Abstract:

A dynamic Unmanned Aerial Vehicle （UAV） path planning method based on Modified Whale Optimization Algorithm （MWOA） was proposed for the problem of UAV path planning in environments with complex terrains. Firstly， by analyzing the mountain terrain， dynamic targets， and threat zones， a three-dimensional dynamic environment and a UAV route model were established. Secondly， an adaptive step size Gaussian walk strategy was proposed to balance the algorithm’s abilities of global exploration and local exploitation. Finally， a supplementary correction strategy was proposed to correct the optimal individual in the population， and combined with differential evolution strategy， the population was avoided from falling into local optimum while improving convergence accuracy of the algorithm. To verify the effectiveness of MWOA， MWOA and intelligent algorithms such as Whale Optimization Algorithm （WOA）， and Artificial Hummingbird Algorithm （AHA） were used to solve the CEC2022 test functions， and validated in designed UAV dynamic environment model. The comparative analysis of simulation results shows that compared with the traditional WOA， MWOA improves the convergence accuracy by 6.1%， and reduces the standard deviation by 44.7%. The above proves that the proposed MWOA has faster convergence and higher accuracy， and can handle UAV path planning problems effectively.

Key words: Whale Optimization Algorithm (WOA), adaptive step Gaussian walk, supplementary correction strategy, differential evolution, Unmanned Aerial Vehicle (UAV), dynamic path planning

摘要：

针对复杂地形环境下的无人机（UAV）路径规划问题，提出一种基于改进鲸鱼优化算法（MWOA）的动态UAV路径规划方法。首先，通过解析山体地形、动态目标和威胁区，建立三维动态环境与UAV航路模型；其次，提出一种自适应步长高斯游走策略，并将该策略用于平衡算法的全局探索与局部发掘的能力；最后，提出一种辅助修正策略对种群最优个体进行修正，并结合差分进化策略，在避免种群陷入局部最优的同时提高算法的收敛精度。为验证MWOA的有效性，使用MWOA与鲸鱼优化算法（WOA）、人工蜂鸟算法（AHA）等智能算法求解CEC2022测试函数，并在设计的UAV动态环境模型中进行验证。仿真结果对比分析表明，与WOA相比，MWOA的收敛精度提高了6.1%，标准差减小了44.7%。可见，所提MWOA收敛更快且精度更高，能有效处理UAV路径规划问题。

关键词: 鲸鱼优化算法, 自适应步长高斯游走, 辅助修正策略, 差分进化, 无人机, 动态路径规划