Détail du document
Identifiant

oai:HAL:hal-02179193v1

Sujet
Big Optimization Quadratic assignment problem Iterative tabu search [INFO.INFO-AI]Computer Science [cs... [INFO.INFO-DC]Computer Science [cs... [INFO.INFO-RO]Computer Science [cs...
Auteur
Abdelkafi, Omar Derbel, Bilel Liefooghe, Arnaud
Langue
en
Editeur

HAL CCSD

Catégorie

CNRS - Centre national de la recherche scientifique

Année

2019

Date de référencement

07/10/2023

Mots clés
[info study [cs tabu science parallel
Métrique

Résumé

International audience; 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.

Abdelkafi, Omar,Derbel, Bilel,Liefooghe, Arnaud, 2019, A Parallel Tabu Search for the Large-scale Quadratic Assignment Problem, HAL CCSD

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