基于改进萤火虫算法的Rapid IO路由选择策略

殷从月; 张兴明; 任权; 魏帅

doi:10.11959/j.issn.2096-109x.2018054

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基于改进萤火虫算法的Rapid IO路由选择策略

论文 | 更新时间：2024-06-03

- 基于改进萤火虫算法的Rapid IO路由选择策略
- Rapid IO routing strategy based on improved glowworm swarm optimization algorithm
- 网络与信息安全学报 2018年4卷第6期页码：52-61
- 作者机构：
- 作者简介：
  
  [ "殷从月（1994-），女，江苏扬州人，国家数字交换系统工程技术研究中心硕士生，主要研究方向为Rapid IO网络、异构计算系统。" ]
  [ "张兴明（1963-），男，河南新乡人，国家数字交换系统工程技术研究中心教授、硕士生导师，主要研究方向为新型网络体系结构。" ]
  [ "任权（1994-），男，湖南常德人，国家数字交换系统工程技术研究中心硕士生，主要研究方向为网络安全防御、顽健网络体系结构。" ]
  [ "魏帅（1984-），男，河南南阳人，国家数字交换系统工程技术研究中心讲师，主要研究方向为嵌入式计算。" ]
- 基金信息：
  
  国家科技重大专项基金资助项目;The National Science Technology Major Project(2016ZX01012101);国家自然科学基金资助项目;The National Natural Science Foundation of China(61572520);国家自然科学基金创新研究群体基金资助项目;The Foundation for Innovative Research Groups of the National Natural Science Foundation of China(61521003)
- DOI：10.11959/j.issn.2096-109x.2018054
  中图分类号： TP393.02
- 网络首发：2018-06，
  
  纸质出版：2018-06-15
- 稿件说明：
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殷从月, 张兴明, 任权, 等. 基于改进萤火虫算法的Rapid IO路由选择策略[J]. 网络与信息安全学报, 2018,4(6):52-61.

Congyue YIN, Xingming ZHANG, Quan REN, et al. Rapid IO routing strategy based on improved glowworm swarm optimization algorithm[J]. Chinese Journal of Network and Information Security, 2018, 4(6): 52-61.
殷从月, 张兴明, 任权, 等. 基于改进萤火虫算法的Rapid IO路由选择策略[J]. 网络与信息安全学报, 2018,4(6):52-61. DOI： 10.11959/j.issn.2096-109x.2018054.

Congyue YIN, Xingming ZHANG, Quan REN, et al. Rapid IO routing strategy based on improved glowworm swarm optimization algorithm[J]. Chinese Journal of Network and Information Security, 2018, 4(6): 52-61. DOI： 10.11959/j.issn.2096-109x.2018054.

摘要

针对Rapid IO网络Qo S路由选择问题，提出一种基于改进萤火虫算法的Rapid IO路由选择策略。首先，利用高斯变异和存储机制对传统萤火虫算法进行优化，高斯变异可以有效控制算法搜索空间中解的散射程度，使算法避免陷入局部最优，存储机制有利于评估并存储每只萤火虫的历史状态，防止信息丢失。然后，将改进后的萤火虫算法与实际Rapid IO网络Qo S问题相结合，选择出最终的最佳路由策略。实验结果表明，在所模拟的Rapid IO测试网络中，改进后的萤火虫算法时延为42 ms，时延抖动为8 ms，代价最低为64 ms，共需要迭代的次数为8，相较于其他算法曲线更加稳定，更能快速找到最优解，表现出的性能最优，有效解决了Rapid IO网络Qo S路由选择问题。

Abstract

Aiming at the problem of Qo S routing in Rapid IO network

a Rapid IO routing strategy based on improved glowworm swarm optimization algorithm was proposed.Firstly

gaussian mutation and storage mechanism were used to optimize the traditional firefly algorithm.Gaussian mutation can effectively control the scattering degree of the solution in the search space of the algorithm

so that the algorithm avoids falling into a local optimum.The storage mechanism is conducive to evaluating and storing the historical state of each glowworm

preventing information loss.Then combine the improved glowworm swarm optimization algorithm with the actual Rapid IO network Qo S problem and select the final best routing strategy.The experimental results show that in the simulated Rapid IO test network

the improved glowworm swarm optimization algorithm has a delay of 42 ms

delayed jitter of 8 ms

a minimum cost of 64 ms

and a total of 8 iterations

which is more stable than other algorithm curves.It can find the optimal solution more quickly and show the best performance

effectively solving the Qo S routing problem of Rapid IO network.

关键词

Keywords

references

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