Research on Path Planning Optimization Based on Adaptive Ant Colony Algorithm

Path planning is widely applied in scenarios such as traffic navigation, autonomous driving, and logistics distribution, holding significant theoretical and practical significance. Building upon the traditional ant colony algorithm, this paper proposes an adaptive ant colony algorithm tailored for path planning by adjusting the size of the pheromone evaporation factor. This approach addresses the limitations of the traditional ant colony algorithm, such as its tendency to get stuck in local optima and low search efficiency, and conducts simulation calculations on the algorithm. Simulation results show that the adaptive ant colony algorithm achieves an optimal path length of 29.8 after seven iterations. The traditional ant colony algorithm achieves the same optimal path length after 69 iterations. The comparison demonstrates that the adaptive ant colony algorithm exhibits superior search efficiency and global capabilities.