Improved hybrid dung beetle algorithm for UAV 3D path planning in complex environments

DOI:	10.11809/bqzbgcxb2025.03.026
Keywords:	complex environments; dung beetle algorithm; UAV path planning; chaotic map; cost functions
Abstract:	To address the difficulty of low convergence accuracy, weak global search ability, and susceptibility to getting stuck in local optima of unmanned aerial vehicle (UAV) path optimization algorithms in complex obstacle environments, a new improved hybrid dung beetle optimization algorithm (SPM and Osprey based Hybrid Dung Beetle Optimizer, SO DBO) combines chaotic mapping and Osprey optimization is proposed. The Sine Piece Wise Linear Chaotic Map (SPM) is used to initialize the population positions which improves the search efficiency of the algorithm. This study introduces dynamic global exploration strategy and Gaussian distributed random angle strategy in both obstacle free and obstacle free modes of the rolling ball beetle population to improve algorithm accuracy and global search capability. Introducing an adaptive T distribution strategy to update the position of foraging beetles enhances the algorithm’s ability to escape local optima. By customizing dynamic weight factors, the algorithm’s global search capability is improved and the risk of getting stuck in local optima is reduced. The experimental study shows that compared to the original Dung Beetle Optimizer (DBO) and Particle Swarm Optimization (PSO), the SO DBO algorithm improves the cost function index by 9.68% and 12.93% in simple environment, and 13.34% and 17.00% in complex environment, respectively, which effectively improves the convergence speed, accuracy and stability of the algorithm.
Issue:	Vol. 46 No. 3 (2025)
Published:	2025-03-31
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