Solving traveling salesman problem via clustering and a new algorithm for merging tours

Traditional methods for solving the traveling salesman problem are not effective for high-dimensional problems due to their high computational complexity. One of the most effective ways to solve this problem is the decomposition approach, which includes three main stages: clustering vertices, solving subproblems within each cluster and then merging the obtained solutions into a final solution. This article focuses on the third stage — merging cycles of solving subproblems — since this stage is not always given sufficient attention, which leads to less accurate final solutions of the problem. The paper proposes a new modified Sigal algorithm for merging cycles. To evaluate its effectiveness, it is compared with two algorithms for merging cycles — the method of connecting midpoints of edges and an algorithm based on closeness of cluster centroids. The dependence of quality of solving subproblems on algorithms used for merging cycles is investigated. Sigal’s modified algorithm performs pairwise clustering and minimizes total distance. The centroid method focuses on connecting clusters based on closeness of centroids, and an algorithm using mid-points estimates the distance between mid-points of edges. Two types of clustering — k-means and affinity propagation — were also considered. Numerical experiments were performed using the TSPLIB dataset with different numbers of cities and topologies to test effectiveness of proposed algorithm. The study analyzes errors caused by the order in which clusters were merged, the quality of solving subtasks and number of clusters. Experiments show that the modified Sigal algorithm has the smallest median final distance and the most stable results compared to other methods. Results indicate that the quality of the final solution obtained using the modified Sigal algorithm is more stable depending on the sequence of merging clusters. Improving the quality of solving subproblems usually results in linear improvement of the final solution, but the pooling algorithm rarely affects the degree of this improvement.