摘要
针对异构无人机协同侦察航迹规划算法难以平衡收敛精度和收敛速度,以及易陷入局部最优的问题,提出了一种基于改进哈里斯鹰算法的异构无人机协同侦查航迹规划算法。首先,建立了多载荷异构多无人机协同侦察模型,以最短路径为目标函数,设计了一种改进的哈里斯鹰算法求解该模型。其次,利用反向学习机制初始化种群,有利于增加种群的多样性以及提高解的质量,在算法前期利用差分变异扰动加快收敛速度,在算法后期利用高斯分布函数系数降低陷入局部最优的可能,避免算法早熟。最后基于模型进行仿真实验,相较于改进粒子群优化算法和多策略哈里斯鹰算法,所设计算法收敛精度分别提高了12%、10%,收敛速度分别提高了42%、44%。
Aiming at the problems that the path planning algorithm of heterogeneous UAV cooperative reconnaissance is difficult to improve convergence accuracy and convergence speed at the same time,and easy to fall into local optimality,a new trajectory planning algorithm based on improved Harris hawks algorithm for heterogeneous UAV cooperative reconnaissance is proposed.Firstly,a heterogeneous UAV collaborative reconnaissance model with multiple payloads is established.Taking the shortest path as the objective function,an improved Harris hawks algorithm is designed to solve the model.Secondly,the reverse learning mechanism is used to initialize the population,which is conducive to increasing the diversity of the population and improving the quality of the solution.Differential variation disturbance is used in the early stage of the algorithm to accelerate the convergence speed,and the Gaussian distribution function coefficient is used in the later stage of the algorithm to reduce the possibility of falling into local optimum,so as to avoid premature maturity of the algorithm.Finally,simulation experiments are carried out based on the model.Compared with the improved particle swarm optimization algorithm and the multi-strategy Harris hawks algorithm,the convergence accuracy of the designed algorithm is increased by 12% and 10%,and convergence speed is increased by 42% and 44%,respectively.
作者
何文彪
胡永江
李文广
HE Wenbiao;HU Yongjiang;LI Wenguang(Shijiazhuang Campus,Army Engineering University,Shijiazhuang 050000,China)
出处
《中国惯性技术学报》
EI
CSCD
北大核心
2023年第7期717-723,共7页
Journal of Chinese Inertial Technology
基金
装备综合研究项目(XX20212A0211XX)。
关键词
哈里斯鹰算法
路径规划
反向学习
高斯差分变异
Harris hawks algorithm
path planning
reverse learning
Gaussian differential variation
