DEACO: Hybrid Ant Colony Optimization with Differential Evolution

Ant colony optimization (ACO) algorithm is a novel meta-heuristic algorithm for the approximate solution of combinatorial optimization problems that has been inspired by the foraging behavior of real ant colonies. ACO has strong robustness and easy to combine with other methods in optimization, but it has the shortcomings of stagnation that limits the wide application to the various areas. In this paper, a hybrid ACO with Differential Evolution (DE) algorithm was proposed to overcome the above-mentioned limitations, and this algorithm was named DEACO. Considering the importance of ACO pheromone trail for ants exploring the candidate paths, DE was applied to optimize the pheromone trail in the basic ACO model. In this way, a reasonable pheromone trail between two neighboring cities can be formed, so as to lead the ants to find out the optimum tour. The proposed algorithm is tested with the Traveling Salesman Problem (TSP), and the experimental results demonstrate that the proposed DEACO is a feasible and effective ACO model in solving complex optimization problems.