A Hybrid Approach Based on Ant Colony and Firefly Algorithm for Traveling Salesman Problem

Parameters have always been the core part of an algorithm, but good parameters are not immutable. They change as the problem changes, so how to set the optimal parameters according to the size of the problem has always been a difficult problem in the field of algorithm. Traditional ant colony algorithm has some problems such as difficulty in determining initial parameters and falling into local optimization. A hybrid ant colony algorithm for searching initial parameters was proposed. The optimal parameter array of the ant colony algorithm is obtained by introducing the firefly algorithm (FA) to optimize the initial parameters. The hybrid ant colony algorithm after parameter optimization to solve the traditional traveling salesman problem (TSP) is used. The results show that the running time and shortest tour path of the hybrid ant colony algorithm with the calculated parameters are shorter. In the last, the number of cities is continuously reduced to repeatedly search the optimal parameters under different difficulty problems. The experimental results have certain reference significance for the follow-up research of the hybrid ant colony algorithm.