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【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】 The improved Particle Swarm Optimization (PSO) is used to search knowledge of cloud computing industry alliance and improve the accuracy and efficiency of searching. 【Method】 Firstly, the Map function of MapReduce was used to parallelize the grouping of particles, then Reduce function was used to reduce the result of particle search and the search time was shortened. During the particle search process, the information of the particles in the group is exchanged according to the average value of the optimal position in the group to avoid the premature convergence of the algorithm to a local optimal value. 【Result】 The results show that the improved particle swarm optimization has obvious superiority in terms of efficiency and accuracy through three groups of simulation experiments comparing the improved PSO with the standard PSO. [Limitations] sample data interference data to be improved. 【Conclusion】 This method can improve the accuracy of cloud computing industry alliance knowledge search and improve search efficiency.