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基于云计算任务调度的遗传粒子群优化算法
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(完整内容请下载后查看)Computer Science and Application 计
Genetic and Particle Swarm Optimization
Algorithm Based on Cloud Task Scheduling
Qing Wang, Xueliang Fu*, Honghui Li, Jianrong Li
College of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot
Inner Mongolia
Received: Aug. 22nd, 2018; accepted: Aug. 30th, 2018; published: Sep. 6th, 2018
Abstract
The task scheduling algorithm of cloud platform is a hot topic in the field of cloud computing. How
to achieve faster convergence speed while not meeting the local optimal solution has always been
one of the goals pursued by researchers. To this end, this paper proposes an enhanced genetic and
particle swarm optimization algorithm (GA_EPSO) that introduces an enhanced particle swarm
optimization algorithm (EPSO) with improved random factors and inertia weights into mutation
operations in genetic algorithm (GA). Reconstructing the mutation operator by the current optim-
al solution and the global optimal solution in enhanced particle swarm optimization algorithm,
the enhanced genetic and particle swarm optimization algorithm has a faster convergence speed
without falling into the local optimal solution. Simulation experiments show that under the same
conditions, compared with genetic algorithm (GA), improved genetic algorithm (IGA), particle
swarm optimization (PSO), enhanced particle swarm optimization (EPSO) and genetic particle
swarm optimization (GA_PSO), the algorithm not only accelerates the convergence speed, but also
has a significant improvement in task scheduling efficiency.
Keywords
Cloud Computing, Task Scheduling, Genetic Algorithm, Particle Swarm Optimization Algorithm,
Convergence Speed, Task Scheduling Efficiency
基于云计算任务调度的遗传粒子群优化算法
王
晴,付学良*,李宏慧,李建荣
内蒙古农业大学计算机与信息工程学院,内蒙古 呼和浩特
收稿日期:2018年8月22日;录用日期:2018年8月30日;发布日期:2018年9月6日
*通讯作者。
文章引用: 王晴, 度的遗传粒子群优化算法[J]. 计算机科学与应用, 2018,
8(9): 1334-1340. DOI
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