计算机工程与应用 ›› 2021, Vol. 57 ›› Issue (12): 273-278.DOI: 10.3778/j.issn.1002-8331.2003-0038
• 工程与应用 • 上一篇
陈元文
出版日期:
发布日期:
CHEN Yuanwen
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摘要：
针对用于求解复杂物资调运及配载问题的多目标遗传算法耗时较长的问题，设计了基于云计算MapReduce技术的并行化部署和改进方案。实验对比了算法在多种串行、并行环境下的时效性，证实了MapReduce架构在一定环境下能较大幅度提高算法的时耗性能。
关键词: 物资调运及配载, 多目标遗传算法, MapReduce, 并行化
Abstract:
Aiming at the problem that multi-objective genetic algorithms for solving complex material scheduling and stowage problems take a long time, this paper designs a parallel deployment and improvement scheme based on cloud computing MapReduce technology. The experiments compare the timeliness of the algorithm in a variety of serial and parallel environments, confirming that the MapReduce architecture can greatly improve the time-consuming performance of the algorithm under certain circumstances.
Key words: material transportation and stowage, multi-objective genetic algorithms, MapReduce, parallel
陈元文. MapReduce技术在物资调运与配载问题中的应用[J]. 计算机工程与应用, 2021, 57(12): 273-278.
CHEN Yuanwen. Application of MapReduce Technology in Problem of Material Transportation and Stowage[J]. Computer Engineering and Applications, 2021, 57(12): 273-278.
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链接本文: http://cea.ceaj.org/CN/10.3778/j.issn.1002-8331.2003-0038
http://cea.ceaj.org/CN/Y2021/V57/I12/273
