Study of the Effectiveness of Applying the K-Means Method to Decompose Large-Scale Traveling Salesman Problems
The decomposition of the problem is based on clustering the input set of points using the well-known k- means method, combined with an algorithm for extending partial solutions within clusters. k-means clustering algorithm is examined for partitioning the input data set of large-scale TSP instances into smaller subproblems. The efficiency of using it to reduce problem size is substantiated. Based on experiments, the application of a hierarchical version of the algorithm is proposed for problems with more than one million points.