Drone trajectory prediction is a key technology for achieving efficient, safe, and intelligent operation. This paper proposes a PSO-DBO hybrid optimization algorithm that combines the beetle optimization algorithm (DBO) to address the problems of traditional particle swarm optimization (PSO) being prone to local optima and slow convergence speed in multi-modal scenes. By introducing the rolling ball disturbance, propagation disturbance, and theft disturbance mechanisms of DBO, a dynamic role assignment strategy is designed to balance local development efficiency and global diversity. The experiment is based on a three-dimensional mountain environment model, constructing a fitness function that includes trajectory length, threat, path smoothness, and flight altitude, and comparing it with traditional PSO, PSO-DBO, the convergence performance of chaotic particle swarm algorithm. The simulation results show that the algorithm effectively overcomes the premature convergence problem of particle swarm optimization and provides a more robust solution for UAV path planning in complex environments.
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Chen, Y., Geng, J., Wang, S. (2025) Three-dimensional Trajectory Prediction of Unmanned Aerial Vehicles Based on Improved Particle Swarm Optimization Algorithm. Scientific Research Bulletin, 2(2), 1-10.