The ant colony metaphor in continuous spaces using boundary search

Autores
Leguizamón, Guillermo
Año de publicación
2003
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
This paper presents an application of the ant colony metaphor for continuous space optimization problems. The ant algortihm proposed works following the principle of the ant colony approach, i.e., a population of agents iteratively, cooperatively, and independently search for a solution. Each ant in the distributed algorithm applies a local search operator which explores the neighborhood region of a particular point in the search space (individual search level). The local search operator is designed for exploring the boundary between the feasible and infeasible search space. On the other hand, each ant obtains global information from the colony in order to exploit the more promising regions of the search space (cooperation level). The ant colony based algorithm presented here was successfully applied to two widely studied and interesting constrained numerical optimization test cases.
Eje: Agentes y Sistemas Inteligentes (ASI)
Red de Universidades con Carreras en Informática (RedUNCI)
Materia
Ciencias Informáticas
Hormigas
Optimization
Algorithms
ARTIFICIAL INTELLIGENCE
Intelligent agents
ant colony optimization
evolutionary algorithms
constraint optimization problems
boundary search
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:10915/22787
Acceder
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oai_identifier_str oai:sedici.unlp.edu.ar:10915/22787
network_acronym_str SEDICI
repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling The ant colony metaphor in continuous spaces using boundary searchLeguizamón, GuillermoCiencias InformáticasHormigasOptimizationAlgorithmsARTIFICIAL INTELLIGENCEIntelligent agentsant colony optimizationevolutionary algorithmsconstraint optimization problemsboundary searchThis paper presents an application of the ant colony metaphor for continuous space optimization problems. The ant algortihm proposed works following the principle of the ant colony approach, i.e., a population of agents iteratively, cooperatively, and independently search for a solution. Each ant in the distributed algorithm applies a local search operator which explores the neighborhood region of a particular point in the search space (individual search level). The local search operator is designed for exploring the boundary between the feasible and infeasible search space. On the other hand, each ant obtains global information from the colony in order to exploit the more promising regions of the search space (cooperation level). The ant colony based algorithm presented here was successfully applied to two widely studied and interesting constrained numerical optimization test cases.Eje: Agentes y Sistemas Inteligentes (ASI)Red de Universidades con Carreras en Informática (RedUNCI)2003-10info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf740-751http://sedici.unlp.edu.ar/handle/10915/22787enginfo: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:UNLP2025-10-22T16:36:42Zoai:sedici.unlp.edu.ar:10915/22787Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-10-22 16:36:42.925SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv The ant colony metaphor in continuous spaces using boundary search
title The ant colony metaphor in continuous spaces using boundary search
spellingShingle The ant colony metaphor in continuous spaces using boundary search
Leguizamón, Guillermo
Ciencias Informáticas
Hormigas
Optimization
Algorithms
ARTIFICIAL INTELLIGENCE
Intelligent agents
ant colony optimization
evolutionary algorithms
constraint optimization problems
boundary search
title_short The ant colony metaphor in continuous spaces using boundary search
title_full The ant colony metaphor in continuous spaces using boundary search
title_fullStr The ant colony metaphor in continuous spaces using boundary search
title_full_unstemmed The ant colony metaphor in continuous spaces using boundary search
title_sort The ant colony metaphor in continuous spaces using boundary search
dc.creator.none.fl_str_mv Leguizamón, Guillermo
author Leguizamón, Guillermo
author_facet Leguizamón, Guillermo
author_role author
dc.subject.none.fl_str_mv Ciencias Informáticas
Hormigas
Optimization
Algorithms
ARTIFICIAL INTELLIGENCE
Intelligent agents
ant colony optimization
evolutionary algorithms
constraint optimization problems
boundary search
topic Ciencias Informáticas
Hormigas
Optimization
Algorithms
ARTIFICIAL INTELLIGENCE
Intelligent agents
ant colony optimization
evolutionary algorithms
constraint optimization problems
boundary search
dc.description.none.fl_txt_mv This paper presents an application of the ant colony metaphor for continuous space optimization problems. The ant algortihm proposed works following the principle of the ant colony approach, i.e., a population of agents iteratively, cooperatively, and independently search for a solution. Each ant in the distributed algorithm applies a local search operator which explores the neighborhood region of a particular point in the search space (individual search level). The local search operator is designed for exploring the boundary between the feasible and infeasible search space. On the other hand, each ant obtains global information from the colony in order to exploit the more promising regions of the search space (cooperation level). The ant colony based algorithm presented here was successfully applied to two widely studied and interesting constrained numerical optimization test cases.
Eje: Agentes y Sistemas Inteligentes (ASI)
Red de Universidades con Carreras en Informática (RedUNCI)
description This paper presents an application of the ant colony metaphor for continuous space optimization problems. The ant algortihm proposed works following the principle of the ant colony approach, i.e., a population of agents iteratively, cooperatively, and independently search for a solution. Each ant in the distributed algorithm applies a local search operator which explores the neighborhood region of a particular point in the search space (individual search level). The local search operator is designed for exploring the boundary between the feasible and infeasible search space. On the other hand, each ant obtains global information from the colony in order to exploit the more promising regions of the search space (cooperation level). The ant colony based algorithm presented here was successfully applied to two widely studied and interesting constrained numerical optimization test cases.
publishDate 2003
dc.date.none.fl_str_mv 2003-10
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/10915/22787
url http://sedici.unlp.edu.ar/handle/10915/22787
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
740-751
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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