一种适用于大规模变量的并行遗传算法研究

计算机科学 ›› 2012, Vol. 39 ›› Issue (7): 182-184.

一种适用于大规模变量的并行遗传算法研究

李东,潘志松

(解放军理工大学指挥自动化学院南京 210007)

出版日期:2018-11-16 发布日期:2018-11-16

Research on Parallel Genetic Algorithms Based on MapReduce

Online:2018-11-16 Published:2018-11-16

摘要/Abstract

摘要： 当前MapReduce并行编程模型得到了广泛的应用。相对于传统的基于PVM或者MPI的并行编程方式，它在执行时间和处理问题规模等方面有明显优势。针对并行遗传算法的特点，提出基于MapRcducc实现一种典型的并行遗传算法—粗粒度并行算法的方法，用以解决大规模变量问题。实验平台采用Hadoop，硬件条件为普通的服务器集群。在多目标优化问题测试中，当问题规模达到一定、处理变量数超过10E}7时，并行算法效率比串行提高数倍，并且能突破内存瓶颈。根据MapReduce自身特点调整其参数，改变并行程度，分析其对并行执行时间的影响。

关键词: 大规模变量，MapRcducc，并行遗传算法，多目标优化问题，性能分析

Abstract: MapReduce programming model enjoys widespread application and becomes new popular parallel programming paradigm nowadays. It uses MapReduce model to parallelize coarse-grained genetic algorithms,and takes mull objective optimization problem as the benchmark. All the experiments are made under hadoop and a cluster which consists of commodity servers. When the number of variable reach to 10E}7，the efficiency of parallel algorithm can be multiplied several times without introducing any bottlenecks revolved memory. Finally we studied how the parallel degree af- feels the performance.

Key words: Keywords Largcscale variables, MapReduce, Parallel genetic algorithms, Mult objective optimization, Performance analysis

李东,潘志松. 一种适用于大规模变量的并行遗传算法研究[J]. 计算机科学, 2012, 39(7): 182-184. https://doi.org/

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