移动云计算多目标任务调度进化算法

张小庆; 安春玲; 胡亚捷

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移动云计算多目标任务调度进化算法

更新时间：2026-09-04

- 移动云计算多目标任务调度进化算法
- Multi-objective task scheduling evolutionary algorithm in mobile cloud computing
- 微电子学与计算机 2020年37卷第10期页码：79-86
- 作者机构：
  
  1. 武汉轻工大学数计学院,湖北,武汉,430023
  2. 武汉轻工大学机构图书馆,湖北,武汉,430023
- 作者简介：
- 基金信息：
  
  湖北省自然科学基金项目 2018CFB407武汉轻工大学校立科研项目 2019y07
- DOI：
  中图分类号：
- 纸质出版：2020
- 稿件说明：
移动端阅览
张小庆, 安春玲, 胡亚捷. 移动云计算多目标任务调度进化算法[J]. 微电子学与计算机, 2020, 37(10): 79-86.

ZHANG Xiao-qing, AN Chun-lin, HU Ya-jie. Multi-objective task scheduling evolutionary algorithm in mobile cloud computing[J]. 2020, 37(10): 79-86.
张小庆, 安春玲, 胡亚捷. 移动云计算多目标任务调度进化算法[J]. 微电子学与计算机, 2020, 37(10): 79-86. DOI：

ZHANG Xiao-qing, AN Chun-lin, HU Ya-jie. Multi-objective task scheduling evolutionary algorithm in mobile cloud computing[J]. 2020, 37(10): 79-86. DOI：

摘要

研究了移动云环境中任务调度的多目标优化问题，提出一种多目标任务调度进化算法MTSEA.建立了截止时间、预算及能量约束下的任务调度多目标优化模型，模型引入执行跨度、执行代价及执行能耗三目标最优化；设计了一种进化算法对冲突三目标最优化进行求解，算法重点在种群初始化操作中引入了效率最高、代价最小以及能效最高的三个种群个体，以此代替随机个体生成；并利用交叉和变异操作对个体进化迭代，最终通过非占优排序形式得到满足帕累托最优的调度解集.通过仿真实验与两种多目标调度算法进行了性能对比.结果表明，MTSEA算法调度解的收敛性及解空间距离和分布上是更优的.

Abstract

The multi-objective optimization problem of tasks scheduling in mobile cloud is studied

a multi-objective scheduling evolutionary algorithm MTSEA is proposed. The multi-objective opitization model of tasks scheduling with the constraints of deadline

budget and energy

which introduces the execution makespan

execution cost and execution energy consumption as tre-objective optimization. An evolationary algorithm is presented to solve this tre-objective optimization problem with conflict. Our algorithm focuses to introduce three population individual with highest efficiency

minimal cost and highest energy-efficiency in the population initialization instead of randomly generated particles. And

our algorithm uses the evolutionary iteration of the crossover and mutation operation among population individuals. Finally the algorithm can generate the scheduling solution set meeting Pareto optimal by the form of the not-dominant order. Some simulation experiments are constructed and the performance comparsion are performed with other two kinds of multi-objective scheduling algorithms. The results show that MTSEA performs better on the convergence of scheduling solutions and the solution space distance and its distribution.

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references

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