To settle the contradictory between convergence speed and precocity and stagnation in ant colony algorithm, an adaptive ant colony algorithm, which is based on the equilibrium of the ant distribution, is presented. By dynamically adjusting the influence of each ant to the trail information updating and the selected probabilities of the paths according to the equilibrium of the ant distribution, the algorithm can keep good balance between accelerating convergence and averting precocity and stagnation. Experimental results on symmetric and asymmetric TSP 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 TSP.