TY  - EJOU
AU  - Yao, Rui 
AU  - Wang, Yuye 
AU  - Yu, Fei 
AU  - Wu, Hongrun 
AU  - Diao, Zhenya 

TI  - Dynamic Weighted Spherical Particle Swarm Optimization for UAV Path Planning in Complex Environments
T2  - Computers, Materials \& Continua

PY  - 2026
VL  - 87
IS  - 2
SN  - 1546-2226

AB  - 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.
KW  - Dynamic weight adjustment; lens opposition learning; particle swarm optimization; path planning; unmanned aerial vehicles

DO  - 10.32604/cmc.2026.073861