In practice, combinatorial optimization problems are com-plex 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 re-sources. GPI' computing is recently revealed as a powerful way to harness these resources. In this paper, the focus is on the multi-start model for lo-cal search algorithms on GPU. We address its re-design, implementation and associated issues related to the GPU execution context. The prelim-inary results demonstrate the effectiveness of the proposed approaches and their capabilities to exploit the GPU architecture.
展开▼
