GPU-based Multi-start Local Search Algorithms

The Van Luong, Nouredine Melab, El-Ghazali Talbi
INRIA Dolphin Project / Opac LIFL CNRS, 40 avenue Halley, 59650 Villeneuve d’Ascq Cedex, France
inria-00638813, 2011




   title={GPU-based Multi-start Local Search Algorithms},

   author={Luong, Th{‘e} Van and Melab, Nouredine and Talbi, El-Ghazali},


   affiliation={DOLPHIN – INRIA Lille – Nord Europe – INRIA – CNRS : UMR8022 – Universit{‘e} des Sciences et Technologies de Lille – Lille I},

   booktitle={Learning and Intelligent Optimization},


   address={Rome, Italie},


   editor={Carlos A. Coello Coello},

   series={Lecture Notes in Computer Science},





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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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