N状态二进制一致性算法设计及其优化

Design and optimization of an N-state binary consensus algorithm

针对二进制一致性算法扩展性差、经验依赖性强的缺点,提出了一种 N 状态分布式二进制一致性算法。首先,基于Gossip算法的平均一致性思想和轮盘赌思想,更新无线传感器网络状态均值和当前状态均值的偏差程度,计算所有可能更新状态的初始概率分布;然后,利用遗传算法优化初始概率分布,得到准确率较高的最优概率分布。仿真结果表明,在相同状态个数条件下,本文所设计的算法具有更好的准确率和收敛时间。

Given the disadvantages of poor expansibility and strong experience dependence of existing binary consensus algorithms, we propose an N -state distributed binary consensus algorithm. Firstly, based on the idea of average consensus of the Gossip algorithm and the roulette idea, the degree of deviation between the state average of the wireless sensor network and the current state average is updated, and the initial probabilistic distribution of all possible update states is calculated. Secondly, the genetic algorithm is applied to optimize the initial probability distribution and obtain the optimal probability distribution with better accuracy. The results show that the proposed algorithm has higher accuracy and shorter convergence time under the same number of states.