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多目标猫群优化算法支持下的云计算任务调度
引用本文:丁岳伟,窦飞飞.多目标猫群优化算法支持下的云计算任务调度[J].电子科技,2016,29(2):4.
作者姓名:丁岳伟  窦飞飞
作者单位:(1.上海理工大学 光电信息与计算机工程学院,上海 200093; 2.上海理工大学 计算机软件技术研究所,上海 200093)
基金项目:上海市自然科学基金资助项目(14ZR1440100);上海市教委科研创新重点基金资助项目(14ZS167)
摘    要:针对云环境下任务调度易出现多目标冲突的问题,提出一种改进的基于猫群的多目标优化算法。该算法模拟猫的行为模式,采用基于线性混合比率的猫行为选择方式来提高全局搜索和局部寻优能力;并在迭代过程中结合任务完成时间和任务费用支出,引入一个可调节的多目标集成效用函数,实现了资源与任务的智能调度。实验结果表明,所提算法不仅求解质量高,且在求解速度和调度消耗方面均优于多目标遗传算法和多目标粒子群算法。

关 键 词:多目标猫群算法  线性混合比率  多目标集成效用函数  智能调度  

Task Scheduling in Cloud Computing Based on Optimized Multi-objective Cat Swarm Algorithm
DING Yuewei,DOU Feifei.Task Scheduling in Cloud Computing Based on Optimized Multi-objective Cat Swarm Algorithm[J].Electronic Science and Technology,2016,29(2):4.
Authors:DING Yuewei  DOU Feifei
Affiliation:(1.School of Optical-Electrical and Computer Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China; 2.Institute of Computer Software Technology,University of Shanghai for Science and Technology,Shanghai 200093,China)
Abstract:Considering the multi-objective conflict problem for job scheduling in cloud computing environment,this paper proposes an optimized multi-objective cat swarm algorithm (MO-CSO) based on cat behavior using the linear mixture ratio selection method to improve the global search and local optimization ability.The algorithm combines the makespan and total cost to propose an adjustable multi-objective integrated utility function,which implements the intelligent dispatching of resources and task.The experimental results show that the optimized MO-CSO not only has high quality,but also are superior in solving speed and scheduling costs to the multi-objective genetic algorithm (MO-GA) and the multi-objective particle swarm optimization (MO-PSO) algorithm.
Keywords:multi objective cat swarm algorithm  linear mixing ratio  a multi objective integrated utility function  intelligent scheduling  
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