• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Data Envelopment Analysis and Decision Science >The Measuring Efficiency of Large Scale Datasets in DEA with Metaheuristic Algorithm Approach
【24h】

The Measuring Efficiency of Large Scale Datasets in DEA with Metaheuristic Algorithm Approach

机译：元启发式算法在DEA中大规模数据集的测量效率

获取原文
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Data Envelopment Analysis (DEA) is a non-parametric technique for measuring the efficiency of Decision Making Units (DMUs) with multiple inputs and outputs. DEA for a large dataset with many input/output variables and/or many DMUs would need huge computer resources in terms of memory and CPU time. This paper proposed an Electromagnetism Algorithm (EA) for estimating the efficiency of DMUs in large datasets for the first time. Since the parameters have important roles on the convergence and quality of the algorithms, they are calibrated by means of the experimental design in order to improve their performances. To evaluate the effectiveness of EM, a numerical experiment was conducted using several data sets and compared with simulated annealing (SA) Algorithm as a well-known metaheuristic. Experimental results indicated that EM outperformed SA.
机译：数据包络分析（DEA）是一种非参数技术，用于测量具有多个输入和输出的决策单元（DMU）的效率。具有许多输入/输出变量和/或许多DMU的大型数据集的DEA就内存和CPU时间而言将需要大量的计算机资源。本文首次提出了一种电磁算法（EA），用于估计大型数据集中DMU的效率。由于参数在算法的收敛性和质量上具有重要作用，因此通过实验设计对其进行校准以提高其性能。为了评估EM的有效性，使用几个数据集进行了数值实验，并将其与模拟退火（SA）算法进行比较，这是众所周知的元启发式算法。实验结果表明，EM优于SA。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号