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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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Submitted on : Monday, November 7, 2011 - 3:53:28 PM
Last modification on : Thursday, January 20, 2022 - 5:27:54 PM
Long-term archiving on: : Wednesday, February 8, 2012 - 2:30:40 AM


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  • HAL Id : inria-00638813, version 1


Thé Van Luong, Nouredine Melab, El-Ghazali Talbi. GPU-based Multi-start Local Search Algorithms. Learning and Intelligent Optimization, 2011, Rome, Italy. ⟨inria-00638813⟩



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