基于雾计算的分布式网络优化方法OA

This paper addresses the high latency and low energy efficiency issues in distributed networks within smart tea garden environments by proposing an optimization algorithm based on fog computing.A multi-layered network model is developed,considering computational capacity,communication bandwidth,and energy consumption.This model serves as the foundation for an optimization framework that efficiently allocates tasks and manages computational mi-gration.The paper also introduces an hybrid particle swarm optimization(Hybrid-PSO)algorithm,which combines adaptive inertia weights,local search,genetic crossover,and a constraint penalty mechanism.This approach sig-nificantly enhances the algorithm's global search ability and solution feasibility.Experimental results show that Hy-brid-PSO reduces system latency,energy consumption,and task completion time,outperforming traditional particle swarm optimization(PSO),genetic algorithm(GA),and PSO-GA algorithms in terms of convergence speed.Fur-ther analysis indicates that computational migration and energy-aware routing strategies improve load balancing and extend the network's lifetime.