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A Nearest Neighbor Method with a Frequency Graph for Traveling Salesman Problem

机译:旅行商问题中带有频率图的最近邻方法

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Travelling salesman problem (TSP) is one of the famous discrete optimization problems. Due to its NP-completeness, the exact methods are not found till now. Here we use a nearest neighbor method to search the approximations with a frequency graph. The frequency graph is computed with a set of local optimal paths derived from a weighted graph. The frequencies on the edges represent the number of edges emulated from the set of local optimal paths. We believe the local optimal paths have more intersections of edges with the best circuit than the general paths do with it. Hence the edges with big frequencies may be included by the best circuit. To reduce the complexity, we use the local optimal paths with four vertices to compute the frequency graphs. These local optimal paths are computed with a four-vertex-three-line inequality. After a frequency graph is computed, we use a nearest neighbor method to find the approximations with it. The experimental results illustrate the nearest neighbor method is able to find the better approximations with the frequency graphs than those with the weighted graphs.
机译:旅行商问题(TSP)是著名的离散优化问题之一。由于其NP完整性,因此至今尚未找到确切的方法。在这里,我们使用最近邻居方法来搜索频率图的近似值。使用从加权图得出的一组局部最优路径来计算频率图。边缘上的频率表示从局部最优路径集中模拟的边缘数量。我们认为,局部最佳路径与最佳路径相比,边缘与最佳电路的交点更多。因此,最佳电路可能会包含频率较高的边缘。为了降低复杂度,我们使用具有四个顶点的局部最优路径来计算频率图。这些局部最优路径是通过四顶点三线不等式计算的。在计算了频率图之后,我们使用最近邻居方法找到它的近似值。实验结果表明,与加权图相比,频率图可以更好地逼近最近邻法。

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