A Novel Two-Level Hybrid Algorithm for Multiple Traveling Salesman Problems

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.

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