Hybrid Particle Swarm Optimization for UAV Path Planning: A Novel Approach

This paper proposes a novel hybrid particle swarm optimization (PSO) algorithm for path planning of unmanned aerial vehicles (UAVs). The algorithm combines the advantages of traditional PSO with a novel mutation strategy to enhance exploration and exploitation capabilities. The proposed algorithm effectively balances exploration and exploitation, leading to improved path efficiency and obstacle avoidance. Simulation results demonstrate that the proposed algorithm outperforms existing methods in terms of path length, flight time, and obstacle avoidance rate. This research contributes to the development of more efficient and reliable UAV path planning techniques, paving the way for wider applications in various domains, such as aerial surveillance, delivery, and disaster relief.