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Open Access

ARTICLE

Dynamic Weighted Spherical Particle Swarm Optimization for UAV Path Planning in Complex Environments

Rui Yao1,2, Yuye Wang1,2,*, Fei Yu1,2,3,*, Hongrun Wu1,2, Zhenya Diao1,2
1 College of Physics and Information Engineering, Minnan Normal University, Zhangzhou, 363000, China
2 Key Lab of Intelligent Optimization and Information Processing, Minnan Normal University, Zhangzhou, 363000, China
3 Key Lab of Light Field Manipulation and System Integration Applications in Fujian Province, Zhangzhou, 363000, China
* Corresponding Author: Yuye Wang. Email: email; Fei Yu. Email: email

Computers, Materials & Continua https://doi.org/10.32604/cmc.2026.073861

Received 27 September 2025; Accepted 22 December 2025; Published online 28 January 2026

Abstract

Path planning for Unmanned Aerial Vehicles (UAVs) in complex environments presents several challenges. Traditional algorithms often struggle with the complexity of high-dimensional search spaces, leading to inefficiencies. Additionally, the non-linear nature of cost functions can cause algorithms to become trapped in local optima. Furthermore, there is often a lack of adequate consideration for real-world constraints, for example, due to the necessity for obstacle avoidance or because of the restrictions of flight safety. To address the aforementioned issues, this paper proposes a dynamic weighted spherical particle swarm optimization (DW-SPSO) algorithm. The algorithm adopts a dual Sigmoid-based adaptive weight adjustment mechanism for balancing global exploration and local exploitation, as well as a lens-based opposition learning one to improve search flexibility and solution diversity. Simulation experiments on real digital elevation models demonstrate that DW-SPSO significantly outperforms recent state-of-the-art particle swarm optimization (PSO) variants in terms of path safety, smoothness, and convergence speed. The performance superiority is statistically validated by the Wilcoxon signed-rank test. The results confirm the algorithm’s effectiveness in generating high-quality UAV paths under diverse threat conditions, offering a robust solution for autonomous navigation systems.

Keywords

Dynamic weight adjustment; lens opposition learning; particle swarm optimization; path planning; unmanned aerial vehicles

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