Optimized Particle Swarm-Ant Colony Fusion for UAV Path Planning Research

Ant Colony Algorithm (ACO) and Particle Swarm Algorithm (PSO) are two heuristic algorithms commonly used in three-dimensional path planning for UAVs. The particle swarm algorithm is prone to produce local optimization, while the ACO algorithm has a slow convergence speed. Therefore, a new fusion algorithm is proposed to address the above problems. The fusion algorithm utilizes the early and fast convergence of the particle swarm algorithm to generate a suboptimal solution as the initial condition of the pheromone distribution of the ant colony algorithm, and then fuses the two improved algorithms to obtain the final path planning scheme. Experimental results show that this fusion algorithm outperforms the other three algorithms in terms of convergence speed and convergence accuracy.