An improved ant colony algorithm is proposed to address issues such as susceptibility to local optima and slow convergence speed. Firstly, the relationship between the current target node and the next target node and the normal distribution function are introduced into the heuristic function, enhancing the algorithm's search capability in the early stages. In addition, by introducing an inflection point factor, the diversity of directional selection is enhanced. Secondly, an adaptive dynamic pheromone volatility coefficient is proposed to adjust the pheromone evaporation rate adaptively, modifying the pheromone update rules. Finally, simulation experiments were conducted using Matlab to compare the traditional ant colony algorithm and the improved ant colony algorithm on three different grid maps. The experimental results demonstrate that, compared with the traditional ant colony algorithm, the improved algorithm exhibits advantages such as faster convergence speed, shorter paths, and fewer inflection points.