Large neighborhood local search optimization on graphics processing units

Thé Van Luong, N. Melab, E. Talbi
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引用次数: 20

Abstract

Local search (LS) algorithms are among the most powerful techniques for solving computationally hard problems in combinatorial optimization. These algorithms could be viewed as ¿walks through neighborhoods¿ where the walks are performed by iterative procedures that allow to move from a solution to another one in the solution space. In these heuristics, designing operators to explore large promising regions of the search space may improve the quality of the obtained solutions at the expense of a highly computationally process. Therefore, the use of graphics processing units (GPUs) provides an efficient complementary way to speed up the search. However, designing applications on GPU is still complex and many issues have to be faced. We provide a methodology to design and implement large neighborhood LS algorithms on GPU. The work has been experimented for binary problems by deploying multiple neighborhood structures. The obtained results are convincing both in terms of efficiency, quality and robustness of the provided solutions at run time.
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基于图形处理单元的大邻域局部搜索优化
局部搜索(LS)算法是解决组合优化中计算困难问题的最强大技术之一。这些算法可以被看作是“穿过街区”,其中行走是通过迭代过程执行的,允许从一个解决方案移动到另一个解决方案空间。在这些启发式算法中,设计算子来探索搜索空间中较大的有希望的区域,可能会以牺牲高计算过程为代价来提高得到的解的质量。因此,图形处理单元(gpu)的使用为加快搜索速度提供了一种有效的补充方式。然而,在GPU上设计应用程序仍然很复杂,需要面对许多问题。我们提供了一种在GPU上设计和实现大邻域LS算法的方法。这项工作已经通过部署多个邻域结构对二元问题进行了实验。所获得的结果在运行时提供的解决方案的效率、质量和健壮性方面都令人信服。
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