摘要
通过分析云储存系统的数据处理及存储的原理,提出基于粒群优化算法的云存储数据检索方法主要通过对云存储数据的关键词进行相似度对比,利用粒群优化算法的全局最优及局部最优算法,对查询数据进行匹配,直至寻找到最优查询结果。为了验证设计方法的可行性及性能,在Matlab软件中实现优化模型并构建实验场景,模拟云存储过程及数据检索过程,对此数据检索优化方法进行测试验证。仿真结果表明,在模型稳定性方面,粒群优化算法随着粒子位置的迭代,模型逐渐收敛且能够查询出最优解;在模型应用方面,查询响应延时较随机查询模型减少了34.7%,且准确率达到99.6%。总之,设计的基于粒群优化算法的云存储数据检索方法具有较高的检索精度及稳定性。
Through analyzing the principle of data storage processing in cloud storage system, the proposed cloud storage data retrieval method based on particle swarm optimization algorithm is mainly based on key words similarity comparison among cloud storage data using the global optimal and local optimal algorithm particle swarm optimization algorithm, and then make query data matching to find the optimal query results. In order to verify the feasibility of the designed method and its performance, authors achieve the optimization model and build the scene in the Matlab soft- ware, and make simulation of the process of cloud storage and data retrieval process to make validation to optimize data retrieval methods. Simulation results show that, in the aspect of model stability, particle swarm optimization algorithm can query the optimal solution with iterative particle position and model gradually convergence; In the aspect of model application, query response delay is decreased by 34.7% compared with random query model, which has 99.6% accuracy. All in all, the cloud storage data retrieval method based on particle swarm optimization algorithm this paper designed has high retrieval accuracy and stability.
出处
《激光杂志》
北大核心
2016年第11期98-102,共5页
Laser Journal
基金
江苏省高等职业院校国内高级访问学者计划资助项目(2013fx096)
关键词
云存储
粒群优化
数据检索
最优解
响应延时
cloud storage
particle swarm optimization
data retrieval
optimal solution
response latency
作者简介
刁爱军（1976-），男（汉族），江苏南京人，硕士，副教授，主要研究方向：计算机网络应用。
