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| 改进粗粒度并行遗传算法在网格任务调度中的应用 | |
| 引用本文： | 薛胜军,刘芳芳,唐晨杰. 改进粗粒度并行遗传算法在网格任务调度中的应用[J]. 计算机测量与控制, 2012, 20(2): 487-489 |
| 作者姓名： | 薛胜军 刘芳芳 唐晨杰 |
| 作者单位： | 1. 南京信息工程大学江苏省网络控制中心,江苏南京210044;南京信息工程大学计算机与软件学院,江苏南京210044 2. 南京信息工程大学计算机与软件学院,江苏南京,210044 3. 电子科技大学电子工程学院,四川成都,700071 |
| 摘 要： | 现有并行遗传算法采用随机方法划分子种群,算法收敛性能不高,并且不可避免的破坏种群的较优模式;为了改进这些缺陷,设计了一种新的多点交叉算子,提出了一种改进的粗粒度并行遗传算法;取资源数为6,任务数为50,种群的规模为60,遗传代数为600;采用相同的控制参数进行仿真实验;仿真实验表明,与传统并行遗传算法相比较,提出的改进算法在收敛速度和寻优空间方面有很大的提升。 |
| 关 键 词： | 网格 任务调度 聚类 并行遗传算法 |
Application of an improved Coarse-Grained Parallel Genetic Algorithm to Grid Task Scheduling |
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| Xue Shengjun , Liu Fangfang , Tang Chenjie. Application of an improved Coarse-Grained Parallel Genetic Algorithm to Grid Task Scheduling[J]. Computer Measurement & Control, 2012, 20(2): 487-489 | |
| Authors: | Xue Shengjun Liu Fangfang Tang Chenjie |
| Affiliation: | 1.Network Control Center of Jiangsu Province,Nanjing University of Information Science & Technology,Nanjing 210044,China; 2.School of Computer and Software,Nanjing University of Information Science & Technology,Nanjing 210044,China; 3.School of electronic and engineering,University of Science and Technology of China,Chengdu 710071,China) |
| Abstract: | Traditional parallel genetic algorithms adopt random method to divide sub-populations,convergence is not high and it inevitably damages better schema of the populations.In order to improve the faults,design a new crossover operator,this paper proposes an improved coarse-grained parallel genetic algorithm.Take resource number six,task number fifty and population size sixty.Use same parameters to do simulation experiment.The simulation experiment shows that compared with traditional parallel genetic algorithm convergence rate and optimizing space of the improved algorithm are greatly promoted. |
| Keywords: | grid task scheduling clustering parallel genetic algorithm |
| 本文献已被 CNKI 万方数据 等数据库收录！ | |