The ant colony algorithm (ACO) is an intelligent optimization algorithm inspired by the behavior of ants searching for food in the nature. As a general stochastic optimization algorithm, the ant colony algorithm has been successfully applied to TSP, mobile robot path planning and other combinatorial optimization problems, and achieved good results. But because the probability of the algorithm is a typical algorithm, the parameters set in the algorithm is usually determined by experimental method, leading to the optimization of the performance closely related to people’s experience, it is difficult to optimize the algorithm performance. Moreover, the traditional ant colony algorithm has many shortcomings, such as long convergence time and easiness to fall into the local optimal solution. In order to overcome these shortcomings, in this paper, a large number of experimental data are analyzed to obtain the main appropriate parameters of the ant colony algorithm, such as the number (Forumala Presented). of ants, the number (Forumala Presented). of iterations, the influence factor (Forumala Presented)., and a new pheromone updating method that is related to the sine function is proposed in this paper, the simulation results show that the improved algorithm can accelerate the speed by 60%, and the global optimal solution can be found more easily than the original ant colony algorithm.