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Conference Papers Year : 2011

GPU-based Multi-start Local Search Algorithms

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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Dates and versions

inria-00638813 , version 1 (07-11-2011)

Identifiers

  • HAL Id : inria-00638813 , version 1

Cite

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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