云计算中基于生物共生机制改进粒子群优化的任务调度方案

王琳杰

doi:10.11959/j.issn.1000-0801.2016241

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云计算中基于生物共生机制改进粒子群优化的任务调度方案

研究与开发 | 更新时间：2024-06-05

- 云计算中基于生物共生机制改进粒子群优化的任务调度方案
- Task scheduling scheme based on improved particle swarm optimization with biological symbiosis mechanism in cloud computing
- 电信科学 2016年32卷第9期页码：113-119
- 作者机构：
- 作者简介：
  
  [ "王琳杰(1981-)，男，铜仁学院副教授，主要研究方向为计算机网络、密码学。" ]
- 基金信息：
  
  国家自然科学基金资助项目;The National Natural Science Foundation of China(61364003);国家自然科学基金资助项目;The National Natural Science Foundation of China(61562703);贵州省联合基金资助项目(黔科合J字);Guizhou Province Mutual Foundation(LKT[2012]16号)
- DOI：10.11959/j.issn.1000-0801.2016241
  中图分类号： TP311
- 网络出版日期：2016-09，
  
  纸质出版日期：2016-09-15
- 稿件说明：
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王琳杰. 云计算中基于生物共生机制改进粒子群优化的任务调度方案[J]. 电信科学, 2016,32(9):113-119.

Linjie WANG. Task scheduling scheme based on improved particle swarm optimization with biological symbiosis mechanism in cloud computing[J]. Telecommunications science, 2016, 32(9): 113-119.
王琳杰. 云计算中基于生物共生机制改进粒子群优化的任务调度方案[J]. 电信科学, 2016,32(9):113-119. DOI： 10.11959/j.issn.1000-0801.2016241.

Linjie WANG. Task scheduling scheme based on improved particle swarm optimization with biological symbiosis mechanism in cloud computing[J]. Telecommunications science, 2016, 32(9): 113-119. DOI： 10.11959/j.issn.1000-0801.2016241.

摘要

针对传统的基于智能算法的云计算任务调度方案获取最优解用时较多的问题，受生物界共生现象的启发，提出一种基于生物共生机制(SM)改进粒子群优化(PSO)的任务调度方案。首先，将PSO中的粒子分成2个种群，各自执行寻优。然后，每执行k次PSO迭代后，将两个种群中的个体进行互利共生和寄生操作。通过互利共生操作使搜索过程穿过最佳解区域，从而增强搜索能力；通过寄生操作排除较差解并引入较优解来防止过早收敛。最终获得任务调度的最优解。仿真结果表明，提出的优化算法可快速收敛，相比其他几种较新的调度方案，提出的方案能够获得最小的任务完成时间和响应时间。

Abstract

For the issues that the existing task scheduling scheme based on intelligent algorithms can’t obtain the optimal solution in cloud computing and inspired by nature symbiosis

a new task scheduling scheme based on improved particle swarm optimization(PSO)with biological symbiosis mechanism(SM)was proposed.Firstly

the particles in PSO were divided into two populations

and the optimization process were performed alone.Then

after each execution of the k iteration of PSO

the individual in the two populations performed the mutualism and parasitism operation.The search process was optimized by mutualism operation to through the optimal solution region

which could enhance the search ability.The parasitism operation was used to avoid premature convergence by eliminating the poor and introducing the optimal solution.Finally

the optimal solution of the task scheduling was obtained.Simulation results show that the optimal scheduling scheme can obtain the minimum task completion time and response time.

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references

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