基于PSO-GA的分片区块链系统性能优化方法

System Performance Optimization Method with PSO-GA for Sharded Blockchain

在这篇文章中,针对分片区块链(Sharded Blockchain)系统性能优化问题,提出了一种结合粒子群和遗传算法的系统性能优化方法(PSO-GA),目的是为了在尽可能满足当前网络环境情况下,提升其系统吞吐量.该方法考虑分片区块链中节点的计算能力、恶意节点的概率以及节点之间的传输速率等不同网络环境下,找到响应网络状态的最佳分片区块链系统参数;为了避免传统粒子群优化算法陷入局部最优的问题,引入遗传算法中的交叉操作和变异操作,有效提高方法的准确性.通过大量仿真实验对方法的有效性进行验证分析.实验结果表明,相比于其他的方法,本文所提出的方法可以在更短的时间取得更高的系统吞吐量.

In this article,in view of the performance optimization problem on sharded blockchain system,an optimization method combining Particle Swarm Optimization and Genetic Algorithm(PSO-GA)is proposed,in order to improve system throughput under the current network environment as much as possible.This method considers the transmission rate between nodes in the sharded blockchain,the computing resource of the nodes,and the probability of malicious nodes to find the best sharded blockchain system parameters that respond to the network state.In order to avoid the problem of local optimization in the traditional particle swarm optimization algorithm,the cross-operation and mutation operation in the genetic algorithm are introduced to effectively improve the accuracy of the method.A large number of simulation experiments are used to verify the effectiveness of the method.Experimental results show that compared with other methods,the proposed method can achieve higher Transaction Per Second in a shorter time.