基于改进粒子群算法的云计算产业联盟知识搜索算法研究  

Knowledge Search for Cloud Computing Industry Alliance:An Algorithm Based on Improved Particle Swarm Optimization

作　　者：高长元[1,2] 于建萍[1] 何晓燕[1,2] Gao Changyuan Yu Jianping He Xiaoyan(College of Management, Harbin University of Science and Technology, Harbin 150040, China High-tech Industrial Development Research Center, Harbin University of Science and Technology, Harbin 150040, China)

机构地区：[1]哈尔滨理工大学管理学院,哈尔滨150040 [2]哈尔滨理工大学高新技术产业发展研究中心,哈尔滨150040

出　　处：《数据分析与知识发现》2017年第3期81-89,共9页Data Analysis and Knowledge Discovery

基　　金：黑龙江省自然科学基金项目"黑龙江省移动云计算联盟商业模式研究"(项目编号:G201301)的研究成果之一

摘　　要：【目的】利用改进的粒子群算法进行云计算产业联盟知识搜索,提高搜索的准确率和效率。【方法】首先利用MapReduce中Map函数对粒子分组实现并行化处理,再运用Reduce函数对粒子搜索的结果进行归约,缩短搜索的时间。在粒子搜索过程中,根据小组内最优位置的平均值进行小组内粒子的信息交互,避免算法早熟收敛于一个局部最优值。【结果】通过三组仿真实验对改进的粒子群算法和标准粒子群算法进行对比分析,结果表明改进的粒子群算法在效率与准确率方面均具有明显的优越性。【局限】样本数据存在干扰数据,有待改进。【结论】该方法能提高云计算产业联盟知识搜索的准确性,并提升搜索效率。[Objective] This paper uses an algorithm based on the improved particle swarm optimization to conduct knowledge search for cloud computing industry alliance, aiming to improve its accuracy and efficiency. [Methods] First, we utilized the Map function of the MapReduce model to process particle grouping. Secondly, we used the Reduce function to shorten the particle search result lists and search time. Lastly, the information interaction of the particles was decided by the average value of the optimal position within each group, which avoided the premature convergence of using a local optimal value. [Results] We compared the performance of the improved algorithm with the standard ones by three rounds of simulation experiments. We found that the improved particle swarm algorithm was superior in efficiency and accuracy. [Limitations] There is some noisy data in the sample. [Conclusions] The proposed algorithm could improve the accuracy and efficiency of knowledge search for the cloud computing industry alliance.

关 键 词：云计算产业联盟 知识搜索 粒子群优化算法 MAPREDUCE 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]