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arXiv 1003.5330cs.DS
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广义旅行商问题的Lin-Kernighan启发式适应算法

Lin-Kernighan Heuristic Adaptations for the Generalized Traveling Salesman Problem

Daniel Karapetyan, Gregory Gutin

摘要:

Lin-Kernighan启发式算法是求解旅行商问题(TSP)最成功的算法之一。它也被证明是有效的应用到其他一些问题。在本文中,我们讨论了可能的适应广义旅行商问题(GTSP)的TSP算法,并专注于林-Kernighan算法的情况下。首先,我们提供了一个易于理解的描述原来的Lin-Kernighan启发式。然后,我们提出了几个适应,既琐碎的和复杂的。最后,我们进行了一个公平的竞争之间的所有变化的Lin-Kernighan适应和其他一些GTSP算法。看来,我们的适应林Kernighan算法的GTSP再现了原来的启发式的成功。我们的自适应的不同变化在解决方案质量和运行时间之间的广泛权衡中优于所有其他算法,使Lin-Kernighan成为最先进的GTSP局部搜索。

英文摘要:

The Lin-Kernighan heuristic is known to be one of the most successful heuristics for the Traveling Salesman Problem (TSP). It has also proven its efficiency in application to some other problems. In this paper we discuss possible adaptations of TSP heuristics for the Generalized Traveling Salesman Problem (GTSP) and focus on the case of the Lin-Kernighan algorithm. At first, we provide an easy-to-understand description of the original Lin-Kernighan heuristic. Then we propose several adaptations, both trivial and complicated. Finally, we conduct a fair competition between all the variations of the Lin-Kernighan adaptation and some other GTSP heuristics. It appears that our adaptation of the Lin-Kernighan algorithm for the GTSP reproduces the success of the original heuristic. Different variations of our adaptation outperform all other heuristics in a wide range of trade-offs between solution quality and running time, making Lin-Kernighan the state-of-the-art GTSP local search.

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。

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