基于改进的免疫克隆蛙跳算法的多约束QoS路由优化研究

卢毅; 徐梦颖; 周杰

doi:10.11959/j.issn.1000-436x.2020102

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基于改进的免疫克隆蛙跳算法的多约束QoS路由优化研究

学术论文 | 更新时间：2024-06-05

- 基于改进的免疫克隆蛙跳算法的多约束QoS路由优化研究
- Multi-constraints QoS routing optimization based on improved immune clonal shuffled frog leaping algorithm
- 通信学报 2020年41卷第5期页码：141-149
- 作者机构：
  
  石河子大学信息科学与技术学院，新疆石河子 832003
- 作者简介：
  
  [ "卢毅（1981- ），男，陕西西安人，石河子大学工程师、博士生，主要研究方向为人工智能、物联网" ]
  [ "徐梦颖（1996- ），女，江苏徐州人，石河子大学硕士生，主要研究方向为人工智能" ]
  [ "周杰（1982- ），男，湖南湘乡人，博士，石河子大学副教授，主要研究方向为人工智能" ]
- 基金信息：
  
  兵团中青年科技创新领军人才计划基金资助项目(2018CB006)
- DOI：10.11959/j.issn.1000-436x.2020102
  中图分类号： TP212.6
- 网络出版日期：2020-05，
  
  纸质出版日期：2020-05-25
- 稿件说明：
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卢毅, 徐梦颖, 周杰. 基于改进的免疫克隆蛙跳算法的多约束QoS路由优化研究[J]. 通信学报, 2020,41(5):141-149.

Yi LU, Mengying XU, Jie ZHOU. Multi-constraints QoS routing optimization based on improved immune clonal shuffled frog leaping algorithm[J]. Journal on communications, 2020, 41(5): 141-149.
卢毅, 徐梦颖, 周杰. 基于改进的免疫克隆蛙跳算法的多约束QoS路由优化研究[J]. 通信学报, 2020,41(5):141-149. DOI： 10.11959/j.issn.1000-436x.2020102.

Yi LU, Mengying XU, Jie ZHOU. Multi-constraints QoS routing optimization based on improved immune clonal shuffled frog leaping algorithm[J]. Journal on communications, 2020, 41(5): 141-149. DOI： 10.11959/j.issn.1000-436x.2020102.

摘要

针对多约束路由选择问题，设计了数学模型并提出了一种改进的免疫克隆蛙跳算法。所提方法结合了免疫克隆算法与传统蛙跳算法，在分组丢失率、链路带宽、时延抖动、时延、能量损耗条件的限制下，计算源节点到终端节点的能量损耗，通过所提算法寻找一条能量损耗最小的路径。在仿真实验中，将所提算法与自适应遗传算法、自适应蚁群算法进行了对比。实验结果表明，所提算法在一定程度上解决了多约束QoS单播路由优化问题，与自适应遗传算法与自适应蚁群算法相比，所提算法避免了局部最优，有效地降低了数据在传输路径上的能量损耗。

Abstract

Aiming at the multi-constraint routing problem

a mathematical model was designed

and an improved immune clonal shuffled frog leaping algorithm (IICSFLA) was proposed

which combined immune operator with traditional SFLA.Under the constraints of bandwidth

delay

packet loss rate

delay jitter and energy cost

total energy cost from the source node to the terminal node was computed.The proposed algorithm was used to find an optimal route with minimum energy cost.In the simulation

the performance of IICSFLA with adaptive genetic algorithm and adaptive ant colony optimization algorithm was compared.Experimental results show that IICSFLA solves the problem of multi-constraints QoS unicast routing optimization.The proposed algorithm avoids local optimum and effectively reduces energy loss of data on the transmission path in comparison with adaptive genetic algorithm and adaptive ant colony optimization algorithm.

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references

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