UAV path planning algorithm based on Global Optimal Solution Tracking Enhanced Particle Swarm Optimization

With the increasing demand for unmanned aerial vehicles (UAVs) in various fields, there is a growing need for effective and efficient autonomous navigation in complex three-dimensional environments. To address the problem of autonomous navigation of UAV in complex three-dimensional environments. We propose an improved particle swarm optimization algorithm based on global best solution tracking. Firstly, due to conventional particle swarm algorithms display considerable randomness and are susceptible to particle oscillations, resulting in being trapped in local optima. Therefore, we reinitialized the particle swarm at each iteration based on the global best solution from the previous iteration. Then, in order to ensure the algorithm maintains good search capability throughout the entire process, a parameter adaptive strategy has been adopted. The experimental results showed that the improved algorithm significantly reduces the number of iterations and path length.