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.
References
[1] Darwish, A., Ezzat, D., Hassanien, A. E. (2020). An optimized model based on convolutional neural networks and orthogonal learning particle swarm optimization algorithm for plant diseases diagnosis. Swarm and evolutionary computation, 52, 100616.
[2] Wang, H., Mo, Y. (2025). Adaptive hybrid optimization algorithm for numerical computing in engineering applications. Engineering Optimization, 57(4), 845-883.
[3] Huang, C. (2021). A Novel Three‐Dimensional Path Planning Method for Fixed‐Wing UAV Using Improved Particle Swarm Optimization Algorithm. International Journal of Aerospace Engineering, 2021(1), 7667173.
[4] Wu, P., Li, T., Song, G. (2020). UCAV path planning based on improved chaotic particle swarm optimization. In 2020 Chinese Automation Congress (CAC), 1069-1073.
[5] Wu, J., Duan, Q. (2023). An algorithm for solving travelling salesman problem based on improved particle swarm optimisation and dynamic step Hopfield network. International Journal of Vehicle Design, 91(1-3), 208-231.
[6] Gecejová, N., Češkovič, M., Kurdel, P. (2025). Simulation Environment Conceptual Design for Life-Saving UAV Flights in Mountainous Terrain. Drones, 9(6), 416.
[7] Zhu, D., Wang, S., Shen, J., Zhou, C., Li, T., Yan, S. (2023). A multi-strategy particle swarm algorithm with exponential noise and fitness-distance balance method for low-altitude penetration in secure space. Journal of Computational Science, 74, 102149.
[8] Li, T., Chen, H., Sun, S., Corchado, J. M. (2018). Joint smoothing and tracking based on continuous-time target trajectory function fitting. IEEE transactions on Automation Science and Engineering, 16(3), 1476-1483.
[9] Fu, Z., Yu, J., Xie, G., Chen, Y., Mao, Y. (2018). A heuristic evolutionary algorithm of UAV path planning. Wireless Communications and Mobile Computing, 2018(1), 2851964.
[10] Yuan, J., Liu, Z., Lian, Y., Chen, L., An, Q., Wang, L., Ma, B. (2022). Global optimization of UAV area coverage path planning based on good point set and genetic algorithm. Aerospace, 9(2), 86.
[11] Kim, J. H., Briceno, S. I., Justin, C. Y., Mavris, D. (2021). Designated points-based free-flight approach to enable real-time flight path planning. In AIAA AVIATION 2021 FORUM, 2403.
[12] Rezaee, M. R., Hamid, N. A. W. A., Hussin, M., Zukarnain, Z. A. (2024). Comprehensive review of drones collision avoidance schemes: Challenges and open issues. IEEE Transactions on Intelligent Transportation Systems, 25(7), 6397-6426.
[13] Gad, A. G. (2022). Particle swarm optimization algorithm and its applications: A systematic review. Archives of computational methods in engineering, 29(5).
[14] Sengupta, S., Basak, S., Peters, R. A. (2018). Particle Swarm Optimization: A survey of historical and recent developments with hybridization perspectives. Machine learning and knowledge extraction, 1(1), 157-191.
[15] Priyadarshi, R., Kumar, R. R. (2025). Evolution of swarm intelligence: a systematic review of particle swarm and ant colony optimization approaches in modern research. Archives of Computational Methods in Engineering, 1-42.
[16] Halim, A. H., Ismail, I., Das, S. (2021). Performance assessment of the metaheuristic optimization algorithms: an exhaustive review. Artificial Intelligence Review, 54(3), 2323-2409.
Share and Cite
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), 1-11.