基于多策略改进蜣螂优化算法的无人机3维路径规划OA

The efficiency of unmanned aerial vehicle(UAV)largely depends on their path planning capabilities in complex three-dimensional environments.Addressing the issues of the dung beetle optimizer(DBO),such as the tendency to fall into local optima and poor path planning quality when dealing with UAV three-dimensional path planning tasks in complex environments with multiple constraints,an improved dung beetle optimizer(IDBO)is proposed.Firstly,the flight environment of the UAV is spatially modeled and an objective function is formulated.Secondly,the diversity of the algorithm is enhanced by introducing Chebyshev mapping for population initialization,and the local search capability is strengthened by employing a golden sine strategy.Additionally,a dynamic weight coefficient is added to the thievery behavior to improve adaptability in dynamic environments.Finally,the superiority of the proposed algorithm is verified through simulation experiments.Simulation results demonstrate that the IDBO performs well on multiple test functions and environments,enhancing the quality of UAV path planning in terms of seeking optimal solutions.