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基于指数平均动量鸽群优化的多无人机协同目标防御

段海滨 仝秉达 刘冀川

段海滨, 仝秉达, 刘冀川等 . 基于指数平均动量鸽群优化的多无人机协同目标防御[J]. 北京航空航天大学学报, 2022, 48(9): 1624-1629. doi: 10.13700/j.bh.1001-5965.2022.0308
引用本文: 段海滨, 仝秉达, 刘冀川等 . 基于指数平均动量鸽群优化的多无人机协同目标防御[J]. 北京航空航天大学学报, 2022, 48(9): 1624-1629. doi: 10.13700/j.bh.1001-5965.2022.0308
DUAN Haibin, TONG Bingda, LIU Jichuanet al. Coordinated target defense for multi-UAVs based on exponentially averaged momentum pigeon-inspired optimization[J]. Journal of Beijing University of Aeronautics and Astronautics, 2022, 48(9): 1624-1629. doi: 10.13700/j.bh.1001-5965.2022.0308(in Chinese)
Citation: DUAN Haibin, TONG Bingda, LIU Jichuanet al. Coordinated target defense for multi-UAVs based on exponentially averaged momentum pigeon-inspired optimization[J]. Journal of Beijing University of Aeronautics and Astronautics, 2022, 48(9): 1624-1629. doi: 10.13700/j.bh.1001-5965.2022.0308(in Chinese)

基于指数平均动量鸽群优化的多无人机协同目标防御

doi: 10.13700/j.bh.1001-5965.2022.0308
基金项目: 

科技创新2030-“新一代人工智能”重大项目 2018AAA0102303

国家自然科学基金 U20B2071

国家自然科学基金 91948204

国家自然科学基金 T2121003

国家自然科学基金 U1913602

详细信息
    通讯作者:

    段海滨, E-mail: hbduan@buaa.edu.cn

  • 中图分类号: V221+.3; TB553

Coordinated target defense for multi-UAVs based on exponentially averaged momentum pigeon-inspired optimization

Funds: 

Science and Technology Innovation 2030-Key Project of "New Generation Artificial Intelligence" 2018AAA0102303

National Natural Science Foundation of China U20B2071

National Natural Science Foundation of China 91948204

National Natural Science Foundation of China T2121003

National Natural Science Foundation of China U1913602

More Information
  • 摘要:

    针对多无人机(UAV)协同目标防御问题,提出了一种基于指数平均动量鸽群优化(EM-PIO)算法。针对三维空间中的多无人机协同目标防御系统进行建模,得到了无人机支配区域的曲面约束方程,并获得了双方无人机的最优控制输入量。采用多级罚函数法构造了优化算法的目标函数,并通过所提出的EM-PIO算法来求解最优目标点。将所提EM-PIO算法与遗传算法(GA)和粒子群优化(PSO)算法进行仿真对比实验,验证了所提EM-PIO算法更加有效解决多无人机协同目标防御问题。

     

  • 图 1  EM-PIO算法解决多无人机协同防御问题实现流程

    Figure 1.  Procedure of coordinated target defense with multi-UAVs cooperative using EM-PIO algorithm

    图 2  约束曲面示意图

    Figure 2.  Schematic of constraint surface

    图 3  EM-PIO、PSO和GA算法进化曲线对比

    Figure 3.  Comparison of evolution curves of EM-PIO, PSO and GA algorithms

    表  1  EM-PIO、PSO和GA算法参数

    Table  1.   Parameters of EM-PIO, PSO and GA algorithms

    算法 参数 数值 描述
    EM-PIO N 20 鸽群中鸽子数目
    Nc1 100 地图和指南针算子迭代次数
    Nc2 120 地标算子迭代次数
    α 0.5 动量因子
    PSO N 20 种群粒子数目
    Nc 120 迭代次数
    ω 0.5 惯性权重
    c1 0.6 社会学习因子
    c2 0.4 个体学习因子
    GA N 20 种群个体数目
    Nc 120 迭代次数
    Pc 0.8 交叉概率
    Pm 0.05 变异概率
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-04-30
  • 录用日期:  2022-05-18
  • 网络出版日期:  2022-06-06

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