论文标题
学习（重新）用于车辆路线问题的启动解决方案
Learning (Re-)Starting Solutions for Vehicle Routing Problems
论文作者
论文摘要
解决组合优化问题的关键挑战是如何指导代理（即求解器）有效探索巨大的搜索空间。传统的方法通常依赖于枚举（例如，详尽，随机或禁忌搜索），或者必须将探索限制在相当有限的区域（例如，如迭代算法中的单个路径）。在本文中，我们表明可以使用机器学习来加速探索。特别是，对价值网络进行了训练以评估解决方案候选者，该候选者在搜索空间上提供了有用的结构（即近似值表面）；然后，该值网络用于筛选解决方案，以帮助黑框优化代理初始化或重新启动，以便浏览搜索空间朝向理想的解决方案。实验表明，提出的``学会重新启动''算法在解决电容的车辆路由问题（CVRP）方面取得了有希望的结果。
A key challenge in solving a combinatorial optimization problem is how to guide the agent (i.e., solver) to efficiently explore the enormous search space. Conventional approaches often rely on enumeration (e.g., exhaustive, random, or tabu search) or have to restrict the exploration to rather limited regions (e.g., a single path as in iterative algorithms). In this paper, we show it is possible to use machine learning to speedup the exploration. In particular, a value network is trained to evaluate solution candidates, which provides a useful structure (i.e., an approximate value surface) over the search space; this value network is then used to screen solutions to help a black-box optimization agent to initialize or restart so as to navigate through the search space towards desirable solutions. Experiments demonstrate that the proposed ``Learn to Restart'' algorithm achieves promising results in solving Capacitated Vehicle Routing Problems (CVRPs).
