Improved ant colony optimization algorithm for the traveling salesman problems

Abstract

Ant colony optimization (ACO) is a new heuristic algorithm which has been proven a successful technique and applied to a number of combinatorial optimization problems. The traveling salesman problem (TSP) is among the most important combinatorial problems. An ACO algorithm based on scout characteristic is proposed for solving the stagnation behavior and premature convergence problem of the basic ACO algorithm on TSP. The main idea is to partition artificial ants into two groups: scout ants and common ants. The common ants work according to the search manner of basic ant colony algorithm, but scout ants have some differences from common ants, they calculate each route's mutation probability of the current optimal solution using path evaluation model and search around the optimal solution according to the mutation probability. Simulation on TSP shows that the improved algorithm has high efficiency and robustness.