摘要
云计算资源的调度是云计算中的一项关键技术.针对粒子群算法存在易陷入局部最优解和"早熟"的缺陷,提出一种改进的粒子群算法.通过改进粒子迭代过程中社会项系数和认知项系数的权重变化,使算法更符合最优解的求解规律,避免陷入局部最优解.仿真实验表明,改进后的粒子群算法适应度更强、收敛速度更快,具有更强的全局搜索能力.该算法可以有效提高云计算资源的利用率,具有良好的应用价值.
Cloud resource scheduling strategy is the most important technology in cloud computing.In order to solve the problem of being easy to to fall into locally optimal solution,we proposed an improved particle swarm optimization algorithm（IPSO）.Coefficient about social and personal cognition are changed according to the times of iteration,which can comply with objective rules much better and avoid falling to locally optimal solution.Simulation results show that IPSO can improve the availability of cloud resources.It has higher global search ability,better fitness,and good application value.
作者
蔡晓丽
钱诚
CAI Xiao-li;QIAN Cheng(School of Information ＆ Engineering, Changzhou Institute of Technology, Changzhou 213002,China)
出处
《微电子学与计算机》
CSCD
北大核心
2018年第6期28-30,35,共4页
Microelectronics & Computer
基金
国家自然科学基金(61602063)
常州工学院课题(A3-4403-17-011)
关键词
云计算
粒子群算法
资源调度
迭代
适应度
cloud computing
particle swarm optimization
resource scheduling
iteration
fitness
