The ant colony metaphor for multiple knapsack problem

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 P...

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Detalles Bibliográficos
Autores: Cena, Marcelo Guillermo, Crespo, María Liz, Kavka, Carlos, Leguizamón, Mario Guillermo
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2000
País:Argentina
Institución:Universidad Nacional de La Plata
Repositorio:SEDICI (UNLP)
Idioma:inglés
OAI Identifier:oai:sedici.unlp.edu.ar:10915/9392
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/9392
Access Level:acceso abierto
Palabra clave:Ciencias Informáticas
nature based metaheuristic; ant colony optimisation; subset problems; multiple knapsack problem
Algorithms
Optimization
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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:UNLP2024-05-08T12:30:00Zoai: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:13292024-05-08 12:30:00.744SEDICI (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
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
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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
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info:eu-repo/semantics/altIdentifier/issn/1666-6038
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
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Creative Commons Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0)
eu_rights_str_mv openAccess
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instname:Universidad Nacional de La Plata
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