This paper presents a novel hybrid Particle Swarm Optimization (PSO) algorithm called PPSwarm for multi-UAV path planning in complex environments. The algorithm combines the RRT* algorithm to generate initial paths, a Prioritized Planning (PP) method to assign priorities to UAVs, and a path randomization strategy to enhance particle diversity. The PPSwarm algorithm is designed to address the challenges of finding feasible paths in environments with numerous obstacles and collaborative constraints among multiple UAVs. Experimental results show that PPSwarm outperforms other algorithms such as DE, PSO, ABC, GWO, and SPSO in terms of path quality, convergence speed, and runtime, both in scenarios with 40 UAVs and larger-scale experiments involving 500 UAVs. The algorithm demonstrates excellent processing capability and scalability, making it suitable for real-world applications in complex and dynamic environments.This paper presents a novel hybrid Particle Swarm Optimization (PSO) algorithm called PPSwarm for multi-UAV path planning in complex environments. The algorithm combines the RRT* algorithm to generate initial paths, a Prioritized Planning (PP) method to assign priorities to UAVs, and a path randomization strategy to enhance particle diversity. The PPSwarm algorithm is designed to address the challenges of finding feasible paths in environments with numerous obstacles and collaborative constraints among multiple UAVs. Experimental results show that PPSwarm outperforms other algorithms such as DE, PSO, ABC, GWO, and SPSO in terms of path quality, convergence speed, and runtime, both in scenarios with 40 UAVs and larger-scale experiments involving 500 UAVs. The algorithm demonstrates excellent processing capability and scalability, making it suitable for real-world applications in complex and dynamic environments.