The ML-Constructive heuristic is a recently presented method and the first hybrid method capable of scaling up to real scale traveling salesman problems. It combines machine learning techniques and classic optimization techniques. In this paper we present improvements to the computational weight of the original deep learning model. In addition, as simpler models reduce the execution time, the possibility of adding a local-search phase is explored to further improve performance. Experimental results corroborate the quality of the proposed improvements.

中文翻译：

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旅行商问题的机器学习构造和本地搜索

ML-Constructive 启发式算法是最近提出的一种方法，也是第一种能够扩展到实际规模的旅行商问题的混合方法。它结合了机器学习技术和经典优化技术。在本文中，我们提出了对原始深度学习模型计算权重的改进。此外，由于更简单的模型减少了执行时间，因此探索了添加本地搜索阶段以进一步提高性能的可能性。实验结果证实了所提出的改进的质量。