基于改进量子粒子群的分布式并行计算框架设计  

Design of Distributed Parallel Computing Framework Based on Improved Quantum Particle Swarm

作　　者：王卫锋[1] 田亮[1] 

机构地区：[1]新乡学院计算机与信息工程学院,河南新乡453003

出　　处：《计算机测量与控制》2014年第6期1960-1962,1966,共4页Computer Measurement &Control

摘　　要：为了实现用户任务在大规模计算机集群上进行高效地处理,并克服现有并行计算框架通用性不强的缺点,提出了一种基于改进量子群算法和Map-Reduce模型的通用并行计算框架;首先,对经典的Map-Reduce分布式并行计算框架以及并行计算流程进行了具体描述;然后,基于改进的量子粒子群算法设计了改进的Map-Reduce模型,在Map阶段通过多种群并行搜索并计算所有粒子适应度,在Shuffle和Sort阶段实现粒子的排序和种群的重新划分,然后在Reduce阶段更新控制系数和粒子位置,当最优解不变时,通过混沌扰动对其进行扰动;仿真实验表明同,文中设计的基于改进量子粒子群算法和Map-Reduce模型能高效地执行任务,较传统的MapReduce模型具有较少的执行时间,具有很强的可行性,是一种有效的通用并行计算模型。In order to realize ellectlve management ot user tasks In the large computer group, and conquer the defects of low universality of the given parallel computing framework, a parallel computing framework is propoesd based on improved Quantum particle swarm al- gorithm and Map--Reduce model. Firstly, the classic Map--Reduce model and the parallel computing flow were described. Then the im- proved Map--Reduce model was designed based on improved Quantum particle swarm algorithm, the multi--population was parallel searched and the fitness was computed, and the particle was sorted and the particle population was divided, then the control coefficient and particle po- sition were renewed in the Reduce stage, when the global solution was unchanged, the particle was changed by chaos interrupt. The simula- tion experiment shows the method in this paper can execute task effectively, and compared with the traditional Map--Reduce model it has the less execution time. Therefore, the method in this paper has strong feasibility and universal parallel computing model.

关 键 词：量子粒子群 任务 并行计算 混沌 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]