Improved Red-Billed Blue Magpie Optimizer for Unmanned Aerial Vehicle Path Planning

Unmanned Aerial Vehicle (UAV) path planning is a critical aspect of autonomous flight operations, requiring efficient and accurate solutions to navigate complex and dynamic environments. Traditional optimization methods often fall short in addressing the nonlinearities and constraints associated with UAV path planning. To overcome these challenges, metaheuristic algorithms, particularly swarm intelligence-based approaches, have gained significant attention. This paper introduces an improved version of the Red-billed Blue Magpie Optimizer (RBMO), a novel metaheuristic algorithm inspired by the cooperative and adaptive behaviors of the red-billed blue magpie integrated with levy flight function. The enhanced RBMO incorporates advanced strategies to improve convergence speed, solution precision, and robustness, addressing the limitations of the original algorithm. The effectiveness of the improved RBMO is measured in terms of path lenght, execution time and convergence plot. It is demonstrated through its application to UAV path planning, where it outperforms existing methods in terms of computational efficiency and path optimization quality.