請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/33386完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 鍾添東 | |
| dc.contributor.author | Chen-Cheng Lee | en |
| dc.contributor.author | 李臻誠 | zh_TW |
| dc.date.accessioned | 2021-06-13T04:37:46Z | - |
| dc.date.available | 2006-07-28 | |
| dc.date.copyright | 2006-07-28 | |
| dc.date.issued | 2006 | |
| dc.date.submitted | 2006-07-18 | |
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Dowling, Mechanical behavior of materials-engineering methods for deformation, fracture, and fatigue, Prentice-Hall, Inc, 1993. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/33386 | - |
| dc.description.abstract | 本文提出結構最佳化遺傳演算之再生核近似法。首先給定結構的幾何參數，並發展參數化設計程式自動繪製結構的實體模型；然後發展有限元素分析程式自動進行結構分析，並將分析結果當作族群個體的適應度值，以求得族群個體的再生核形狀函數，接著即可建立適應度函數的再生核近似模型。本文利用遺傳演算法來求解最佳化問題，在遺傳演算法的過程中，本文利用改良式可信範圍法，只在某幾個特定世代計算族群個體的適應度正確值，在接下來的數個世代(稱為族群延遲)，則使用再生核近似模型計算族群個體的適應度近似值。另外本文利用可適性競爭選擇法，動態調整每一代的競爭規模來減少當代的近似誤差。當搜尋至某一世代中，有90%族群個體具相同適應度值的時候，稱收斂至此最佳化問題的最佳解。最後發展一套整合型程式，結合電腦輔助設計軟體，有限元素分析軟體，再生核近似法及遺傳演算法進行結構最佳化設計。利用此程式，本文對一些結構設計問題進行最佳化設計。由最佳化結果可知，此程式是可信賴的，且對大部分的範例皆能快速地得到滿意的收斂結果。 | zh_TW |
| dc.description.abstract | This thesis proposes the reproducing kernel approximation method for structural optimization using genetic algorithms. Firstly, geometric parameters of a structure are defined, and a parametric design program is developed to automatically generate the solid model of the structure. Then, a macro program to automatically analyze structural behaviors of the structure is developed. Analysis results are used as fitnesses of population individuals to generate reproducing kernel shape functions. Then, reproducing kernel approximations of fitnesses are developed. Genetic algorithms are used to solve the optimization problem. In genetic algorithms processes, a modified trust region approach is developed. Fitnesses of population individuals are evaluated exactly only for some specific generations. Fitnesses of population individuals for the following some generations, called the generation delay, are evaluated approximately by reproducing kernel approximations. In addition, an adaptive tournament selection scheme is developed by adjusting the tournament size to reduce approximation errors in each generation. When 90% of population individuals in a certain generation have the same fitness value, the solution of the optimization problem is found. Finally, an integrated program combining computer aided design software, finite element analysis software, reproducing kernel approximation method and genetic algorithms is developed for structural optimization. With the developed program, optimum design processes of several structural design problems are investigated. From optimum results, they show that this proposed program is reliable and results in fast and satisfactory convergent solutions. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-13T04:37:46Z (GMT). No. of bitstreams: 1 ntu-95-D88522004-1.pdf: 9743704 bytes, checksum: 811597dafbc733a8e0a3bace99fb35ae (MD5) Previous issue date: 2006 | en |
