[1]孔金生,肖天,徐津..基于混合遗传免疫粒群优化的网络拥塞控制方法[J].郑州大学学报(工学版),2013,34(02):57-59.[doi:10.3969/j. issn.1671 - 6833.2013.02.015]
　KONG Jin-sheng,XIAO Tian,XU Jin.Network Congestion Control Method Based on Hybrid Genetic ImmuneParticle Swarm Optimization[J].Journal of Zhengzhou University (Engineering Science),2013,34(02):57-59.[doi:10.3969/j. issn.1671 - 6833.2013.02.015]
点击复制

基于混合遗传免疫粒群优化的网络拥塞控制方法()
分享到：

《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
34
期数:
2013年02期
页码:
57-59
栏目:
出版日期:
2013-03-28

文章信息/Info

Title:
Network Congestion Control Method Based on Hybrid Genetic ImmuneParticle Swarm Optimization
作者:
孔金生; 肖天; 徐津.
郑州大学电气工程学院,河南郑州,450001, 华中科技大学电子与信息工程系,湖北武汉,430074
Author(s):
KONG Jin-sheng1; XIAO Tian2; XU Jin1
1.Ssehool of Electrical Engineering, Zhengzhou University,Zhengzhou 45001 , China; 2.Department of Electronics and Informa-tion Engineering，Huazhong University of Science & Technology，Wuhan 430074，China
关键词:
微粒群优化算法;  遗传算法;  免疫算法;  混合遗传免疫粒群;  网络拥塞控制
Keywords:
PSO; GA; IA; IGAPSO ;  network congestion control
分类号:
TP301.6
DOI:
10.3969/j. issn.1671 - 6833.2013.02.015
文献标志码:
A
摘要:
微粒群优化算法具有搜索速度快、易于实现等优点,然而在解决实际问题中它容易陷入局部最优.笔者通过给出一种混合的策略——遗传免疫粒群算法,将遗传算法,免疫算法引入到微粒群算法中,既能提高全局搜索能力,避免在搜索过程中陷入局部最优,又使算法保留了种群多样性的特点,提高算法的收敛速度.将该算法应用于网络拥塞控制中,提出一种基于混合遗传免疫粒群优化的网络拥塞控制方法来解决网络拥塞问题,通过仿真研究,验证了该方法的可行性.
Abstract:
Particle swarm optimization ( PSO) algorithm has the features of rapid calculation speed and simplerealization. But it easily falls into local optimum when dealing with actual problems. This paper presents akind of hybrid strategy : the genetic immune PS0. We introduce the genetic algorithm,the immune algorithmto PSO,which can improve the global search ability,avoid faling into local optimal in searching process,andimprove the convergence rate of the algorithm by reserving the diversity of population algorithm. Applying thealgorithm to the network congestion control,we proposed a network congestion method based on hybrid geneticimmune PS0 to solve the network congestion phenomenon. The simulation results show the feasibility of thismethod.

相似文献/References:

[1]杨文强,张素君,郭昊.求解仓储作业优化问题的多物种协同进化算法[J].郑州大学学报(工学版),2020,41(06):33.[doi:10.13705/j.issn.1671-6833.2019.03.030]
　YANG Wenqiang,ZHANG Sujun,GUO Hao.Operation Optimization of Warehousing by Multispecies Co-evolution Algorithm[J].Journal of Zhengzhou University (Engineering Science),2020,41(02):33.[doi:10.13705/j.issn.1671-6833.2019.03.030]
[2]段向军,王敏..基于改进的奇异值和遗传算法的人脸识别研究[J].郑州大学学报(工学版),2010,31(04):69.[doi:10.3969/j.issn.1671-6833.2010.04.017]
[3]杨华芬,杨有,尚晋..一种改进的进化神经网络优化设计方法[J].郑州大学学报(工学版),2010,31(05):116.[doi:10.3969/j.issn.1671-6833.2010.05.028]
[4]冯冬青,孔祥伟,许仿..城市恒压变频供水系统的一种智能优化控制策略[J].郑州大学学报(工学版),2011,32(01):85.[doi:10.3969/j.issn.1671-6833.2011.01.021]
[5]李阳,赵华东,杨威..基于遗传算法的二维不规则形排样研究[J].郑州大学学报(工学版),2011,32(04):56.[doi:10.3969/j.issn.1671-6833.2011.04.014]
[6]刘银芳,陈国荣,尤国英,等.基于microGA和有限元的混凝土坝热学参数反分析[J].郑州大学学报(工学版),2011,32(06):63.
　LIU Yin-fangCHEN Guo-rong,YOU Guo-ying,JIANG Chao.Back Analysis for Thermal Parameters of Concrete Dam with MicroGenetic Algorithm and Finite Element Method[J].Journal of Zhengzhou University (Engineering Science),2011,32(02):63.
[7]孙文彬,孙芳锦..大跨度屋盖风振控制的遗传算法研究[J].郑州大学学报(工学版),2012,33(01):40.[doi:10.3969/j.issn.1671-6833.2012.01.010]
　SUN Wenbin,SUN Fangjin.Study on Genetic Algorithms in Controlling of Wind-inducedVibration of Long-span Roofs[J].Journal of Zhengzhou University (Engineering Science),2012,33(02):40.[doi:10.3969/j.issn.1671-6833.2012.01.010]
[8]刘景艳,李玉东,杨晓邦..遗传神经网络在齿轮故障诊断中的应用[J].郑州大学学报(工学版),2012,33(03):36.[doi:10.3969/j.issn.1671-6833.2012.03.009]
　LIU Jingyan,LI Yudong,YANG Xiaobang.Application of Genetic Neural Network to Gear Fault Diagnosis[J].Journal of Zhengzhou University (Engineering Science),2012,33(02):36.[doi:10.3969/j.issn.1671-6833.2012.03.009]
[9]王来军,胡大伟,高扬..基于场景规划的随机型设施定位问题优化研究[J].郑州大学学报(工学版),2013,34(06):94.[doi:10.3969/j.issn.1671-6833.2013.06.023]
　WANGLaijun,HU Da·wei,GAO Yang.Researchon Optimization ofStochastic Facility Location Problem Based onScenario Planning[J].Journal of Zhengzhou University (Engineering Science),2013,34(02):94.[doi:10.3969/j.issn.1671-6833.2013.06.023]
[10]冯冬青,郭艳..遗传算法改进BP神经网络在地下水水质评价中的应用[J].郑州大学学报(工学版),2009,30(03):126.
　FENG Dongqing,GUO Yan.Application of Imoproved BP Neural Networks Based on Genetic Algorithms toGroundwater Quality Evaluation[J].Journal of Zhengzhou University (Engineering Science),2009,30(02):126.

更新日期/Last Update: 1900-01-01