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基于 AC-DSDE 进化算法多 UAVs协同目标分配

黄刚 李军华

黄刚, 李军华. 基于 AC-DSDE 进化算法多 UAVs协同目标分配. 自动化学报, 2021, 47(1): 173−184 doi: 10.16383/j.aas.c190334
引用本文: 黄刚, 李军华. 基于 AC-DSDE 进化算法多 UAVs协同目标分配. 自动化学报, 2021, 47(1): 173−184 doi: 10.16383/j.aas.c190334
Huang Gang, Li Jun-Hua. Multi-UAV cooperative target allocation based on AC-DSDE evolutionary algorithm. Acta Automatica Sinica, 2021, 47(1): 173−184 doi: 10.16383/j.aas.c190334
Citation: Huang Gang, Li Jun-Hua. Multi-UAV cooperative target allocation based on AC-DSDE evolutionary algorithm. Acta Automatica Sinica, 2021, 47(1): 173−184 doi: 10.16383/j.aas.c190334

基于 AC-DSDE 进化算法多 UAVs协同目标分配

doi: 10.16383/j.aas.c190334
基金项目: 国家自然科学基金(61440049, 61866025, 61866026), 江西省自然科学基金(20181BAB202025), 江西省优势科技创新团队计划(20181BCB24008), 江西省研究生创新基金(YC2018-S369)资助
详细信息
    作者简介:

    黄刚:南昌航空大学硕士研究生. 主要研究方向为导航制导与控制. E-mail: hgnchk@163.com

    李军华:南昌航空大学教授. 主要研究方向为进化计算和智能控制. 本文通信作者. E-mail: jhlee126@126.com

Multi-UAV Cooperative Target Allocation Based on AC-DSDE Evolutionary Algorithm

Funds: Supported by National Natural Science Foundation of China (61440049, 61866025, 61866026), Natural Science Foundation of Jiangxi Province (20181BAB202025), Superiority Science and Technology Innovation Team Program of Jiangxi Province (20181BCB24008), Graduate Innovation Foundation of Jiangxi Province (YC2018-S369)
  • 摘要:

    多无人机协同目标分配最优问题(Multi-UAV cooperative target allocation optimal problem, MUCTAOP), 旨在求解组合分配问题的最小代价值, 是最具有挑战性的多约束组合优化问题之一. 结合进化算法解决MUCTAOP需要考虑两个关键因素: 1) 在进化过程中保持覆盖问题空间的“探索性”和“开发性”平衡; 2) 建立符合实际战场复杂环境的多约束条件. 为解决这两个关键因素, 本文提出一种新的近似聚类混合双策略差分进化算法(Approximate clustering dual-strategy differential evolution algorithm, AC-DSDE). 首先, 根据父代种群适应度值将个体分成“探索类个体”与“开发类个体”; 然后根据混合双策略变异方案平衡后代多样性与收敛性; 最后, 结合无人机自身性能约束、协同约束和实际三维复杂环境构建约束函数. 实验结果表明, 本文所提出的AC-DSDE算法能够快速地找到合理的分配方案.

  • 图  1  MUCTA在三维任务环境中的场景

    Fig.  1  Simulation diagram of MUCTA in athree-dimensional environment

    图  2  个体分组

    Fig.  2  Individual grouping

    图  3  个体分组

    Fig.  3  Individual grouping

    图  4  三维环境中实验1的仿真结果

    Fig.  4  Simulation results of Experiment 1 in a three-dimensional environment

    图  5  三维环境中实验2的仿真结果

    Fig.  5  Simulation results of Experiment 2 in a three-dimensional environment

    图  6  实验1分配结果的单条收敛曲线

    Fig.  6  Single convergence curve of Experiment 1assignment results

    图  7  实验1分配结果的单条和平均收敛曲线对比

    Fig.  7  Comparison of the single and average convergence curves of the results of Experiment 1

