Results of Computational Experiment for Solving the Minimum Traveling Salesman Problem by the Cycle Merging Algorithm

The traveling salesman problem is an important combinatorial optimization problem, which consists in finding the shortest path that passes through each city exactly once and ends at the starting point. Despite its simple formulation, this problem turns out to be computationally complex, and its exact solution requires significant computational resources. In this regard, the search for efficient algorithms to solve the traveling salesman problem remains an urgent task in the context of modern technological challenges. In this paper we present the results of a computational experiment that gives an insight into the quality of the cycle merging algorithm for the minimum traveling salesman problem. The quality of the cycle merging algorithm is investigated on seven sets of instances of the traveling salesman problem. The results obtained are compared with the results of computational experiments for some known heuristics. The analysis allows us to say that the cycle merging algorithm shows relatively good results for all tested sets and can be used for a wide range of instances of the traveling salesman problem as a relatively fast heuristic of good enough quality.