基于改进哈里斯鹰优化算法的无人机路径规划

UAV Path Planning based on improved Harris Hawk Optimization Algorithm

摘要:

智能算法是解决三维环境中无人机路径规划问题的重要工具，在求解时容易出现陷入局部最优、路径最优性有限的问题，为解决此问题，提出了一种基于分工的增强型哈里斯鹰优化算法进行路径规划；设计了势能波动学习策略增强探索效率，提高算法对空间的搜索性能；对算法在开发阶段选择迭代策略时的盲目性问题进行了分析，提出了分工机制根据种群质量调整猎物逃逸机会，避免策略的盲目选择；设计了一种精英时变莱维飞行，平衡算法对于前期跳出局部最优、后期提高收敛精度的不同需求；使用仿真实验与其他算法进行对比，评估改进算法的性能。仿真结果表明，改进算法在收敛精度和稳定性等方面均得到明显提升，能有效解决无人机路径规划问题。

Abstract:

Intelligent algorithm is an important tool to solve the path planning problem of UAV in three-dimensional environment, and it is easy to fall into local optimum and the path optimality is limited. To solve this problem, an enhanced Harris Eagle optimization algorithm based on division of labor is proposed for path planning. The potential energy fluctuation learning strategy is designed to enhance the efficiency of exploration and improve the search performance of the algorithm. This paper analyzes the blindness of iterative strategy selection in the development stage of the algorithm, and puts forward a division of labor mechanism to adjust the escape opportunity of prey according to the population quality to avoid blind strategy selection. An elite time-varying levy flight is designed to balance the different requirements of the algorithm for jumping out of local optimum in the early stage and improving convergence accuracy in the later stage. The performance of the improved algorithm is evaluated by comparing the simulation experiment with other algorithms. The simulation results show that the improved algorithm can obviously improve the convergence accuracy and stability, and can effectively solve the path planning problem of UAV.