Clustering Evolutionary Computation for Solving Travelling Salesman Problems

This paper proposes the methods for solving the traveling salesman problems using clustering techniques and evolutionary methods. Gaussian mixer model and K-means clustering are two clustering techniques that are considered in this paper. The traveling salesman problems are clustered in order to group the nearest nodes in the problems. Then, the evolutionary methods are applied to each cluster. The results of genetic algorithm and ant colony optimization are compared. In the last steps, a cluster connection method is proposed to find the optimal path between any two clusters. These methods are implemented and tested on the benchmark datasets. The results are compared in terms of the average minimum tour length and the average computational time. These results show that the clustering techniques are able to improve the efficiency of evolutionary methods on traveling salesman problems. Moreover, the proposed methods can be applied to other problems.