基于影响度介数中心性的多智能体牵引控制算法
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中国人民解放军陆军工程大学

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TP273

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国家重点研发计划2018YFC0806900,中国博士后科学基金资助项目2018M633757,江苏省重点研发计划BE2016904、BE2017616、BE2018754、BE2019762.


Multi-agent pinning control algorithm based on influence degree betweenness centrality
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The Army Engineering University of PLA

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    摘要:

    针对多智能体系统在牵制控制过程中最终收敛时系统稳定性较差的问题,设计一种影响度矩阵,重新构建介数中心性算法来完成牵制控制节点选择工作。首先,根据子网的度分布计算影响度矩阵;其次,由影响度矩阵计算子网的介数中心性分布矩阵;最后,根据介数中心性选择牵制控制节点。既保留个体本身的重要性,也引入邻居个体重要性对其影响程度。经过对比实验验证了影响度介数中心性算法可有效增强多智能体系统的鲁棒性并提高系统的收敛速度。

    Abstract:

    Aiming at the poor stability of the final convergence of multi-agent systems in the process of pinning control, we design an influence degree matrix and reconstruct an intermediate centrality algorithm to complete the selection of pinning control nodes. Firstly, influence degree matrix is calculated according to degree distribution of subnet. Secondly, the influence degree matrix is used to calculate the intermediate centrality distribution matrix of subnet. Finally, traction control nodes are selected according to the distribution of betweenness centrality. We not only preserve the importance of the individual in the system, but also introduce the influence of the importance of the individual in the neighborhood. Comparative experiments show that betweenness centrality algorithm can greatly enhance the robustness of multi-agent systems and improve the convergence speed of the system.

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  • 收稿日期:2019-08-01
  • 最后修改日期:2020-12-18
  • 录用日期:2020-03-06
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