An improved ant colony optimization algorithm for solving TSP

Abstract

The basic ant colony optimization (ACO) algorithm takes on a longer computing time in the search process and is prone to fall into local optimal solutions, an improved ACO (CEULACO) algorithm is proposed in this paper. In the CEULAC algorithm, the direction guidance and real variable function are used to initialize pheromone concentration according to the path information of undirected graph. The pheromone dynamic evaporation rate strategy is proposed to control the pheromone evaporation in order to increase the global search capability and accelerate the convergence speed. An adaptive dynamic factor is introduced into pheromone updating rule to control the updating proportion of pheromone concentration with optimal solution in single iteration. And the local search strategy is used to improve the quality of the solution and select the current optimal path for global updating the pheromone in order to save some computing time and not reduce the searching efficiency. Some traveling salesman problems are selected to test the performance of the CEULACO algorithm. The simulation experiments show that the improved ACO algorithm can effectively improve the accuracy and the quality of solutions, and distinctly speed up the convergence speed and computing time.