基于DC_GWO优化算法的多无人机协同航迹规划  

Multi-UA V cooperative flight path planning based on DC_GWO optimization algorithm

作　　者：李汶键 李金峰 鲁旭涛[1] 李静 Li Wenjian;Li Jinfeng;Lu Xutao;Li Jing(Shanxi Key Laboratory of High-end Equipment Reliability Technology,North University of China,Taiyuan 030051,China;Military Representative Office in Hangzhou District,Army Equipment Department,Hangzhou 310011,China;School of Electrical and Control Engineering,North University of China,Taiyuan 030051,China)

机构地区：[1]中北大学高端装备可靠性技术山西省重点实验室,太原030051 [2]陆装驻杭州地区军代室,杭州310011 [3]中北大学电气与控制工程学院,太原030051

出　　处：《战术导弹技术》2024年第6期127-138,共12页Tactical Missile Technology

基　　金：中北大学高端装备可靠性技术重点实验室研究基金资助项目(446-110103);智元实验室资助(ZYL2024020)。

摘　　要：针对原始灰狼优化(Grey Wolf Optimization,GWO)算法求解多无人机三维航迹规划问题时会出现缺乏多样性、收敛速度慢和易陷入局部最优等问题,提出一种基于差分进化多重策略混合灰狼优化算法(DC_GWO)。基于差分进化算法的突变策略进行种群优化,为算法全局搜索过程中丰富种群多样性奠定基础;根据算法的收敛特性构建新型非线性收敛因子,平衡算法的全局和局部搜索能力;在灰狼位置更新中引入莱维飞行策略,使灰狼具有更强的全局搜索能力,避免算法过早地陷入局部最优。为验证DC_GWO算法的有效性,进行了6个国际通用的标准测试函数收敛性对比实验。实验结果表明,DC_GWO算法有较高的求解精度和较快的求解速度。为验证DC_GWO算法在航迹规划上的优势,进行了多无人机航迹规划仿真实验。实验结果表明,DC_GWO算法相较于GWO算法,适应度最优值降低了6%、适应度平均值降低了8%和适应度方差降低了86%,验证了DC_GWO算法在多无人机航迹规划上具有一定参考价值。Due to the lack of diversity,slow convergence and local optimality of Grey Wolf Optimization(GWO)algorithm when solving multi-UAV 3D path planning problems,a differential evolution&multiple combined strategies Wolf Optimization algorithm(DC_GWO)is proposed.Population optimization based on the mutation strategy of differential evolution algorithm lays a foundation for enriching population diversity in the global search process of the algorithm.According to the convergence characteristics of the algorithm,a new nonlinear convergence factor is constructed to balance the global and local search ability of the algorithm.Levy flight strategy is introduced into Grey Wolf position update to make Grey Wolf have stronger global search ability and avoid the algorithm falling into local optimal prematurely.In order to verify the validity of DC_GWO algorithm,the convergence comparison tests for six international standard test functions are carried out.The experimental results show that the DC_GWO algorithm has higher solving accuracy and faster solving speed.In order to verify the advantages of DC_CWO algorithm in flight path planning,a multi-UAV flight path planning simulation experiment is carried out.The experimental results show that compared with the GWO algorithm,the optimal fitness value of DC_CWO algorithm is reduced by 6%,the average fitness value is reduced by 8%.and the fitness variance is reduced by 86%,which verify that DC_GWO algorithm has certain reference value in multi-UAV flight path planning.

关 键 词：多无人机 航迹规划 混合灰狼优化算法 突变策略 新型非线性因子 莱维飞行策略 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TP85[自动化与计算机技术—控制科学与工程]