UAV Trajectory Planning based on Improved Whale Optimization Algorithmm

To address the shortcomings of the traditional whale optimization algorithm (WOA) in the process of the trajectory planning, which has poor global search efficiency and is easy to fall into the local optimal solution, a trajectory planning method based on the chaotic multi-strategy whale optimization algorithm(CMS-WOA) is proposed. Firstly, CMS-WOA employs Circle chaotic mapping and reverse learning for population initialization, enhancing algorithmic efficiency. Secondly, non-linear convergence factors and an adaptive weight strategy ensure a balance between global exploration and local optimization capabilities. Moreover, replacing the traditional algorithm's global search strategy with the Lévy flight strategy enhances exploration across the entire search space. Finally, when the algorithm search stagnates, the random differential mutation strategy is used to update the location information of the whale population to avoid premature convergence of the algorithm. Simulation results highlight CMS-WOA's superiority in terms of accelerated convergence, local optima evasion, and higher-quality three-dimensional trajectory planning compared to other algorithms.