Improved Snake Swarm Optimizer for UAV 3D Path Planning Problem
A multi-strategy improved snake swarm optimizer based unmanned aerial vehicle(UAV)3D trajectory planning algorithm is proposed to address the shortcomings of poor accuracy,slow convergence and easy generation of local optimum in complex environments.This work establishes constraints and objective cost functions for path planning,and transforms 3D path planning into an optimization problem for objective function.In order to improve the performance of the snake swarm optimizer,an improved Sine chaotic map is designed to improve the initial population quality and ergodicity.A nonlinear switching probability threshold is designed to achieve adaptive switching of population combat/mating modes.A learning factor adaptive adjustment is introduced to enhance the population learning ability.And in the later stage of iteration,an elite selection and simulated annealing algorithm are combined to enhance population diversity to avoid search stagnation at a local optimum.The improved snake swarm optimizer is used to solve the three-dimensional trajectory planning problem of unmanned aerial vehicles,and the effectiveness of the algorithm is verified by establishing simple and complex scenarios.The results show that the improved algorithm has lower cost for planning path and can effectively improve the planning efficiency.
NETL
NSTL
万方数据
