful metaheuristics being used successfully since their creation for the resolution of optimization problems are Cellular Genetic Algorithm (cGA) and Particle Swam Optimization (PSO). Over the last years, interest in hybrid metaheuristics has risen considerably in the field of optimization. Combinations of operators and metaheuristics have provided very powerful search techniques. In this work we incorporate active components of PSO into the cGA. We replace the mutation and the crossover operators by concepts inherited by PSO internal techniques. We present four hybrid algorithms and analyze their performance using a set of different problems. The results obtained are quite satisfactory in efficacy and efficiency.Eje: Workshop Agentes y sistemas inteligentes (WASI)Red de Universidades con Carreras en Informática (RedUNCI)2012-10info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdfhttp://sedici.unlp.edu.ar/handle/10915/23611enginfo: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:UNLP2025-11-26T09:34:05Zoai:sedici.unlp.edu.ar:10915/23611Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-11-26 09:34:05.386SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Cellular gas with active components of PSO: mutation and crossover
title Cellular gas with active components of PSO: mutation and crossover
spellingShingle Cellular gas with active components of PSO: mutation and crossover
Villagra, Andrea
Ciencias Informáticas
Intelligent agents
Heuristic methods
Optimization
Cellular GAs
Active Components of PSO
Mutation
Crossover
title_short Cellular gas with active components of PSO: mutation and crossover
title_full Cellular gas with active components of PSO: mutation and crossover
title_fullStr Cellular gas with active components of PSO: mutation and crossover
title_full_unstemmed Cellular gas with active components of PSO: mutation and crossover
title_sort Cellular gas with active components of PSO: mutation and crossover
dc.creator.none.fl_str_mv Villagra, Andrea
Leguizamón, Guillermo
Alba Torres, Enrique
author Villagra, Andrea
author_facet Villagra, Andrea
Leguizamón, Guillermo
Alba Torres, Enrique
author_role author
author2 Leguizamón, Guillermo
Alba Torres, Enrique
author2_role author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
Intelligent agents
Heuristic methods
Optimization
Cellular GAs
Active Components of PSO
Mutation
Crossover
topic Ciencias Informáticas
Intelligent agents
Heuristic methods
Optimization
Cellular GAs
Active Components of PSO
Mutation
Crossover
dc.description.none.fl_txt_mv Two powerful metaheuristics being used successfully since their creation for the resolution of optimization problems are Cellular Genetic Algorithm (cGA) and Particle Swam Optimization (PSO). Over the last years, interest in hybrid metaheuristics has risen considerably in the field of optimization. Combinations of operators and metaheuristics have provided very powerful search techniques. In this work we incorporate active components of PSO into the cGA. We replace the mutation and the crossover operators by concepts inherited by PSO internal techniques. We present four hybrid algorithms and analyze their performance using a set of different problems. The results obtained are quite satisfactory in efficacy and efficiency.
Eje: Workshop Agentes y sistemas inteligentes (WASI)
Red de Universidades con Carreras en Informática (RedUNCI)
description Two powerful metaheuristics being used successfully since their creation for the resolution of optimization problems are Cellular Genetic Algorithm (cGA) and Particle Swam Optimization (PSO). Over the last years, interest in hybrid metaheuristics has risen considerably in the field of optimization. Combinations of operators and metaheuristics have provided very powerful search techniques. In this work we incorporate active components of PSO into the cGA. We replace the mutation and the crossover operators by concepts inherited by PSO internal techniques. We present four hybrid algorithms and analyze their performance using a set of different problems. The results obtained are quite satisfactory in efficacy and efficiency.
publishDate 2012
dc.date.none.fl_str_mv 2012-10
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/10915/23611
url http://sedici.unlp.edu.ar/handle/10915/23611
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
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
_version_ 1849875726602338304
score 13.011256

Publicaciones similares