Ant colony optimization algorithm based on directional pheromone diffusion

Ant colony optimization (ACO) Algorithm is a novel search algorithm, which simulates the social behaviors of ant colony for solving complicated combinatorial optimization problems. With the analysis of shortcomings of basic ACO such as lack and lag of collaboration among ants, ACO algorithm based on Pheromone diffusion (ACOPD) has been proposed. It is proved that ACOPD can improve the collaboration among nearby ants and converge to a local solution soon, but it often gets into a local optimum solution without escaping from it. In order to avoid the problem, this article will introduce an ACO algorithm based on Directional pheromone diffusion (ACODPD), which is based on the idea that pheromone strength on a path affects the amount of pheromone diffusing to this path. The contrastive simulation results for TSP problem show that our new algorithm ACODPD has much higher convergence speed and stronger capability of finding optimal solutions than the ACOPD.