Asynchronous randomized Gossip consensus algorithm with nonuniform-selected probability and optimizing
非均匀选择概率下异步随机Gossip共识算法及优化

The traditional asynchronous randomized Gossip consensus algorithm is founded on the basis of the uniformselected 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 eigenvalue of the probabilistic weighted matrix. An optimization algorithm 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 matrix the disadvantages of dependence on the network topology.