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基于NSGA-Ⅲ的UUV集群打击任务分配模型

吴思聪 吴曦

吴思聪, 吴曦. 基于NSGA-Ⅲ的UUV集群打击任务分配模型[J]. 水下无人系统学报, 2023, 31(3): 474-480 doi: 10.11993/j.issn.2096-3920.2023-0023
引用本文: 吴思聪, 吴曦. 基于NSGA-Ⅲ的UUV集群打击任务分配模型[J]. 水下无人系统学报, 2023, 31(3): 474-480 doi: 10.11993/j.issn.2096-3920.2023-0023
WU Sicong, WU Xi. UUV Cluster Strike Task Allocation Model Based on NSGA-Ⅲ[J]. Journal of Unmanned Undersea Systems, 2023, 31(3): 474-480. doi: 10.11993/j.issn.2096-3920.2023-0023
Citation: WU Sicong, WU Xi. UUV Cluster Strike Task Allocation Model Based on NSGA-Ⅲ[J]. Journal of Unmanned Undersea Systems, 2023, 31(3): 474-480. doi: 10.11993/j.issn.2096-3920.2023-0023

基于NSGA-Ⅲ的UUV集群打击任务分配模型

doi: 10.11993/j.issn.2096-3920.2023-0023
详细信息
    作者简介:

    吴思聪(1995-), 男, 在读硕士, 主要研究方向为联合作战体系仿真

  • 中图分类号: TJ67; U674.941; E843

UUV Cluster Strike Task Allocation Model Based on NSGA-Ⅲ

  • 摘要: 无人水下航行器(UUV)集群将成为未来水下作战的重要力量, UUV集群的打击任务分配问题是UUV运用过程中的关键问题之一, 可以看作为一个武器-目标分配问题, 也是一个多约束多目标优化问题。因此, 考虑UUV对敌舰探测概率、载弹量、弹药成本、杀伤概率和目标价值, 以及UUV对鱼雷的拦截概率等因素, 建立打击收益和弹药消耗成本2个目标函数, 构建了UUV集群打击任务分配模型。引入多目标优化算法——第3代非支配排序遗传算法(NSGA-Ⅲ)对该模型进行求解, 分析了编码策略、约束处理方法以及NSGA-Ⅲ的流程和关键步骤。仿真结果表明,与NSGA-Ⅱ、自适应形状估计多目标进化算法(AGE-MOEA)相比, 该算法在运行时间和反世代距离2项指标上表现更好, 能够更好地为决策提供支撑。

     

  • 图  1  NSGA-Ⅲ算法流程

    Figure  1.  Flowchart of NSGA-Ⅲ

    图  2  UUV打击任务分配pareto前沿

    Figure  2.  Pareto frontier of UUV strike task allocation

    图  3  3种算法IGD指标对比

    Figure  3.  Comparison of IGD indicators among three algorithms

    表  1  pareto前沿对应函数值

    Table  1.   Pareto Frontier corresponding function value

    序号$ {f_1} $$ {f_2} $
    1−3.113 2416 800
    2−3.083 3816 200
    3−3.053 3715 600
    4−3.019 7415 000
    5−2.975 7714 200
    下载: 导出CSV

    表  2  平均总运行时间

    Table  2.   Average total running time

    算法名称 种群规模 迭代次数 时间/s
    NSGA-Ⅱ 50 5 000 0.249 8
    10 000 0.477 4
    100 5 000 0.186 3
    10 000 0.384 5
    NSGA-Ⅲ 50 5 000 0.237 9
    10 000 0.469 0
    100 5 000 0.182 2
    10 000 0.377 2
    AGE-MOEA 50 5 000 0.318 0
    10 000 0.479 4
    100 5 000 0.272 0
    10 000 0.735 8
    下载: 导出CSV
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出版历程
  • 收稿日期:  2023-03-09
  • 修回日期:  2023-05-09
  • 录用日期:  2023-05-11
  • 网络出版日期:  2023-05-25

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