An improved dung beetle optimizer for UAV 3D path planning

Abstract

UAV path planning poses the challenge of determining the most efficient route from an initial location to a desired destination, while considering mission objectives and adhering to various flight restrictions. This is a challenging optimization problem with high dimensionality that demands efficient path planning methods. To tackle the intricate UAV path planning problem within complex 3D environments, we propose an improved dung beetle optimizer (IDBO) for UAV path planning. Firstly, we formulate a cost function that converts the UAV path planning problem into a multidimensional function optimization problem, considering both trajectory restrictions and safety restrictions of the UAV. This enables us to effectively search for the optimal path. Secondly, we introduce a chaotic strategy to initialize the population, ensuring a comprehensive exploration of the solution space and enhancing population diversity. Additionally, we incorporate exponentially decreasing inertia weights into the algorithm, which improves convergence speed and exploration capability. Furthermore, to tackle the issue of decreasing population diversity during the late stages of convergence, we employ an adaptive Cauchy mutation strategy to enhance population diversity. Through simulation results, we demonstrate that IDBO achieves faster convergence and generates better paths compared to existing approaches in the same environment. These results demonstrate the remarkable efficacy of the proposed improved algorithm in effectively tackling the UAV path planning problem.