GPU-Based multi-start local search algorithms

Authors:
Thé Van Luong;Nouredine Melab;El-Ghazali Talbi
Affiliations:
INRIA Dolphin Project / Opac LIFL CNRS, Villeneuve d'Ascq Cedex, France;INRIA Dolphin Project / Opac LIFL CNRS, Villeneuve d'Ascq Cedex, France;INRIA Dolphin Project / Opac LIFL CNRS, Villeneuve d'Ascq Cedex, France
Venue:
LION'05 Proceedings of the 5th international conference on Learning and Intelligent Optimization
Year:
2011

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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.