An Enhanced Moth-Flame Optimization Algorithm for UAVs 3D Path Planning
Bingyu Li, Xin Wang, Shengjie Zhang, Lihua Fu · 2023
The navigation of Unmanned Aerial Vehicles (UAVs) heavily rely on their path planning. For three-dimensional path planning of UAVs in complex environments, this paper proposes a method based on an Enhanced Moth-Flame Optimization algorithm for UAVs 3D path planning. This paper has two major contributions. Firstly, an evaluation function is designed considering various constraints during UAV flight. Secondly, adaptive flame adjustment strategy and evolutionary strategies for learning targets are proposed to enhance the original Moth-Flame Optimization algorithm. The enhanced algorithm is applied to the UAVs 3D path planning, and the proposed algorithm is evaluated in three simulated environments: mountains, forests, and buildings. The simulation results show that the proposed algorithm outperforms other compared algorithms in terms of global merit-seeking ability, with an average improvement of 4%. Furthermore, the algorithm shows faster convergence speed, evidenced by its quicker landing in early iterations, tendency to converge at middle iterations, and guaranteed convergence within 90 generations, which is very effective for 3D path planning in complex environments.