摘要
粒子群算法的局部早熟问题会降低云平台任务调度的最大效率,通过混沌搜索生成一个混沌序列来替代早熟的粒子,使早熟粒子及时跳出局部最优,尽快地搜索到最优解.实验结果表明,优化后的粒子群算法的云平台总的任务调度时间明显缩短,表明该方法提高了云平台任务调度的效率.
The task scheduling efficiency of cloud platform is one of the important factors that influence the efficiency and resource utilization of the whole cloud platform. Particle swarm optimization algorithm is a efficient optimal algorithm,So use the particle swarm algorithm to realize the task scheduling of cloud platform is helpful to improve the efficiency of scheduling. But the premature local problem of particle swarm algorithm can reduce the maximum efficiency of task scheduling, Through the chaotic search to generate a chaotic sequence to replace the precocious particle, make the precocious particle to jump out of local optimum in time. This makes the particle can search optimal solution as soon as possible and improve the efficiency of task scheduling in cloud platform. The experimental results show that the task scheduling time of the cloud platform based on optimized particle swarm algorithm is shortened obviously,and improve the efficiency of task scheduling in cloud platform.
出处
《内蒙古师范大学学报（自然科学汉文版）》
CAS
北大核心
2016年第2期233-236,共4页
Journal of Inner Mongolia Normal University(Natural Science Edition)
基金
贵州省2014年省级本科教学工程项目
贵阳市科技计划项目
关键词
云平台
粒子群算法
混沌搜索
任务调度
cloud platform
particle swarm optimization algorithm
chaotic search
scheduling
作者简介
于国龙（1981-），男（满族），辽宁省丹东市人，贵州师范学院讲师，主要从事嵌入式和物联网相关技术研究．
