An adaptive ant colony algorithm based on equilibrium of distributions

To balance the conflict between convergence speed, precocity and stagnation in ant colony algorithms, an adaptive ant colony algorithm, which is based on the equilibrium of the ant distribution, is presented. By dynamically adjusting the dependence of each ant on the trail information updating and by selected probabilities of the paths according to the equilibrium of the ant distribution, the algorithm can keep a good balance between accelerating convergence and averting precocity and stagnation. Experimental results on symmetric and asymmetric traveling salesman problems (TSPs) show that the method presented in this paper has much higher convergence speed and stability than that of classical ant colony algorithm, and is more suitable for solving large scale TSPs.