• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文学位 >Simulation optimization methods that combine multiple comparisons and genetic algorithms with applications in design for computer and supersaturated experiments.
【24h】

Simulation optimization methods that combine multiple comparisons and genetic algorithms with applications in design for computer and supersaturated experiments.

机译：结合了多个比较和遗传算法的仿真优化方法，以及在计算机和过饱和实验设计中的应用。

获取原文
获取原文并翻译 | 示例
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The central issue in this dissertation is the developing relationship between the studies of simulation optimization and of experimental design. We begin by using simulation to evaluate and compare experimental design and analysis techniques for computer experiment meta-modeling. We used two test functions to compare all combinations of four experimental design classes with either second-order response surface (RS) or kriging modeling methods. The findings included for the first test function, any conclusion could be reached about which combination achieved the best prediction accuracy based on the experimental design used to represent each class. Also, we tentatively concluded that, for cases in which the number of runs is comparable to the number of terms in a quadratic polynomial, similar prediction errors can be expected from both kriging and regression modeling procedures as long as regression is used in combination with designs generated to address bias errors.; Next, we propose heuristics for simulation optimization based on combining genetic algorithms with elitist reproduction and comparisons based on confidence intervals. We use eight test functions to compare the proposed method with alternatives from the literature and conclude that, among general-purpose stochastic solvers, the proposed class of algorithms is promising. We also define stochastic equivalence to relate cases in which only a finite number of Monte Carlo simulations are feasible with imagined cases in which an infinite number of simulation are possible. We apply results from statistical selection and ranking and multiple comparison with the best to estimate the sample size requirements for stochastic equivalence to be achieved.; The main application of the proposed heuristic is the development of so-called “supersaturated” experimental designs. These designs are used for identifying the important inputs or factors in situations in which the number of factors is larger than the number of runs. Using simulation optimization we are able to evaluate and derive experimental designs from more realistic assumptions and more relevant objectives than was possible previously. We use hierarchical prior assumptions from the design literature that include the possibility of interactions between factors. Also, we directly optimize the probability of success in selecting important factors, which is new.
机译：本文的核心问题是仿真优化研究与实验设计之间的发展关系。我们首先使用仿真来评估和比较用于计算机实验元建模的实验设计和分析技术。我们使用了两个测试功能，将两种实验设计类的所有组合与二阶响应面（RS）或克里金模型方法进行了比较。包括第一个测试功能的发现，可以基于代表每个类别的实验设计得出关于哪种组合达到最佳预测精度的任何结论。此外，我们初步得出结论，对于游程数与二次多项式中的项数相当的情况，只要将回归与设计结合使用，就可以从克里金法和回归建模过程中获得相似的预测误差。生成以解决偏差错误。接下来，我们提出基于遗传算法与精英复制相结合以及基于置信区间进行比较的启发式仿真优化方法。我们使用八个测试函数将所提出的方法与文献中的替代方法进行比较，并得出结论，在通用随机求解器中，所提出的算法类别很有希望。我们还定义了随机等价关系，以关联只有有限数量的蒙特卡洛模拟可行的情况与带有无限数量的模拟可能的想象情况。我们将统计选择和排名结果与最佳结果进行多次比较，以估计达到随机等效性所需的样本量。提议的启发式方法的主要应用是开发所谓的“过饱和”实验设计。这些设计用于在因素数量大于运行数量的情况下识别重要的输入或因素。使用仿真优化，我们能够比以前更现实的假设和更相关的目标评估和得出实验设计。我们使用设计文献中的分层先验假设，其中包括因素之间相互作用的可能性。此外，我们直接优化了选择重要因素的成功概率，这是新的。

著录项

  • 作者

    Bernshteyn, Mikhail Alexander.;

  • 作者单位

    The Ohio State University.;

  • 授予单位 The Ohio State University.;
  • 学科 Engineering Industrial.; Operations Research.
  • 学位 Ph.D.
  • 年度 2001
  • 页码 157 p.
  • 总页数 157
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 一般工业技术;运筹学;
  • 关键词

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