    图  8  不同算法的平均收敛曲线

    Fig.  8  Average convergence curve of different algorithms

    表  1  实验初始数据

    Table  1  Initialization of experimental data

    ModelData type12345678910
    N=MUpos[10, 25, 10][140, 15, 12][30, 80, 13][110, 40, 15][80, 20, 15][20, 55, 15][120, 16, 17][160, 20, 15][80, 50, 12][170, 62, 13]
    Tpos[20, 210, 13][46, 200, 12][64, 210, 23][154, 210, 12][100, 200, 12][118, 210, 14][82, 220, 12][136, 190, 11][10, 170, 10][172, 170, 12]
    Rpos[130, 70, 4, 23][120, 140, 4, 26][42, 103, 5, 30][42, 180, 5, 25][50, 50, 5, 26]
    $V\ (\rm km/h)$[0.2, 0.3][0.2, 0.4][0.4, 0.75][0.3, 0.6][0.2, 0.3][0.35, 0.45][0.3, 0.5][0.3, 0.6][0.3, 0.5][0.3, 0.6]
    Umissile6.08.06.04.06.04.08.08.06.06.0
    $D\ (\rm km)$500700300350700900450610450610
    W3.02.01.03.02.01.02.03.02.01.0
    Tsort[3, 4][5, 2][6, 1][7, 4]
    N>MUpos[10, 25, 10][140, 15, 12][30, 80, 13][110, 40, 15][80, 20, 15][20, 55, 15][120, 16, 17][160, 20, 15][80, 50, 12][170, 40, 13]
    Tpos[28, 210, 13][64, 210, 23][154, 210, 14][118, 210, 14][136, 190, 11][172, 170, 12]
    Rpos[130, 70, 4, 23][120, 140, 4, 26][42, 103, 5, 30][42, 180, 5, 25][50, 50, 5, 26]
    $V\ (\rm km/h)$[0.4, 0.75][0.3, 0.6][0.2, 0.3][0.35, 0.45][0.3, 0.5][0.3, 0.6][0.2, 0.3][0.35, 0.45][0.3, 0.5][0.3, 0.6]
    Umissile6.08.06.04.06.04.08.08.06.06.0
    $D\ (\rm km)$400700650500700900450610400700
    W1.03.04.02.01.01.0
    Tsort[1, 3][2, 4][1, 2]
    $Twait\ (\rm{m})$200500600200800600300200700500
    N<MUpos[78, 20, 15][93, 31, 12][31, 20, 13][112, 32, 15][150, 25, 10][170, 50, 15]
    Tpos[28, 211, 13][46, 220, 12][64, 210, 23][154, 212, 12][100, 200, 12][118, 213, 14][82, 225, 12][136, 190, 11][10, 180, 10][172, 170, 12]
    Rpos[130, 70, 4, 23][120, 140, 4, 26][42, 103, 5, 30][42, 180, 5, 25][50, 50, 5, 26]
    Umissile4.08.06.04.06.04.0
    $V\ (\rm km/h)$[0.2, 0.3][0.2, 0.4][0.4, 0.75][0.3, 0.6][0.2, 0.3][0.2, 0.4]
    $D\ (\rm km)$400700650500610400
    W1.00.80.60.70.90.81.00.71.01.0
    Tsort[4, 5][5, 2][6, 7][7, 4]
    $Twait\ (\rm{m})$100600
    下载: 导出CSV

    表  2  两组实验进化参数的设定

    Table  2  Setting of experimental evolution parameters of two groups

    模式实验 1实验 2
    UnTnPnGenNumUnTnPnGenNum
    N = M1010501 000203030501 00010
    N > M106501 000203010501 00010
    N < M610501 000201030501 00010
    下载: 导出CSV

    表  3  实验1的分配结果

    Table  3  Assignment results of Experiment 1

    模式分配结果
    N = MUAV12345678910
    Target96175231084
    Cost265.9685.4306.1481.3609.2367.6490.1216.4424.6294.0
    N > MUAV12345678910
    Target1625114653
    Cost429.4311.7338.0437.6866.6354.6530.2216.4424.6294.0
    N < MUAV1112333456
    Target35761298410
    Cost228.7630.775.3525.9110.583.1547.1450.6452.8169.6
    下载: 导出CSV

    表  4  两组实验分配结果的统计数据

    Table  4  Statistics of the results of the two groups ofexperimental assignments

    实验模式平均时间(s)平均代价最优代价约束违背(%)优解(%)
    实验1N = M16.18 107.98 058.84.49 95
    N > M22.96 808.56 690.44.07 75
    N < M23.65 795.45 573.32.04 80
    实验2N = M38.218 605.118 093.66.21 60
    N > M46.613 490.513 302.17.13 70
    N < M50.513 537.412 528.75.24 80
    下载: 导出CSV

    表  5  AC-DSDE与其他算法之间的比较

    Table  5  Comparison between AC-DSDE and other algorithms

    模式方法UAV与目标点数量种群数量迭代次数实验次数CRMRIGMR最优代价平均代价平均时间 (s)
    N = MAC-DSDE N = M = 10301 000100.9null0.38 058.88 107.916.1
    DMDEN = M = 10301 00010CRnull1-CR8 257.68 204.819.8
    APC-DEN = M = 10301 000100.9null0.38 145.88 168.524.6
    N > MAC-DSDEN = 10, M = 6301 000100.9null0.36 690.46 808.522.9
    DMDEN = 10, M = 6301 00010CRnull1-CR6 737.16 973.729.19
    APC-DEN = 10, M = 6301 000100.9null0.36 770.36 835.626.5
    N < MAC-DSDEN = 6, M = 10301 000100.9null0.35 573.35 795.423.6
    DMDEN = 6, M = 10301 00010CRnull1-CR5 819.95 970.726.4
    APC-DEN = 6, M = 10301 000100.9null0.35 712.62 838.727.5
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-05-06
  • 录用日期:  2019-08-15
  • 网络出版日期:  2021-01-29
  • 刊出日期:  2021-01-20

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