计算机应用 ›› 2019, Vol. 39 ›› Issue (11): 3328-3332.DOI: 10.11772/j.issn.1001-9081.2019040635

• 先进计算 • 上一篇    下一篇

云环境下方差定向变异遗传算法的任务调度

孙敏, 叶侨楠, 陈中雄   

  1. 山西大学 计算机与信息技术学院, 太原 030006
  • 收稿日期:2019-04-15 修回日期:2019-07-01 发布日期:2019-08-26 出版日期:2019-11-10
  • 通讯作者: 叶侨楠
  • 作者简介:孙敏(1965-),女,山西太原人,副教授,硕士,CCF会员,主要研究方向:云计算、Web智能、协同编辑;叶侨楠(1995-),女,山西长治人,硕士研究生,主要研究方向:云计算、人工智能;陈中雄(1992-),男,山西临汾人,硕士研究生,主要研究方向:云计算、人工智能。

Task scheduling of variance-directional variation genetic algorithm in cloud environment

SUN Min, YE Qiaonan, CHEN Zhongxiong   

  1. School of Computer and Information Technology, Shanxi University, Taiyuan Shanxi 030006, China
  • Received:2019-04-15 Revised:2019-07-01 Online:2019-08-26 Published:2019-11-10

摘要: 云环境下遗传算法(GA)的任务调度存在寻优能力差、结果不稳定等问题。对于上述问题,提出了一种基于方差与定向变异的遗传算法(V-DVGA)。在选择部分,在每一次迭代的过程中进行多次选择,利用数学方差来保证种群的多样性并扩大较优解的搜索范围。在交叉部分,建立新的交叉机制,丰富种群的多样性并提高种群整体的适应度。在变异部分,优化变异机制,在传统变异的基础上采用定向变异来提高算法的寻优能力。通过workflowSim平台进行云环境仿真实验,将此算法与经典的遗传算法和当前的基于遗传算法的工作流调度算法(CWTS-GA)进行比较。实验结果表明,在相同的设置条件下,该算法在执行效率、寻优能力和稳定性等方面优于其他两个算法,是一种云计算环境下有效的任务调度算法。

关键词: 云环境, 任务调度, 遗传算法, 方差, 定向变异

Abstract: The task scheduling of Genetic Algorithm (GA) in cloud environment has problems such as poor optimization ability and unstable results. For the above problems, a Variance-Directional Variation GA (V-DVGA) was proposed. In the selection part, multiple selections were made in the process of each iteration, and the mathematical variance was used to ensure the diversity of the population and expand the search range of the better solution. In the intersection part, a new intersection mechanism was established to enrich the diversity of the population and improve the overall fitness of the population. In the variation part, the variation method was improved, the directional variation was used on the basis of the traditional variation to increase the optimization ability of the algorithm. The cloud environment simulation experiments were carried out on the workflowSim platform, and the proposed algorithm was compared with the classical GA and the current Workflow Scheduling Algorithm based on Genetic Algorithm (CWTS-GA). The experimental results show that under the same setting conditions, the proposed algorithm is superior to the other two algorithms in terms of execution efficiency, optimization ability and stability, and is an effective task scheduling algorithm in cloud computing environment.

Key words: cloud environment, task scheduling, Genetic Algorithm (GA), variance, directional variation

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