摘要
计算机网络的可靠性是衡量计算机综合性能的一项重要指标,传统的计算机网络系统庞大复杂且受多方面因素的影响,极易造成系统的不稳定进而给整个网络带来风险。针对这一情况,在分析已有计算机网络设计方案的基础上,结合实际建立新型计算机网络可靠性优化模型以及网络成本链路模型。对传统的遗传算法的繁殖方式进行改进,在保证全局收敛性的同时提高算法的流畅性。通过实验仿真验证了改进遗传算法对计算机网络的优化明显强于模糊神经网络、神经网络算法,可以大幅度提升计算机网络的可用性和可靠性,降低甚至避免网络错误的出现,优化网络性能。
The reliability of computer network is an important indicator for measuring the overall performance of a computer.The traditional computer network systems are large and complex,and are affected by many factors,which can easily cause system instability and bring risks to the entire network.In view of this situation,based on the analysis of existing computer network design schemes,a new computer network reliability optimization model and network cost link model were established in combination with the actual situation.By improving the reproducing method of the traditional genetic algorithm,the fluency of the algorithm was improved while ensuring the global convergence.The simulation results show that the improved genetic algorithm is better than fuzzy neural network and neural network algorithm in optimizing the reliability of computer network,which can maximize the availability and reliability of computer network,reduce or even avoid network errors,and optimize network performance.
作者
魏波
WEI Bo(School of Information Engineering,Zhengzhou University of Industrial Technology,Zhengzhou 451100,China)
出处
《新乡学院学报》
2020年第3期32-35,共4页
Journal of Xinxiang University
基金
河南省科技攻关计划项目(162102210119)。
关键词
计算机网络
遗传算法
网络可靠性
computer network
genetic algorithm
network reliability
