《计算机应用研究》|Application Research of Computers

基于改进的鸡群算法在云计算资源调度中的研究

Based on improved chicken swarm optimization in cloud computing resource scheduling

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作者 陈暄,龙丹
机构 1.浙江工业职业技术学院,浙江 绍兴 312000;2.浙江大学,杭州 310058
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文章编号 1001-3695(2019)09-005-2584-04
DOI 10.19734/j.issn.1001-3695.2018.03.0161
摘要 针对云计算中的资源调度效率低的问题,提出将改进后的鸡群算法用于调度。引入反向学习概念对鸡群种群进行初始化,提高全局搜索能力。对小鸡的位置引入了粒子群算法中的权重值和学习因子的概念进行改进,优化了鸡群个体位置,通过差分算法对鸡群算法整体的个体位置进行优化,最后通过边界处理从整体上预防了算法中个体位置可能出现的越界。在仿真实验中,将优化后的鸡群算法与基本鸡群算法、粒子群算法和蚁群算法进行在完成时间、花费成本、能量消耗和负载均衡中进行了对比,取得了较好的效果。
关键词 鸡群算法; 反向学习; 学习因子; 差分算法
基金项目 国家自然科学基金资助项目(LQ18A010003,11426205)
绍兴市科技局项目(2015B70013)
本文URL http://www.arocmag.com/article/01-2019-09-005.html
英文标题 Based on improved chicken swarm optimization in cloud computing resource scheduling
作者英文名 Chen Xuan, Long Dan
机构英文名 1.Zhejiang Industry Polytechnic College,Shaoxing Zhejiang 312000,China;2.Zhejiang University,Hangzhou 310058,China
英文摘要 In order to solve the problem of low efficiency of resource scheduling in cloud computing, it was proposed to sche-dule the improved chicken swarm optimization. First, the concept of reverse learning was used to initialize the chicken swarm and improve the global search capability. Secondly, the position of chick was introduced into the concept of weight value and learning factor in particle swarm optimization to improve the individual position of the flock; the individual position of the chicken swarm optimization was again optimized by the difference algorithm, and finally the whole was processed by boundary processing to prevent possible cross-border of individual locations in the optimization. In the simulation experiment, the optimized chicken swarm optimization and the basic chicken swarm optimization, the particle swarm optimization algorithm and the ant colony algorithm are compared in terms of completion time, cost, energy consumption and load balance, and good results have been achieved.
英文关键词 chicken swarm optimization(CSO); reverse learning; learning factor; difference algorithm
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收稿日期 2018/3/14
修回日期 2018/4/18
页码 2584-2587
中图分类号 TP301
文献标志码 A