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This paper introduces a Dynamic Weighted Spherical Particle Swarm Optimization (DW-SPSO) algorithm for UAV path planning in complex environments. DW-SPSO employs a dual Sigmoid-based adaptive weight adjustment and lens-based opposition learning to enhance exploration, exploitation, and solution diversity. Experimental results on real digital elevation models demonstrate that DW-SPSO outperforms state-of-the-art PSO variants in path safety, smoothness, and convergence speed, validated by the Wilcoxon signed-rank test.
Forget getting stuck in local optima: this new PSO variant leverages dynamic weighting and lens-based learning to generate safer, smoother, and faster UAV paths.
: 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鈥檚 effectiveness in generating high-quality UAV paths under diverse threat conditions, offering a robust solution for autonomous navigation systems.
