Research on Trajectory Planning Using Improved Harris Hawk Optimization Algorithm

This paper investigates the trajectory planning problem of Unmanned aerial vehicles (UAVs) in complex urban environments. Addressing the limitations of traditional trajectory planning methods, which often get trapped in local optima and fail to meet practical flight requirements in complex and constrained environments, the study proposes a research approach based on the improved Harris's hawk optimization algorithm. Specifically, to enhance the algorithm's performance, the study first addresses issues related to the lack of diversity in the initial population and susceptibility to local optima by introducing a Gaussian distribution-based initialization method. Secondly, to balance the exploration and exploitation of the search space, a strategy based on a non-linearly decreasing function for updating escape energy is proposed. Experimental results demonstrate the algorithm's superiority in trajectory planning, significantly improving UAV flight efficiency and path planning accuracy, showing promising prospects for practical engineering applications.