The traditional asynchronous randomized Gossip consensus algorithm is founded on the basis of the uniform-selected probability time model which does not consider the impact of topology on local information transfer. We introduce a more reasonable asynchronous randomized Gossip consensus algorithm with nonuniform-select probability, and analyze the convergence of the algorithm in probability sense. The convergence rate depends on the second largest eigenvaiue of the probabilistic weighted matrix. An optimization algonthm for selecting probabilities is proposed by projection subgradient method The numerical example indicates that the algorithm proposed can improve the convergence rate by optimizing the selection of probabilities for agents, and compensates for the traditional algorithm in optimizing communication matnx the disadvantages of dependence on the network topology.%异步随机Gossip算法火都采用以均匀选择概率为基础的时间模型,并未充分考虑网络拓扑结构对智能体获取信息的影响,为此本文提出了一种更为合理的基于非均匀选择概率的异步随机Gossip算法.首先给出了非均匀选择概率下的异步时间模型,在概率意义下分析了算法的收敛性.算法的收敛速度取决于概率化权重矩阵的第2大特征值,并利用投影次梯度算法给出了选择概率优化方法.仿真分析表明,在非均匀选择概率下可通过对各智能体选择概率的优化,改善算法的收敛速度,并且弥补了传统的通信概率矩阵优化方法受制于网络拓扑结构的不足.
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