Solving Very Large Traveling Salesman Problems by SOM Parallelization on Cluster Architectures
PDCAT '05 Proceedings of the Sixth International Conference on Parallel and Distributed Computing Applications and Technologies
Simulated Annealing versus Metropolis for a TSP instance
Information Processing Letters
The Traveling Salesman Problem: A Computational Study (Princeton Series in Applied Mathematics)
The Traveling Salesman Problem: A Computational Study (Princeton Series in Applied Mathematics)
A transformation for a heterogeneous, multiple depot, multiple traveling salesman problem
ACC'09 Proceedings of the 2009 conference on American Control Conference
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Multiple traveling salesmen problem is a NP-hard problem. The method for solving the problem must arrange with reason all cities among traveling salesman and find optimal solution for every traveling salesman. In this paper, two-level hybrid algorithm is put forward to take into account these two aspects. Top level is new designed genetic algorithm to implement city exchange among traveling salesmen with the result clustered by k-means. Bottom level employs branch-and-cut and Lin-kernighan algorithms to solve exactly sub-problems for every traveling salesman. This work has both the global optimization ability from genetic algorithm and the local optimization ability from branch-and-cut.