基于自适应遗传算法的网格任务调度优化  

Grid Task Scheduling Optimization Based on Adaptive Genetic Algorithm

作　　者：肖海蓉[1] 李惠先[2] 

机构地区：[1]陕西理工学院数学与计算机科学学院,陕西汉中723000 [2]河北金融学院信息管理与工程系,河北保定071051

出　　处：《吉林大学学报（理学版）》2015年第2期297-301,共5页Journal of Jilin University:Science Edition

基　　金：陕西省教育厅科研项目(批准号:12JK0863);陕西省汉中市科技发展专项计划项目(批准号:2013hzzx-43)

摘　　要：针对传统任务调度算法效率较低、资源负载不平衡等缺点,基于遗传算法,考虑现代网格系统异构性和动态性的特点,提出一种有效的交叉概率和变异概率自适应更新方法,提高遗传算法的全局搜索能力和收敛速度.仿真实验表明,改进后的遗传算法在进化速度上有明显提升,可较好地处理网格任务调度问题,提高任务调度效率,降低资源负载的不平衡性.In consideration of low efficiency,resource load imbalance of the traditional task scheduling algorithm and other shortcomings,and the heterogeneous and dynamic characteristics of a modern grid system,an effective adaptive update method for the crossover and mutation probability was proposed based on the genetic algorithms,which improves the global search ability and convergence speed of the genetic algorithm.Simulation results show that improved genetic algorithm enhances the speed of evolution significantly,can better handle grid task scheduling,improve task scheduling efficiency and reduce resource load imbalance with high practicality.

关 键 词：遗传算法 自适应 网格 任务调度 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]