Bayesian Guided Pattern Search for Robust Local Optimization
Bayesian Guided Pattern Search for Robust Local Optimization
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DOI：
10.1198/tech.2009.08007
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发表时间：
2009-11
期刊：
影响因子：
2.5
通讯作者：
Matt Taddy;Herbert K. H. Lee;G. A. Gray;J. Griffin
中科院分区：
文献类型：
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作者：
Matt Taddy;Herbert K. H. Lee;G. A. Gray;J. Griffin
工程中复杂系统的优化通常涉及使用昂贵的计算机模拟。通过将使用树形高斯过程的统计仿真与模式搜索优化相结合，我们能够比单独使用任何一种方法更有效地执行鲁棒的局部优化。我们的方法是基于增强本地搜索模式与位置集通过改进预测输入空间。我们进一步开发了一个计算框架的异步并行实现的优化算法。我们证明了我们的方法对两个标准的测试问题和我们的激励校准电路器件模拟器的例子。
Optimization for complex systems in engineering often involves the use of expensive computer simulation. By combining statistical emulation using treed Gaussian processes with pattern search optimization, we are able to perform robust local optimization more efficiently and effectively than when using either method alone. Our approach is based on the augmentation of local search patterns with location sets generated through improvement prediction over the input space. We further develop a computational framework for asynchronous parallel implementation of the optimization algorithm. We demonstrate our methods on two standard test problems and our motivating example of calibrating a circuit device simulator.