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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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Computer Science and Application 
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  
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:2018822日;录用日期:2018830日;发布日期:201896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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