An Improved Black-Winged Kite Algorithm for Unmanned Aerial Vehicle Path Planning

Abstract Three-dimensional (3D) path planning for unmanned aerial vehicles is a classic NP-hard problem, where conventional algorithms often face limitations like slow convergence and susceptibility to local optima. This paper proposes an improved black kite optimization algorithm (IGFMBKA) that enhances performance through the integration of adaptive weights, multi-directional flipping, and differential evolution with vertical crossover strategies. The effectiveness of IGFMBKA was evaluated using 18 benchmark functions, along with test functions from CEC2017 and CEC2022, and further applied to a 3D UAV path planning model. Experimental results demonstrate that IGFMBKA consistently surpasses both individual improvement strategies and state-of-the-art optimization algorithms across all test cases. Notably, IGFMBKA achieved the lowest comprehensive cost while generating smooth and collision-free flight trajectories. This approach provides an efficient solution for UAV path planning in complex scenarios.