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Received:October 23, 2016 Revised:December 19, 2016
Received:October 23, 2016 Revised:December 19, 2016
中文摘要: 针对云计算资源分配中存在分配不均、分配效果不好的问题,利用改进后的蚁群算法和粒子群算法进行资源分配.首先针对粒子群算法的惯性权值进行改进,设定适应度函数并选择最佳位置的粒子,然后将该粒子的位置转变为蚁群算法的初始信息素的值,通过狼群算法改进蚁群算法的信息素的选择.仿真实验表明,本文算法与蚁群算法、粒子群算法相比在任务完成时间、能量消耗方面都有了明显的改善.
Abstract:Aiming at uneven resource distribution in cloud computing and bad distribution effect, this paper distributes resources in improved and colony algorithm and particle swarm algorithm. First of all, improve the inertia weight value of particle swarm algorithm, set the fitness function and select particles at the optimal location, then convert the location of selected particles into the value of ant colony algorithm's initial pheromone and improve ant colony algorithm's selection of pheromone through wolves algorithm. Through simulation experiment, compared with ant colony algorithm and particle swarm algorithm, algorithm in this paper has been significantly improved in time to complete tasks and energy consumption.
keywords: resource distribution inertia weight pheromone ant colony algorithm particle swarm algorithm
文章编号: 中图分类号: 文献标志码:
基金项目:浙江省教育厅科研项目资助(Y201432433);浙江省教育技术研究规划课题(JB119)
| Author Name | Affiliation |
| SHAN Hao-Min | Zhejiang Technical College of Posts & Telecom, Shaoxing 312000, China |
| Author Name | Affiliation |
| SHAN Hao-Min | Zhejiang Technical College of Posts & Telecom, Shaoxing 312000, China |
引用文本:
单好民.基于改进蚁群算法和粒子群算法的云计算资源调度.计算机系统应用,2017,26(6):187-192
SHAN Hao-Min.Cloud Computing Resource Scheduling Based on Improved Ant Colony Algorithm and Particle Swarm Algorithm.COMPUTER SYSTEMS APPLICATIONS,2017,26(6):187-192
单好民.基于改进蚁群算法和粒子群算法的云计算资源调度.计算机系统应用,2017,26(6):187-192
SHAN Hao-Min.Cloud Computing Resource Scheduling Based on Improved Ant Colony Algorithm and Particle Swarm Algorithm.COMPUTER SYSTEMS APPLICATIONS,2017,26(6):187-192

