张义良, 李希勇. 基于萤火虫群的网络状态故障检测算法研究[J]. 微电子学与计算机, 2015, 32(8): 102-105,109. DOI: 10.19304/j.cnki.issn1000-7180.2015.08.021
引用本文: 张义良, 李希勇. 基于萤火虫群的网络状态故障检测算法研究[J]. 微电子学与计算机, 2015, 32(8): 102-105,109. DOI: 10.19304/j.cnki.issn1000-7180.2015.08.021
ZHANG Yi-liang, LI Xi-yong. The Study of Network Status Failure Detection Algorithm Based on Glowworm Swarm[J]. Microelectronics & Computer, 2015, 32(8): 102-105,109. DOI: 10.19304/j.cnki.issn1000-7180.2015.08.021
Citation: ZHANG Yi-liang, LI Xi-yong. The Study of Network Status Failure Detection Algorithm Based on Glowworm Swarm[J]. Microelectronics & Computer, 2015, 32(8): 102-105,109. DOI: 10.19304/j.cnki.issn1000-7180.2015.08.021

基于萤火虫群的网络状态故障检测算法研究

The Study of Network Status Failure Detection Algorithm Based on Glowworm Swarm

  • 摘要: 结合萤火虫群提出了一种新的故障检测算法FDD-GS(Failure Detection algorithm based on Glowworm Swarm).该算法首先根据特征信息熵建立了故障检测评价方法和最小偏差的优化模型,同时给出了具体的检测算法.最后,通过建立网络仿真平台,深入分析了影响FDD-GS算法的关键因素,并且对比研究了FDD-GS与其他算法的性能情况,结果表明FDD-GS算法的检测误差较低.

     

    Abstract: In order to effectively detect the failure of network system, a novel detection algorithm FDD-GS (Failure Detection algorithm based on Glowworm Swarm) is proposed by glowworm swarm. In this algorithm, the failure detection method and the minimum deviation optimization model have proposed by feature information entropy at first, and the algorithm processes is presented. Finally, an experiment with simulation platform was conducted with the study the key factors of FDD-GS. Compared to performance of other algorithm, the results show that FDD-FNN has low detection error.

     

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