Abstract: Aiming at the problem that the ant colony algorithm has slow convergence speed and easy to fall into local optimum, an ant colony optimization algorithm combining dynamic scheduling strategy and competition mechanism is proposed. The algorithm focuses on introducing the scheduling strategy. The algorithm focuses on introducing the scheduling strategy, with the change of the iteration period, the path information is fed back to the scheduling operator in real time through the feedback coefficient, guiding the ants to dynamically select the path, and fully exploring the optimal solution in the broad space to avoid the ant colony falling into the local optimum. In addition, the ant colony is divided into two sub-group competitive search optimal solutions, and different incentives are given to balance the diversity and convergence speed of the algorithm. The algorithm is verified by 14 examples of Traveling Salesman Problem, the algorithm can obtain the optimal solution or the near optimal solution with fewer iterations, indicating the effectiveness and superiority of the algorithm.