An ant colony algorithm with global adaptive optimization

The Ant colony algorithm has a slow convergence speed and is easy to fall into local optimal. In order to overcome these shortcomings, the principles of Ant System (AS) and MAX-MIN Ant System (MMAS) algorithms were introduced. In this paper, we analyze the disadvantages of these two algorithms, and based on the idea of adaptation, we put forward an improved ant colony algorithm with global adaptive optimization (GAO). Some improvements of the rules are made such as state transfer and pheromone update, and the detailed programming steps of GAO are listed. Compared with AS and MMAS for the Traveling Salesman Problem (TSP), the simulation results show that GAO has excellent global optimization capabilities and appropriate convergence time.