[1]陈宏明,章 慧.改进的文化粒子群算法的电子商务服务器集群负载均衡[J].南京师范大学学报(工程技术版),2014,14(03):079.
 Chen Hongming,Zhang Hui.Load Balance for Large E-Commerce Server Cluster Based on ImprovedCultural Particle Swarm Optimization Algorithm[J].Journal of Nanjing Normal University(Engineering and Technology),2014,14(03):079.
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改进的文化粒子群算法的电子商务服务器集群负载均衡
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南京师范大学学报(工程技术版)[ISSN:1006-6977/CN:61-1281/TN]

卷:
14卷
期数:
2014年03期
页码:
079
栏目:
出版日期:
2014-09-30

文章信息/Info

Title:
Load Balance for Large E-Commerce Server Cluster Based on ImprovedCultural Particle Swarm Optimization Algorithm
作者:
陈宏明章 慧
淮阴工学院计算机工程学院,江苏 淮安 223003
Author(s):
Chen HongmingZhang Hui
College of Computer Engineering,Huaiyin Institute of Technology,Huai’an 223003,China
关键词:
电子商务服务器集群负载均衡文化算法粒子群算法
Keywords:
E-commerceserver clusterload balancingcultural algorithmparticle swarm algorithm
分类号:
TP39
文献标志码:
A
摘要:
随着电子商务的发展,电子商务企业服务器集群负载均衡问题越来越严重,为了解决粒子群算法在求解电子商务服务器集群负载均衡问题上存在的不足,提出一种改进的文化粒子群算法的服务器集群负载均衡策略.首先利用粒子群算法的主群体空间和文化算法的知识空间形成“双演化双促进”机制,提高算法全局搜索能力和运行效率; 然后引入遗传算法进化机制对知识空间演化操作进行改进,最后将该算法应用于电子商务服务器集群负载均衡问题求解.经过仿真验证,改进文化粒子群算法,提高服务器集群系统资源利用率,负载更加均衡.
Abstract:
In order to solve problems of particle swarm optimization algorithm in solving the load balancing for large E-commerce server cluster,this paper proposes a large E-commerce load balance method based on improved cultural particle swarm optimization algorithm.Firstly,a main population space of particle swarm algorithm and spatial knowledge of cultural algorithm form the "dual evolution and dual promotion" mechanism to improve global search capability and efficiency; and the evolutionary mechanism of genetic algorithm is introduced to improve the knowledge space and avoid self limiting of culture algorithm,and finally,the algorithm is applied to the solution of load balancing problem for large E-commerce server cluster.The simulation results show that the proposed algorithm has improved resource utilization rate of large E-commerce server cluster system and that the load is more balanced.

参考文献/References:

[1] 温涛,盛国军,郭权,等.基于改进粒子群算法的Web服务组合[J].计算机学报,2013,36(5):1 031-1 046.
Wen Tao,Sheng Gaojun,Guo Quan,et al.Web service composition based on modified particle swarm optimization[J].Chinese Journal of Computers,2013,36(5):1 031-1 046.(in Chinese)
[2]胡旺,Gary G Y,张鑫.基于Pareto熵的多目标粒子群优化算法[J].软件学报,2014,25(5):117-139.
Hu Wang,Gary G Y,Zhang Xin.Multiobjective particle swarm optimization on pareto entropy[J].Journal of Software,2014,25(5):117-139.(in Chinese)
[3]毛澄映,喻新欣,薛云志.基于粒子群优化的测试数据生成及其实证分析[J].计算机研究与发展,2014,51(4):136-149.
Mao Chengying,Yu Xinxin,Xue Yunzhi.Algorithm design and empirical analysis for particle swarm optimization-based test data generation[J].Journal of Computer Research and Development,2014,51(4):136-149.(in Chinese)
[4]朱喜华,李颖晖,李宁,等.基于群体早熟程度和非线性周期振荡策略的改进粒子群算法[J].通信学报,2014,35(2):182-189.
Zhu Xihua,LI Yinghui,Li Ning,et al.Improved PSO algorithm based on swarm prematurely degree and nonlinear periodic oscillating strategy[J].Journal on Communications,2014,35(2):182-189.(in Chinese)
[5]马双良,张英敏,宋丽君.基于LVS和计算任务的实时集群负载均衡方法[J].计算机工程与设计,2007,28(20):4 934-4 937.
Ma Shuangliang,Zhang Yingmin,Song Lijun.Load balancing method for real-time cluster based on LVS and handling tasks[J].Computer Engineering and Design,2007,28(20):4 934-4 937.(in Chinese)
[6]郑宇军,陈胜勇,凌海风,等.多Agent主从粒子群分布式计算框架[J].软件学报,2012,23(11):3 000-3 008.
Zheng Yujun,Chen Shengyong,Ling Haifeng,et al.Multi-agent based distributed computing framework for master-slave particle swarms[J].Journal of Software,2012,23(11):3 000-3 008.(in Chinese)
[7]马超,邓超,熊尧,等.一种基于混合遗传和粒子群的智能优化算法[J].计算机研究与发展,2013,50(11):2 278-2 286.
Ma Chao,Deng Chao,Xiong Yao,et al.An intelligent optimization algorithm based on hybrid of GA and PSO[J].Journal of Computer Research and Development,2013,50(11):2 278-2 286.(in Chinese)
[8]龚梅,王鹏,吴跃.一种集群系统的透明动态反馈负载均衡算法[J].计算机应用,2007,27(11):2 662-2 665.
Gong Mei,Wang Peng,Wu Yue.Transparency dynamic feed-back load balancing algorithm based on cluster system[J].Computer Application,2007,27(11):2 662-2 665.(in Chinese)
[9]蒋鹏,宋华华,林广.基于粒子群优化和M-H抽样粒子滤波的传感器网络目标跟踪方法[J].通信学报,2013,34(11):8-17.
Jiang Peng,Song Huahua,Lin Guang.Target tracking algorithm for wireless sensor networks based on particle swarm optimization and metropolis-hasting sampling particle filter[J].Journal on Communications,2013,34(11):8-17.(in Chinese)
[10]刘健,徐磊,张维明.基于动态反馈的负载均衡算法[J].计算机工程与科学,2003,25(5):65-68.
Liu Jian,Xu Lei,Zhang Weiming.A load balancing algorithm based on dynamic feed-back[J].Computer Engineering and Science,2003,25(5):65-68.(in Chinese)
[11]查日军,张德平,聂长海,等.组合测试数据生成的交叉熵与粒子群算法及比较[J].计算机学报,2010,33(10):1 896-1 908.
Zha Rijun,Zhang Deping,Nie Changhai,et al.Test data generation algorithms of combinatorial testing and comparison based on cross-entropy and particle swarm optimization method[J].Chinese Journal of Computers,2010,33(10):1 896-1 908.(in Chinese)
[12]王奕首,艾景波.文化粒子群优化算法[J].大连理工大学学报,2007,47(4):540-541.
Wang Yishou,Ai Jingbo.Cultural-based particle swarm optimization algorithm[J].Journal of Dalian University of Technology,2007,47(4):540-541.(in Chinese)

备注/Memo

备注/Memo:
收稿日期:2014-01-19.
基金项目:淮安市产学研联合研究项目(HC201308).
通讯联系人:陈宏明,副教授,研究方向:计算机网络应用、图形图像处理.E-mail:chm6219@qq.com
更新日期/Last Update: 2014-09-30