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计算机工程 ›› 2012, Vol. 38 ›› Issue (08): 180-182. doi: 10.3969/j.issn.1000-3428.2012.08.059

• 人工智能及识别技术 • 上一篇    下一篇

基于MapReduce模型的并行量子进化算法

贾瑞玉1,刘范范1,潘雯雯1,王伟东2   

  1. (1. 安徽大学计算机科学与技术学院,合肥 230039;2. 泰山学院信息科学技术学院,山东 泰安 271021)
  • 收稿日期:2011-08-12 出版日期:2012-04-20 发布日期:2012-04-20
  • 作者简介:贾瑞玉(1965-),女,副教授,主研方向:智能计算,数据挖掘;刘范范、潘雯雯,硕士研究生;王伟东,助教
  • 基金资助:

    安徽省教育厅自然科学研究基金资助重点项目(2011A 006)

Parallel Quantum Evolutionary Algorithm Based on MapReduce Model

JIA Rui-yu1, LIU Fan-fan1, PAN Wen-wen1, WANG Wei-dong2   

  1. (1. School of Computer Science and Technology, Anhui University, Hefei 230039, China; 2. School of Information Science and Technology, Taishan University, Taian 271021, China)
  • Received:2011-08-12 Online:2012-04-20 Published:2012-04-20

摘要: 利用MapReduce模型可自动编写串行程序及编程接口简单的优点,实现量子进化算法在MapReduce模型下的并行化,提出基于MapReduce模型的并行量子进化算法MRQEA,并将其部署到Hadoop云计算平台上运行。对0-1背包问题的测试结果证明,MRQEA算法在处理大型数据集时具有良好的加速比和并行效率。

关键词: 量子进化算法, apReduce模型, 计算平台, adoop平台

Abstract: This paper aims at the parallelism of Quantum Evolutionary Algorithm(QEA), makes full use of MapReduce’s the highly abstract, the preparation of serial program automatically running in parallel, simple programming interface and easy parallel programming, realizes the parallelization of QEA in MapReduce, puts forward parallel QEA based on MapReduce model and runs the algorithm on Hadoop platform. Using 0-1 knapsack problem for test, experimental results prove the feasibility of MRQEA, and it has good speed-up ratio and parallel efficiency in dealing with large data set.

Key words: Quantum Evolutionary Algorithm(QEA), MapReduce model, cloud computing platform, Hadoop platform

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