The ant colony metaphor for multiple knapsack problem

Autores
Cena, Marcelo Guillermo; Crespo, María Liz; Kavka, Carlos; Leguizamón, Mario Guillermo
Año de publicación
2000
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
This paper presents an Ant Colony Optimisation (ACO) model for the Multiple Knapsack Problem (MKP). The ACO algorithms, as well as other evolutionary metaphors, are being applied successfully to diverse heavily constrained problems: Travelling Salesman Problem, Quadratic Assignment Problem and Bin Packing Problem. An Ant System, the first ACO algorithm that we presented in this paper, is also considered a class of multiagent distributed algorithm for combinatorial optimisation. The principle of an ACO Algorithm is adapted to the MKP. We present some results regardin its perfomance against known optimun for different instances of MKP. The obtained results show the potential power of this particular evolutionary approach for optimisation problems.
Facultad de Informática
Materia
Ciencias Informáticas
nature based metaheuristic; ant colony optimisation; subset problems; multiple knapsack problem
Algorithms
Optimization
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc/3.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/9392
Acceder
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oai_identifier_str oai:sedici.unlp.edu.ar:10915/9392
network_acronym_str SEDICI
repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling The ant colony metaphor for multiple knapsack problemCena, Marcelo GuillermoCrespo, María LizKavka, CarlosLeguizamón, Mario GuillermoCiencias Informáticasnature based metaheuristic; ant colony optimisation; subset problems; multiple knapsack problemAlgorithmsOptimizationThis paper presents an Ant Colony Optimisation (ACO) model for the Multiple Knapsack Problem (MKP). The ACO algorithms, as well as other evolutionary metaphors, are being applied successfully to diverse heavily constrained problems: Travelling Salesman Problem, Quadratic Assignment Problem and Bin Packing Problem. An Ant System, the first ACO algorithm that we presented in this paper, is also considered a class of multiagent distributed algorithm for combinatorial optimisation. The principle of an ACO Algorithm is adapted to the MKP. We present some results regardin its perfomance against known optimun for different instances of MKP. The obtained results show the potential power of this particular evolutionary approach for optimisation problems.Facultad de Informática2000info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArticulohttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://sedici.unlp.edu.ar/handle/10915/9392enginfo:eu-repo/semantics/altIdentifier/url/http://journal.info.unlp.edu.ar/wp-content/uploads/2015/papers_02/theant.pdfinfo:eu-repo/semantics/altIdentifier/issn/1666-6038info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc/3.0/Creative Commons Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-10-22T16:32:16Zoai:sedici.unlp.edu.ar:10915/9392Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-10-22 16:32:16.911SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv The ant colony metaphor for multiple knapsack problem
title The ant colony metaphor for multiple knapsack problem
spellingShingle The ant colony metaphor for multiple knapsack problem
Cena, Marcelo Guillermo
Ciencias Informáticas
nature based metaheuristic; ant colony optimisation; subset problems; multiple knapsack problem
Algorithms
Optimization
title_short The ant colony metaphor for multiple knapsack problem
title_full The ant colony metaphor for multiple knapsack problem
title_fullStr The ant colony metaphor for multiple knapsack problem
title_full_unstemmed The ant colony metaphor for multiple knapsack problem
title_sort The ant colony metaphor for multiple knapsack problem
dc.creator.none.fl_str_mv Cena, Marcelo Guillermo
Crespo, María Liz
Kavka, Carlos
Leguizamón, Mario Guillermo
author Cena, Marcelo Guillermo
author_facet Cena, Marcelo Guillermo
Crespo, María Liz
Kavka, Carlos
Leguizamón, Mario Guillermo
author_role author
author2 Crespo, María Liz
Kavka, Carlos
Leguizamón, Mario Guillermo
author2_role author
author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
nature based metaheuristic; ant colony optimisation; subset problems; multiple knapsack problem
Algorithms
Optimization
topic Ciencias Informáticas
nature based metaheuristic; ant colony optimisation; subset problems; multiple knapsack problem
Algorithms
Optimization
dc.description.none.fl_txt_mv This paper presents an Ant Colony Optimisation (ACO) model for the Multiple Knapsack Problem (MKP). The ACO algorithms, as well as other evolutionary metaphors, are being applied successfully to diverse heavily constrained problems: Travelling Salesman Problem, Quadratic Assignment Problem and Bin Packing Problem. An Ant System, the first ACO algorithm that we presented in this paper, is also considered a class of multiagent distributed algorithm for combinatorial optimisation. The principle of an ACO Algorithm is adapted to the MKP. We present some results regardin its perfomance against known optimun for different instances of MKP. The obtained results show the potential power of this particular evolutionary approach for optimisation problems.
Facultad de Informática
description This paper presents an Ant Colony Optimisation (ACO) model for the Multiple Knapsack Problem (MKP). The ACO algorithms, as well as other evolutionary metaphors, are being applied successfully to diverse heavily constrained problems: Travelling Salesman Problem, Quadratic Assignment Problem and Bin Packing Problem. An Ant System, the first ACO algorithm that we presented in this paper, is also considered a class of multiagent distributed algorithm for combinatorial optimisation. The principle of an ACO Algorithm is adapted to the MKP. We present some results regardin its perfomance against known optimun for different instances of MKP. The obtained results show the potential power of this particular evolutionary approach for optimisation problems.
publishDate 2000
dc.date.none.fl_str_mv 2000
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Articulo
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/9392
url http://sedici.unlp.edu.ar/handle/10915/9392
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://journal.info.unlp.edu.ar/wp-content/uploads/2015/papers_02/theant.pdf
info:eu-repo/semantics/altIdentifier/issn/1666-6038
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc/3.0/
Creative Commons Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc/3.0/
Creative Commons Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0)
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
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