摘要
如何有效提高多智能体系统的一致性收敛速度是一致性问题中的一个重要研究内容。一致性收敛速度可通过拉普拉斯矩阵的最小非零特征值来衡量,文中通过计算机仿真发现,对于不同的复杂网络模型,影响其一致性收敛速度的因素也不同。提高网络一致性收敛速度的具体方法是:在最近邻耦合网络中,减少节点数N或增大耦合数K;在NW小世界网络中,增加节点数N或者增大随机化加边概率p,因为收敛速度与二者具有良好的线性关系;在Waxman随机图网络中,增加节点数N或增大其模型中的参数α和β,当β增大时,收敛速度整体上呈线性增长,但会出现较小的波动。该研究结果对优化多智能体网络的一致性收敛速度有一定的指导作用。
How to improve uniform convergence rate of multi-agent systems is an important issue in uniform research.The uniform convergence rate can be well performed by the smallest non-zero eigenvalues of the Laplacian matrix.According to the computer simulation,this paper found that the uniform convergence rate is significantly led by different factors in different complex networks.The methods for impraing uniform convergence rate on the different complex networks are listed as follows.For the nearest-neighbor coupled network,the number of nodes N should be reduced,or the number of coupling K should be increased.For the NW small-world network,the number of nodes N should be increased,or the probability of random edged p should be increased.This paper found that the convergence rate has a good linear relationship between the number of nodes N and the probability of random edges p .For the Waxman random graph network,the number of the nodes N should be increased,or the network parameters α and β should be increased.The convergence rate is linear when β increases,but there is a slight fluctuation.The results can help to optimize the convergence rate of multi-agent network.
作者
张森
刘文奇
赵宁
ZHANG Sen;LIU Wen-qi;ZHAO Ning(Faculty of Science,Kunming University of Science and Technology,Kunming 650500,China)
出处
《计算机科学》
CSCD
北大核心
2019年第4期95-99,共5页
Computer Science
基金
国家自然科学基金(61573173)资助
关键词
复杂网络
多智能体系统
一致性
最近邻耦合网络
NW小世界网络
随机图网络
Complex network
Multi-agent systems
Consensus
Nearest-neighbor coupled network
NW small-world network
Random graph network
