A GPU-based iterated tabu search for solving the quadratic 3-dimensional assignment problem

The Van Luong, Lakhdar Loukil, Nouredine Melab, El-Ghazali Talbi
INRIA Lille Nord Europe – CNRS/LIFL Laboratory, 40, avenue Halley, Bt. A, Park Plaza, 59650 Villeneuve d’Asq, France
IEEE/ACS International Conference on Computer Systems and Applications (AICCSA), 2010


   title={A GPU-based iterated tabu search for solving the quadratic 3-dimensional assignment problem},

   author={Loukil, L. and Melab, N. and Talbi, E.G.},

   booktitle={Computer Systems and Applications (AICCSA), 2010 IEEE/ACS International Conference on},





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The quadratic 3-dimensional assignment problem (Q3AP) is an extension of the well-known NP-hard quadratic assignment problem. It has been proved to be one of the most difficult combinatorial optimization problems. Local search (LS) algorithms are a class of heuristics which have been successfully applied to solve such hard optimization problem. These methods handle with a single solution iteratively improved by exploring its neighborhood in the solution space. In this paper, we propose an iterated tabu search for solving the Q3AP. The design of this algorithm is essentially based on a new large neighborhood structure. Indeed, in LS heuristics, designing operators to explore large promising regions of the search space may improve the quality of the obtained solutions. However, designing such neighborhood is at the expense of a highly computationally process. Therefore, the use of graphics processing units (GPUs) provides an efficient complementary way to speed up the search. The proposed GPU-based iterated tabu search has been experimented on 5 different Q3AP instances. The obtained results are convincing both in terms of efficiency, quality and robustness of the provided solutions at run time.
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