Research on Improved Hybrid Particle Swarm Optimization Algorithm for Cloud Computing Task Scheduling

被引：0
|
作者：
Yang, Xiaoguang [1 ]
Wang, Qian [1 ]
Zhang, Yimin [1 ]
机构：
[1] Dalian Neusoft Inst Informat, Dalian 116023, Liaoning, Peoples R China
来源：
PROCEEDINGS OF THE 2018 8TH INTERNATIONAL CONFERENCE ON MANAGEMENT, EDUCATION AND INFORMATION (MEICI 2018) | 2018年 / 163卷
关键词：
Cloud computing; Task scheduling; Ant colony algorithm; Hybrid PSO algorithm;
D O I：
暂无
中图分类号：
G40 [教育学];
学科分类号：
040101 ; 120403 ;
摘要：
In the cloud computing environment, one of the hot spot of researches in cloud computing is how to accomplish the service request in numerous running tasks. This paper puts forward an improved hybrid particle swarm optimization, firstly using particle swarm algorithm as the main level algorithm, the initial solution rapidly, followed by the max min ant colony algorithm, as the algorithm to find the optimal solution based on the initial solution. Finally, the availability and advantage of the proposed algorithm can be tested through the simulation experiment.
引用
收藏
页码：1162 / 1167
页数：6
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