@article{Yao2026, 
author = {Rui Yao and Yuye Wang and Fei Yu and Hongrun Wu and Zhenya Diao},
title = {Dynamic Weighted Spherical Particle Swarm Optimization for UAV Path Planning in Complex Environments},
year = {2026},
journal = {Computers, Materials & Continua},
volume = {87},
number = {2},
pages = {44},
keywords = {Dynamic weight adjustment, lens opposition learning, particle swarm optimization, path planning, unmanned aerial vehicles},
url = {https://www.sciopen.com/article/10.32604/cmc.2026.073861},
doi = {10.32604/cmc.2026.073861},
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.}
}