Parallel ant algorithms for the minimum tardy task problem

Ant Colony Optimization algorithms are intrinsically distributed algorithms where independent agents are in charge of building solutions. Stigmergy or indirect communication is the way in which each agent learns from the experience of the whole colony. However, explicit communication and parallel mo...

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Autores principales: Alba Torres, Enrique, Leguizamón, Guillermo, Ordoñez, Guillermo
Formato: Objeto de conferencia
Lenguaje:Inglés
Publicado: 2004
Materias:
Ciencias Informáticas
Optimization
Hormigas
Parallel
Models
ARTIFICIAL INTELLIGENCE
Intelligent agents
ant colony optimization
parallel models
minimum tardy task problem
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/22556
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-22556
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ciencias Informáticas
Optimization
Hormigas
Parallel
Models
ARTIFICIAL INTELLIGENCE
Intelligent agents
ant colony optimization
parallel models
minimum tardy task problem
spellingShingle Ciencias Informáticas
Optimization
Hormigas
Parallel
Models
ARTIFICIAL INTELLIGENCE
Intelligent agents
ant colony optimization
parallel models
minimum tardy task problem
Alba Torres, Enrique
Leguizamón, Guillermo
Ordoñez, Guillermo
Parallel ant algorithms for the minimum tardy task problem
topic_facet Ciencias Informáticas
Optimization
Hormigas
Parallel
Models
ARTIFICIAL INTELLIGENCE
Intelligent agents
ant colony optimization
parallel models
minimum tardy task problem
description Ant Colony Optimization algorithms are intrinsically distributed algorithms where independent agents are in charge of building solutions. Stigmergy or indirect communication is the way in which each agent learns from the experience of the whole colony. However, explicit communication and parallel models of ACO can be implemented directly on different parallel platforms. We do so, and apply the resulting algorithms to the Minimum Tardy Task Problem (MTTP), a scheduling problem that has been faced with other metaheuristics, e.g., evolutionary algorithms and canonical ant algorithms. The aim of this article is twofold. First, it shows a new instance generator for MTTP to deal with the concept of “problem class”; second, it reports some preliminary results of the implementation of two type of parallel ACO algorithms for solving novel and larger instances of MTTP.
format Objeto de conferencia
Objeto de conferencia
author Alba Torres, Enrique
Leguizamón, Guillermo
Ordoñez, Guillermo
author_facet Alba Torres, Enrique
Leguizamón, Guillermo
Ordoñez, Guillermo
author_sort Alba Torres, Enrique
title Parallel ant algorithms for the minimum tardy task problem
title_short Parallel ant algorithms for the minimum tardy task problem
title_full Parallel ant algorithms for the minimum tardy task problem
title_fullStr Parallel ant algorithms for the minimum tardy task problem
title_full_unstemmed Parallel ant algorithms for the minimum tardy task problem
title_sort parallel ant algorithms for the minimum tardy task problem
publishDate 2004
url http://sedici.unlp.edu.ar/handle/10915/22556
work_keys_str_mv AT albatorresenrique parallelantalgorithmsfortheminimumtardytaskproblem
AT leguizamonguillermo parallelantalgorithmsfortheminimumtardytaskproblem
AT ordonezguillermo parallelantalgorithmsfortheminimumtardytaskproblem
bdutipo_str Repositorios
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