The ant colony algorithm has great advantages in solving some non-deterministic polynomial complete(NPC) problems, but there are some shortages, such as low convergence precision and slow convergence speed. In order to balance convergence accuracy and convergence speed, a local ant colony optimization algorithm based on virtual ants is proposed in this paper. This algorithm improves the efficiency of computing resources by reducing the proportion of repeated computing resources, thus improving the accuracy of less iterations. The unit pheromone and the global updating strategy are adjusted to make the method match. At the same time, the two-point local optimization operator-point exchange and cross removal are added to accelerate the convergence rate and further improve the precision of the solution. By constraining the parameters of local optimization operators , the computation of local optimization is reduced, and the complexity of the whole algorithm is approximately equal to that of the basic ant colony algorithm. From the final experimental data, it can be concluded that the algorithm can obtain higher precision with less iterative times, and a good balance is made between the convergence speed and the convergence precision.