A Parallel Tabu Search for the Large-scale ...
Type de document :
Communication dans un congrès avec actes
Titre :
A Parallel Tabu Search for the Large-scale Quadratic Assignment Problem
Auteur(s) :
Abdelkafi, Omar [Auteur]
Optimisation de grande taille et calcul large échelle [BONUS]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Derbel, Bilel [Auteur]
Optimisation de grande taille et calcul large échelle [BONUS]
Liefooghe, Arnaud [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Optimisation de grande taille et calcul large échelle [BONUS]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Derbel, Bilel [Auteur]
Optimisation de grande taille et calcul large échelle [BONUS]
Liefooghe, Arnaud [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Titre de la manifestation scientifique :
IEEE CEC 2019 - IEEE Congress on Evolutionary Computation
Ville :
Wellington
Pays :
Nouvelle-Zélande
Date de début de la manifestation scientifique :
2019-06-10
Mot(s)-clé(s) en anglais :
Big Optimization
Quadratic assignment problem
Iterative tabu search
Quadratic assignment problem
Iterative tabu search
Discipline(s) HAL :
Informatique [cs]/Intelligence artificielle [cs.AI]
Informatique [cs]/Calcul parallèle, distribué et partagé [cs.DC]
Computer Science [cs]/Operations Research [math.OC]
Informatique [cs]/Calcul parallèle, distribué et partagé [cs.DC]
Computer Science [cs]/Operations Research [math.OC]
Résumé en anglais : [en]
Parallelization is an important paradigm for solving massive optimization problems. Understanding how to fully benefit form the aggregated computing power and what makes a parallel strategy successful is a difficult issue. ...
Lire la suite >Parallelization is an important paradigm for solving massive optimization problems. Understanding how to fully benefit form the aggregated computing power and what makes a parallel strategy successful is a difficult issue. In this study, we propose a simple parallel iterative tabu search (PITS) and study its effectiveness with respect to different experimental settings. Using the quadratic assignment problem (QAP) as a case study, we first consider different small-and medium-size instances from the literature and then tackle a large-size instance that was rarely considered due the its inherent solving difficulty. In particular, we show that a balance between the number of function evaluations each parallel process is allowed to perform before resuming the search is a critical issue to obtain an improved quality.Lire moins >
Lire la suite >Parallelization is an important paradigm for solving massive optimization problems. Understanding how to fully benefit form the aggregated computing power and what makes a parallel strategy successful is a difficult issue. In this study, we propose a simple parallel iterative tabu search (PITS) and study its effectiveness with respect to different experimental settings. Using the quadratic assignment problem (QAP) as a case study, we first consider different small-and medium-size instances from the literature and then tackle a large-size instance that was rarely considered due the its inherent solving difficulty. In particular, we show that a balance between the number of function evaluations each parallel process is allowed to perform before resuming the search is a critical issue to obtain an improved quality.Lire moins >
Langue :
Anglais
Comité de lecture :
Oui
Audience :
Internationale
Vulgarisation :
Non
Collections :
Source :
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