A multi-strategy improved osprey optimization algorithm for 3D UAV path planning

Abstract To address the limitations of slow convergence and susceptibility to local optima using the traditional osprey optimization algorithm (OOA) in unmanned aerial vehicle (UAV) path planning, a multi-strategy improved OOA (MOOA) was proposed. First, a guided learning strategy based on historical population information is constructed at the global level. By utilizing the population standard deviation to establish an adaptive feedback loop for each dimension, an optimization benchmark is determined through dynamic updates, thereby enhancing the convergence efficiency. Second, a composite strategy integrating hyperbolic cosine and tangent search is employed during the exploitation phase. The nonlinear characteristics of the hyperbolic cosine function are leveraged to achieve adaptive attenuation of the search step size, ensuring a smooth transition to a fine-grained search mode, while micro-perturbations based on tangents are introduced to circumvent local stagnation. Furthermore, a high-altitude soaring strategy based on Lévy flight, coordinated with a sine factor varying over time, was incorporated to provide supplementary global search capabilities in the late convergence stage, further elevating the quality of the final solution. Ablation studies on the CEC2017 benchmark suite elucidate the individual contributions of each strategy. Comparative analysis against nine state-of-the-art algorithms confirms the superiority of the MOOA in convergence accuracy and stability, while experiments on typical engineering design problems verify its robustness in handling complex physical constraints. Finally, MOOA was applied to 3D UAV path planning scenarios of varying complexity. The simulation results demonstrate its capability to effectively minimize path costs and significantly maximize planning success rates, validating the algorithm’s effectiveness and engineering practicality in complex real-world environments.