标题： 基于统一神经引导的级联大规模TSP求解：连接局部和种群搜索 显示英文标题

标题： Cascaded Large-Scale TSP Solving with Unified Neural Guidance: Bridging Local and Population-based Search

摘要： 旅行商问题（TSP）是一个基本的NP难优化问题。 这项工作提出了UNiCS，一种新型的统一神经引导级联求解器，用于解决大规模TSP实例。 UNiCS包括一个局部搜索（LS）阶段和一个基于种群的搜索（PBS）阶段，均由一个称为统一神经引导（UNG）的学习组件引导。 具体而言，UNG在两个阶段中引导解决方案的生成，并确定适当的阶段转换时间，以有效结合LS和PBS的互补优势。 尽管仅在相对小规模的TSP实例的简单分布上进行训练，UNiCS能够有效地推广到包含更大规模实例（10,000-71,009个节点）的挑战性TSP基准测试中，这些实例的节点分布在训练期间完全未见过。 在大规模TSP实例上的实验结果表明，UNiCS始终优于最先进的方法，在各种运行时间预算下其优势保持一致。 显示英文摘要

摘要： The traveling salesman problem (TSP) is a fundamental NP-hard optimization problem. This work presents UNiCS, a novel unified neural-guided cascaded solver for solving large-scale TSP instances. UNiCS comprises a local search (LS) phase and a population-based search (PBS) phase, both guided by a learning component called unified neural guidance (UNG). Specifically, UNG guides solution generation across both phases and determines appropriate phase transition timing to effectively combine the complementary strengths of LS and PBS. While trained only on simple distributions with relatively small-scale TSP instances, UNiCS generalizes effectively to challenging TSP benchmarks containing much larger instances (10,000-71,009 nodes) with diverse node distributions entirely unseen during training. Experimental results on the large-scale TSP instances demonstrate that UNiCS consistently outperforms state-of-the-art methods, with its advantage remaining consistent across various runtime budgets.