不精确TSP求解器的任意时间行为和自动算法选择的前景
Anytime Behavior of Inexact TSP Solvers and Perspectives for Automated Algorithm Selection
摘要：
旅行商问题（TSP）可以说是最著名的NP难组合优化问题之一。两个复杂的启发式求解器LKH和EAX以及各自的（重新启动）变体设法计算接近最优甚至最优的解决方案，也适用于在合理的时间内具有数千个节点的大型实例。在这项工作中，我们扩展了现有的基准测试研究，解决任何时候的行为不精确TSP求解器的经验运行时分布的基础上，导致求解器的行为和各自的关系，以问题的硬度增加了解。事实证明，性能排名的求解器是高度依赖于集中的近似质量。对性能交叉点的洞察为根据实例特征构建混合求解器提供了巨大的潜力。此外，为随时性能和相应的性能指标定制的实例功能将通过包含有关求解器质量的全面信息来高度改进自动算法选择模型。
英文摘要：
The Traveling-Salesperson-Problem (TSP) is arguably one of the best-known NP-hard combinatorial optimization problems. The two sophisticated heuristic solvers LKH and EAX and respective (restart) variants manage to calculate close-to optimal or even optimal solutions, also for large instances with several thousand nodes in reasonable time. In this work we extend existing benchmarking studies by addressing anytime behaviour of inexact TSP solvers based on empirical runtime distributions leading to an increased understanding of solver behaviour and the respective relation to problem hardness. It turns out that performance ranking of solvers is highly dependent on the focused approximation quality. Insights on intersection points of performances offer huge potential for the construction of hybridized solvers depending on instance features. Moreover, instance features tailored to anytime performance and corresponding performance indicators will highly improve automated algorithm selection models by including comprehensive information on solver quality.
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