Knowledge Search for Cloud Computing Industry Alliance: An Algorithm Based on Improved Particle Swarm Optimization
Gao Changyuan1,2(), Yu Jianping1, He Xiaoyan1,2
1College of Management, Harbin University of Science and Technology, Harbin 150040, China 2High-tech Industrial Development Research Center, Harbin University of Science and Technology, Harbin 150040, China
[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.
高长元, 于建萍, 何晓燕. 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*[J]. 数据分析与知识发现, 2017, 1(3): 81-89.
Gao Changyuan,Yu Jianping,He Xiaoyan. Knowledge Search for Cloud Computing Industry Alliance: An Algorithm Based on Improved Particle Swarm Optimization. Data Analysis and Knowledge Discovery, 2017, 1(3): 81-89.
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