A genetic algorithm for the generalized traveling salesman problem
A genetic algorithm for the generalized traveling salesman problem
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DOI：
10.1109/cec.2007.4424769
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发表时间：
2007-09
期刊：
影响因子：
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通讯作者：
M. Tasgetiren;P. Suganthan;Q. Pan;Yun-Chia Liang
中科院分区：
文献类型：
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作者：
M. Tasgetiren;P. Suganthan;Q. Pan;Yun-Chia Liang
在旅行商问题中，如果节点集被划分为簇，使得每个簇中的单个节点可以被访问，则该问题被称为广义旅行商问题，其中目标是找到仅通过每个簇中的单个节点的具有最小成本的旅行。本文提出了一种遗传算法来解决一组基准实例上的问题。遗传算法与迭代局部搜索相结合，进一步提高了解的质量。提出了一些加速方法来加速贪婪节点插入。遗传算法进行了测试的一组基准实例的对称距离范围从51到442节点的文献。计算结果表明，所提出的遗传算法是迄今为止在文献中的最佳性能算法的解的质量。
In a traveling salesman problem, if the set of nodes is divided into clusters so that a single node from each cluster can be visited, then the problem is known as the generalized traveling salesman problem where the objective is to find a tour with minimum cost passing through only a single node from each cluster. In this paper, a genetic algorithm is presented to solve the problem on a set of benchmark instances. The genetic algorithm is hybridized with an iterated local search to further improve the solution quality. Some speed-up methods are presented to accelerate the greedy node insertions. The genetic algorithm is tested on a set of benchmark instances with symmetric distances ranging from 51 to 442 nodes from the literature. Computational results show that the proposed genetic algorithm is the best performing algorithm so far in the literature in terms of solution quality.