[1]王秉.高速收敛混沌粒子群算法的云计算任务调度[J].华侨大学学报(自然科学版),2015,36(6):650-654.[doi:10.11830/ISSN.1000-5013.2015.06.0650]
　WANG Bing.Cloud Computing Task Scheduling of High-Speed Convergence of Chaotic Particle Swarm Optimization[J].Journal of Huaqiao University(Natural Science),2015,36(6):650-654.[doi:10.11830/ISSN.1000-5013.2015.06.0650]
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高速收敛混沌粒子群算法的云计算任务调度()
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《华侨大学学报（自然科学版）》[ISSN:1000-5013/CN:35-1079/N]

卷:
第36卷
期数:
2015年第6期
页码:
650-654
栏目:
出版日期:
2015-11-10

文章信息/Info

Title:
Cloud Computing Task Scheduling of High-Speed Convergence of Chaotic Particle Swarm Optimization
文章编号:
1000-5013(2015)06-0650-05
作者:
王秉
河南交通职业技术学院 航运海事系, 河南 郑州 450000
Author(s):
WANG Bing
Department of Maritime, Henan Vocational and Technical College of Communications, Zhengzhou 450005, China
关键词:
云计算;  任务调度;  粒子群算法;  混沌
Keywords:
cloud computing;  task scheduling;  particle swarm optimization algorithm;  chaotic
分类号:
TP393
DOI:
10.11830/ISSN.1000-5013.2015.06.0650
文献标志码:
A
摘要:
针对传统粒子群算法在处理云计算任务调度问题时,存在求解精度不高、容易陷入早熟收敛等缺陷,提出一种改进的高速收敛混沌粒子群算法.首先,采用混沌序列对初始化过程进行优化;其次,利用适应度方差对早熟现象进行有效诊断,并对算法在负梯度方向进行修正,使其跳出局部最优,实现高速收敛.仿真实验表明:改进后的粒子群算法能有效地避免早熟,收敛速度及求解精度都明显提高,非常适合云计算任务调度.
Abstract:
In this paper, we proposed an advanced high speed of convergence chaotic particle swarm algorithm to adjust the common problems of traditional particle swarm algorithm such as low accuracy and easily trapped in premature convergence during the cloud computing task scheduling. Firstly, the initial process was optimzed by chaotic sequence. Then, the effective diagnosis of premature phenomenon was determined by fitness variance. The algorithm correction was performed by negative gradient direction, which could jump out the local optimum and achieve high speed of convergence.Simulation experiments show that the improved particle swarm algorithm can effectively avoid premature, enhance convergence speed and solution accuracy, which is suitable for cloud computing task scheduling.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期: 2015-10-08
通信作者: 王秉(1965-),男,副教授,主要从事计算机图形图像的研究.E-mail:wbjtxy@163.com.
基金项目: 国家自然科学基金资助项目(201411326136); 河南省科技厅项目(2013132300410337)
更新日期/Last Update: 2015-11-20