基于改进蚁群算法和粒子群算法的云计算资源调度  

Cloud Computing Resource Scheduling Based on Improved Ant Colony Algorithm and Particle Swarm Algorithm

作　　者：单好民[1] 

机构地区：[1]浙江邮电职业技术学院,绍兴312000

出　　处：《计算机系统应用》2017年第6期187-192,共6页Computer Systems & Applications

基　　金：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)

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.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.

关 键 词：资源分配 惯性权重 信息素 蚁群算法 粒子群算法 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TP3[自动化与计算机技术—控制科学与工程]