%0 Journal Article
%A CHENG Biyun
%A HUANG Yang
%A LU Haiyan
%A XU Kaibo
%T Ant colony optimization algorithm based on improved pheromones double updating and local optimization for solving TSP
%D 2017
%R 10.11772/j.issn.1001-9081.2017.06.1686
%J Journal of Computer Applications
%P 1686-1691
%V 37
%N 6
%X Concerning the drawbacks of the Ant Colony Optimization (ACO) algorithm such as low convergence rate and being easy to fall into local optimum solutions, an ACO algorithm based on Improved Pheromones Double Updating and Local Optimization (IPDULACO) was proposed. Double updating was performed on the pheromones of subpaths whose path contribution degrees to the current global optimal solution obtained by colony were bigger than the prescribed path contribution threshold. The selected probability of the subpaths which were used to constitute the potential optimal solution was increased and the convergence rate of the proposed algorithm was accelerated. Then, when the ant colony fell into the local optimal solution in the search process, the random insertion method was utilized to change the city sequences of the current local optimal solution in order to enhance the algorithm's ability of jumping out of local optimal solution. The improved algorithm was applied to several classical Traveling Salesman Problem (TSP) instances in the simulation experiments. The experimental results show that, for small-size TSP instances, the IPDULACO can obtain the known optimal solution in less number of iterations. For relatively large-size TSP instances, the IPDULACO can obtain the optimal solution with higher accuracy in less number of iterations. Therefore, the IPDULACO has the stronger ability of searching for the global optimal solution and faster convergence rate, and it can be used for solving TSP effectively.
%U http://www.joca.cn/EN/10.11772/j.issn.1001-9081.2017.06.1686