Dynamic Precision Control in Surrogate Assisted Optimization
碩士 === 國立臺灣大學 === 數學研究所 === 99 === In many optimization problems, the number of function evaluations is severely limited by time or cost. These problems pose a special challenge to the field of global optimization, since existing methods often require more function evaluations than can be comfortabl...
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ndltd-TW-099NTU054790032015-10-28T04:07:30Z http://ndltd.ncl.edu.tw/handle/04740429510755550684 Dynamic Precision Control in Surrogate Assisted Optimization 代理輔助最佳化的動態精度控制法 Hua-Wen Luo 羅華文 碩士 國立臺灣大學 數學研究所 99 In many optimization problems, the number of function evaluations is severely limited by time or cost. These problems pose a special challenge to the field of global optimization, since existing methods often require more function evaluations than can be comfortably afforded. One way to address this challenge is to t response surfaces or surrogate surface to data collected by evaluating the objective and constraint functions at a few points. These surfaces can then be used for visualization, trade o analysis, and optimization. We then show how these approximating functions can be used to construct an efficient global optimization algorithm with a credible stopping rule. The key to using response surfaces for global optimization lies in balancing the need to exploit the approximating surface (by sampling where it is minimized) with the need to improve the approximation (by sampling where prediction error may be high). Striking this balance requires solving certain auxiliary problems which have previously been considered intractable, but we show how these computational obstacles can be overcome. 王偉仲 2011 學位論文 ; thesis 50 en_US |
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碩士 === 國立臺灣大學 === 數學研究所 === 99 === In many optimization problems, the number of function evaluations is severely limited by time or cost. These problems pose a special challenge to the field of global optimization, since existing methods often require more function evaluations than can be comfortably afforded. One way to address this challenge is to t response surfaces or surrogate surface to data collected
by evaluating the objective and constraint functions at a few points. These surfaces can then be used for visualization, trade o analysis, and optimization. We then show how these approximating functions can be used to construct an efficient global optimization algorithm with a credible stopping rule. The key to using response surfaces for global optimization lies in balancing the need to exploit the approximating surface (by sampling where it is minimized) with the need to improve the approximation (by sampling where prediction error may be high). Striking this balance requires solving certain auxiliary problems which have previously been considered intractable, but we show how these computational obstacles can be overcome.
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王偉仲 |
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王偉仲 Hua-Wen Luo 羅華文 |
author |
Hua-Wen Luo 羅華文 |
spellingShingle |
Hua-Wen Luo 羅華文 Dynamic Precision Control in Surrogate Assisted Optimization |
author_sort |
Hua-Wen Luo |
title |
Dynamic Precision Control in Surrogate Assisted Optimization |
title_short |
Dynamic Precision Control in Surrogate Assisted Optimization |
title_full |
Dynamic Precision Control in Surrogate Assisted Optimization |
title_fullStr |
Dynamic Precision Control in Surrogate Assisted Optimization |
title_full_unstemmed |
Dynamic Precision Control in Surrogate Assisted Optimization |
title_sort |
dynamic precision control in surrogate assisted optimization |
publishDate |
2011 |
url |
http://ndltd.ncl.edu.tw/handle/04740429510755550684 |
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AT huawenluo dynamicprecisioncontrolinsurrogateassistedoptimization AT luóhuáwén dynamicprecisioncontrolinsurrogateassistedoptimization AT huawenluo dàilǐfǔzhùzuìjiāhuàdedòngtàijīngdùkòngzhìfǎ AT luóhuáwén dàilǐfǔzhùzuìjiāhuàdedòngtàijīngdùkòngzhìfǎ |
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