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
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/9392
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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:UNLP2025-09-29T10:50:40Zoai: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:13292025-09-29 10:50:40.242SEDICI (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 |
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article |
status_str |
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dc.identifier.none.fl_str_mv |
http://sedici.unlp.edu.ar/handle/10915/9392 |
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http://sedici.unlp.edu.ar/handle/10915/9392 |
dc.language.none.fl_str_mv |
eng |
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eng |
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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) |
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openAccess |
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http://creativecommons.org/licenses/by-nc/3.0/ Creative Commons Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0) |
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