| dc.description.tableofcontents | 中文摘要 i
英文摘要 ii 目錄 iii 圖目錄 v 表目錄 viii 符號說明 ix 第一章 緒論 1 1-1 簡介 1 1-2 文獻回顧 2 1-3 研究動機與目的 13 1-4 研究策略與方法 14 1-5 論文大綱介紹 15 第二章 最佳化方法於機械結構之設計與分析 17 2-1 機械結構最佳化設計理論 17 2-1-1 設計變數處理 17 2-1-2 目標函數處理 18 2-1-3 限制條件處理 18 2-2 最佳化方法處理限制條件的方法 20 2-2-1 靜態懲罰函數 21 2-2-2 動態懲罰函數 22 2-2-3 可適性懲罰函數 23 2-3 遺傳演算法概論 25 2-3-1 基本原理 26 2-3-2 運算子 27 2-3-3 型基理論 35 2-3-4 遺傳演算法與傳統最佳化方法的差異 38 2-4 再生核近似法理論 39 2-4-1 離散再生核近似法 40 2-4-2 離散再生核形狀函數的轉換矩陣 43 2-4-3 離散再生核形狀函數的一致性 44 第三章 最佳化遺傳演算之再生核近似法 47 3-1 利用再生核近似法計算遺傳演算法適應度值 47 3-2 檢驗再生核近似法的奇異點與一致性 55 3-3 利用改良式可信範圍法決定近似模型的更新時機 57 3-4 利用可適性競爭選擇法降低近似誤差 59 3-5 最佳化遺傳演算之再生核近似法流程 61 第四章 整合型程式於結構最佳化設計 63 4-1 實體模型建構模組 64 4-2 有限元素分析模擬模組 66 4-3 近似法模組 67 4-4 最佳化模組 68 4-5 執行程式控制模組 68 第五章 函數最佳化及簡單結構最佳化 71 5-1 函數一最佳化 71 5-2 函數二最佳化 74 5-3 函數三最佳化 75 5-4 函數四最佳化 77 5-5 薄壁懸臂軸最佳化設計 78 5-6 三桿結構最佳化設計 81 5-7 十桿結構最佳化設計 82 5-8 二十五桿結構最佳化設計 84 5-9 結果討論 87 第六章 應用實例之設計 89 6-1 1x2光開關(型一)結構最佳設計 90 6-2 1x4光開關結構最佳設計 97 6-3 微鑽頭外形最佳設計 103 6-4 汽車福祉椅升降機構之最佳化設計 108 6-5 汽車福祉椅旋轉機構之最佳化設計 112 6-6 1x2光開關(型二)之設計與分析 117 6-7 1x2光開關(型三)之設計與分析 123 6-8 結果討論 127 第七章 結論與建議 129 7-1 結論 129 7-2 建議 131 參考文獻 133 附錄A GALib常用函數的使用方法 141 附錄B 整合型最佳化程式的使用方法 143 附錄C 再生核近似函數與實際函數比較 148 作者簡歷 153 | |
| dc.language.iso | zh-TW | |
| dc.subject | 遺傳演算法 | zh_TW |
| dc.subject | 結構最佳化 | zh_TW |
| dc.subject | 參數化設計 | zh_TW |
| dc.subject | 有限元素法 | zh_TW |
| dc.subject | 再生核近似法 | zh_TW |
| dc.subject | Genetic algorithms | en |
| dc.subject | Finite element method | en |
| dc.subject | Reproducing kernel approximation method | en |
| dc.subject | Parametric design | en |
| dc.subject | Structural optimization | en |
| dc.title | 結構最佳化遺傳演算之再生核近似法 | zh_TW |
| dc.title | Reproducing Kernel Approximation Method for Structural Optimization Using Genetic Algorithms | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 94-2 | |
| dc.description.degree | 博士 | |
| dc.contributor.oralexamcommittee | 林陽泰,范光照,楊耀州,廖運炫,劉正良 | |
| dc.subject.keyword | 參數化設計,有限元素法,再生核近似法,遺傳演算法,結構最佳化, | zh_TW |
| dc.subject.keyword | Parametric design,Finite element method,Reproducing kernel approximation method,Genetic algorithms,Structural optimization, | en |
| dc.relation.page | 152 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2006-07-19 | |
| dc.contributor.author-college | 工學院 | zh_TW |
| dc.contributor.author-dept | 機械工程學研究所 | zh_TW |
| 顯示於系所單位： | 機械工程學系 | |
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