In order to solve the slow convergence speed problem of the ant colony algorithm, we study ant colony algorithm pheromone updating rules and propose a pheromone updating rule based on the thought of iteration. We identify the best reasonable value of the pheromone volatilization coefficient under the new pheromone updating rules through information residual factor experiments. Finally, experimental results on the two examples of eil51 and dantzig42 problems show that the improved ant colony algorithm outperforms the traditional ant colony algorithm and other artificial intelligence algorithms in terms of optimal solution and convergence.