基于改进蜣螂算法的无人机航迹规划OA

A Piecewise Levy Flight Dung Beetle Optimizer(PLDBO)is proposed to solve the problems of UAV flight path planning,which requires a lot of computation and is difficult to converge.Firstly,a 3D mission space model and the UAV path planning cost function are established,and the path planning problem is transformed into a multidimensional function optimization problem.Secondly,the Piecewise chaotic mapping is invoked on Dung Beetle Optimizer(DBO)algorithm to change its initialization process,enhance the diversity of the population,and accelerate the convergence speed.Then,the golden sine is introduced to improve the position update formula of dung beetle,which effectively coordinates the global search ability and local mining ability,and speeds up the convergence.Levy strategy is introduced into the thieving dung beetle position update formula to enhance the ability of the algorithm to jump out of the local optima.Then a horizontal crossover with adaptive strategy is introduced to improve the convergence accuracy of the algorithm and enhance the global optimization ability.Finally,the advantages of PLDBO are fully verified by comparison on 15 well-known classical benchmark functions,and the proposed algorithm is applied to the path planning problem.The simulation results show that PLDBO can obtain more feasible and efficient paths.