A Parallel Tabu Search for the Large-scale Quadratic Assignment Problem

Omar Abdelkafi 1, 2 Bilel Derbel 1 Arnaud Liefooghe 2
1 BONUS - Optimisation de grande taille et calcul large échelle
Inria Lille - Nord Europe, CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189
Abstract : 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.
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Omar Abdelkafi, Bilel Derbel, Arnaud Liefooghe. A Parallel Tabu Search for the Large-scale Quadratic Assignment Problem. IEEE CEC 2019 - IEEE Congress on Evolutionary Computation, Jun 2019, Wellington, New Zealand. ⟨hal-02179193⟩

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