GPU-based Multi-start Local Search Algorithms

Thé Van Luong 1 Nouredine Melab 1 El-Ghazali Talbi 1
1 DOLPHIN - Parallel Cooperative Multi-criteria Optimization
LIFL - Laboratoire d'Informatique Fondamentale de Lille, Inria Lille - Nord Europe
Abstract : In practice, combinatorial optimization problems are complex and computationally time-intensive. Local search algorithms are powerful heuristics which allow to significantly reduce the computation time cost of the solution exploration space. In these algorithms, the multi-start model may improve the quality and the robustness of the obtained solutions. However, solving large size and time-intensive optimization problems with this model requires a large amount of computational resources. GPU computing is recently revealed as a powerful way to harness these resources. In this paper, the focus is on the multi-start model for local search algorithms on GPU. We address its re-design, implementation and associated issues related to the GPU execution context. The preliminary results demonstrate the effectiveness of the proposed approaches and their capabilities to exploit the GPU architecture.
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Communication dans un congrès
Carlos A. Coello Coello. Learning and Intelligent Optimization, 2011, Rome, Italy. Springer, 6683, 2011, Lecture Notes in Computer Science
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Thé Van Luong, Nouredine Melab, El-Ghazali Talbi. GPU-based Multi-start Local Search Algorithms. Carlos A. Coello Coello. Learning and Intelligent Optimization, 2011, Rome, Italy. Springer, 6683, 2011, Lecture Notes in Computer Science. 〈inria-00638813〉

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