加速旅行商和最小顶点覆盖的一般Bet-and-Run策略
A Generic Bet-and-run Strategy for Speeding Up Traveling Salesperson and Minimum Vertex Cover
摘要：
改进优化算法的常用策略是当认为算法陷入搜索空间的较低部分时重新启动算法。然而，虽然已经针对特定问题（和特定算法）开发了特定的重启策略，但重启通常不被视为加速优化算法的通用工具。事实上，许多优化算法根本不使用重启。最近，在混合整数规划的上下文中引入了“下注并运行”，其中首先进行一些具有随机初始条件的短期运行，然后继续进行其中最有希望的运行。在这篇文章中，我们考虑了两个经典的NP完全组合优化问题，旅行商和最小顶点覆盖，并研究了不同的投注和运行策略的有效性。特别是，我们的重启策略不考虑任何问题知识，也不适合优化算法。因此，它们可以现成使用。我们观察到，这些问题的最先进的解决方案可以从标准基准测试实例的重新启动中受益匪浅。
英文摘要：
A common strategy for improving optimization algorithms is to restart the algorithm when it is believed to be trapped in an inferior part of the search space. However, while specific restart strategies have been developed for specific problems (and specific algorithms), restarts are typically not regarded as a general tool to speed up an optimization algorithm. In fact, many optimization algorithms do not employ restarts at all. Recently, "bet-and-run" was introduced in the context of mixed-integer programming, where first a number of short runs with randomized initial conditions is made, and then the most promising run of these is continued. In this article, we consider two classical NP-complete combinatorial optimization problems, traveling salesperson and minimum vertex cover, and study the effectiveness of different bet-and-run strategies. In particular, our restart strategies do not take any problem knowledge into account, nor are tailored to the optimization algorithm. Therefore, they can be used off-the-shelf. We observe that state-of-the-art solvers for these problems can benefit significantly from restarts on standard benchmark instances.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
