A New Mechanism of Pheromone Increment and Diffusion for Solving Travelling Salesman Problems with Ant Colony Algorithm

Ant colony optimization (ACO) is a population-based metaheuristic technique to effectively solve combination optimization problems. However it is still an active research topic how to improve the performance of ACO algorithms. This paper presents an algorithm based on a new mechanism of pheromone updating and diffusion for solving TSPs (Travelling Salesman Problems). First, we introduce an ant-constant model, which can effectively embody the difference of pheromone for different paths. Then, we establish a pheromone diffusion model based on info fountain of a path to reflect faithfully the intensity field of pheromone diffusion and strengthen the local collaborations and communications among ants. Finally, we adopt a mutation strategy with lower computational complexity to prevent the proposed approach from getting in local optimal solution. The experimental results of TSPs show that the proposed algorithm can not only get much more optimal solutions but also greatly enhance convergence speed.