[1]张晓丽. 自适应CPSO算法在云计算任务调度中的应用[J].计算机技术与发展,2016,26(08):161-165.
　ZHANG Xiao-li. Application of Self-adaptive Chaos Particle Swarm Optimization in Task Scheduling for Cloud Computing[J].,2016,26(08):161-165.
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 自适应CPSO算法在云计算任务调度中的应用()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
26
期数:
2016年08期
页码:
161-165
栏目:
应用开发研究
出版日期:
2016-08-10

文章信息/Info

Title:
 Application of Self-adaptive Chaos Particle Swarm Optimization in Task Scheduling for Cloud Computing
文章编号:
1673-629X（2016)08-0161-05
作者:
 张晓丽
 西安航空学院 计算机学院
Author(s):
 ZHANG Xiao-li
关键词:
 ; 云计算; 任务调度; 粒子群优化算法; 混沌
Keywords:
 ; cloud computing; task scheduling; Particle Swarm Optimization ( PSO); chaos
分类号:
TP393
文献标志码:
A
摘要:
 云计算环境中的任务调度问题一直是云计算研究的重点。为了克服传统PSO算法易陷入局部最优的缺陷，针对云计算的编程模型框架，将混沌优化搜索技术应用于粒子群优化算法，提出一种基于Tent映射的自适应混沌粒子群任务调度算法。该算法结合了PSO算法的快速收敛性和混沌运动的遍历性、随机性，在初始化粒子的位置时采用混沌赋值的方式产生一系列初始解，再根据每个粒子个体的适应值来自适应地调整其惯性权重，对陷入局部最优的粒子群个体位置进行混沌更新，帮助其跳出局部最优。通过在CloudSim平台进行仿真对比实验，表明该改进算法有效地缩小了总任务的完成时间，具有较好的寻优能力和实时性，是一种有效的云计算任务调度算法。
Abstract:
 Tasks scheduling is an important issue to be resolved in cloud computing research. In order to overcome the defects of tradition-al PSO easy to fall into local optimum,in view of procedure model framework of cloud computing,the chaos optimization search tech-nique is applied to the particle swarm optimization,and an adaptive chaotic particle swarm algorithm of task scheduling based on Tent mapping is presented. It combines the fast convergence of PSO and the ergodic property of chaotic motion,with chaotic assignment way in initializing particle position,and then adaptively adjusts its inertia weight according to the fitness value of each individual particle,up-dating chaos location for particle swarm individual to help themselves escape from local optima. Through simulation experiment on the CloudSim platform,the results show that the ACPOS,with a good real-time performance and optimization ability,significantly reduces the completion time of the task,which is an efficient task scheduling algorithm.

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更新日期/Last Update: 2016-09-29