高技术通讯2026，Vol.36Issue(6)：602-610,9.DOI:10.3772/j.issn.1002-0470.2026.06.006
基于雾计算的分布式网络优化方法
A distributed network optimization approach based on fog computing
摘要
Abstract
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.关键词
雾计算/自适应惯性权重/局部搜索/遗传交叉/约束惩罚机制/改进粒子群算法Key words
fog computing/adaptive inertia weights/localized search/genetic crossover/constrained penalty mechanism/improved particle swarm algorithm引用本文复制引用
彭永倩,刘鸿..基于雾计算的分布式网络优化方法[J].高技术通讯,2026,36(6):602-610,9.基金项目
贵州省高校人文社会科学研究项目(2025RW020)资助. （2025RW020）
