Ant Colony Algorithm Based on Dynamic Adaptive Pheromone Updating and Its Simulation

In order to effectively overcome the defects of local and global pheromone updating for the basic Ant Colony Algorithm, this paper has proposed a new improved Ant Colony Algorithm based on the dynamic adaptive weight in the updating strategy. The proposed algorithm can update pheromone dynamically and adaptively according to the change of taboo lists and the quality of iteration-best solutions. By the experiments of several typical Traveling Salesman Problems (TSP), the proposed algorithm is clearly better than several other typically Ant Colony Algorithms in the convergence speed and the solution quality. The test results can reflect its effectiveness and feasibility.