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研究生:黃肆海
研究生(外文):Si-Hai Huang
論文名稱:混合型模擬退火法於結構工程之應用
論文名稱(外文):Applications of Hybrid Simulated Annealing to Structural Engineering
指導教授:江達雲
指導教授(外文):Dar-Yun Chiang
學位類別:碩士
校院名稱:國立成功大學
系所名稱:航空太空工程學系碩博士班
學門:工程學門
學類:機械工程學類
論文種類:學術論文
論文出版年:2003
畢業學年度:91
語文別:中文
論文頁數:96
中文關鍵詞:模擬退火全域最佳化
外文關鍵詞:simulated annealingglobal optimization
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本文提出一個由改良引導演化模擬火法而發展出的混合型模擬退火法,不但可增加訊息交換的機會,而且能有效避免家族過早同化,又不失去原本優生演化的競爭策略之效益。引入新的產生子代機制與保留家族數量等觀念,使得傳統模擬退火法中溫度參數不易決定及終止準則無法有效判定等問題,都變得容易決定而有效。藉由測試函數的驗證,本法可應用在一般函數最佳化問題上,以求得有效的全域最佳解。
本文並將所發展的混合型模擬退火法應用於結構最佳化設計、拓樸最佳化設計及類神經網路之訓練等工程問題上。關於結構最佳化設計與拓樸最佳化設計的問題,結合混合型模擬退火法與外部懲罰函數法可有效地求解全域最佳值,藉由桁架與懸臂樑最佳化設計等一些實例,顯現出本法的優越性。對於類神經網路訓練的問題,混合型模擬退火法與倒傳遞學習法則的結合,不但改善網路訓練易受初始值影響之缺點,也增加網路學習的效率。
In this thesis, a robust global optimization algorithm was presented which improves the Guided Evolutionary Simulated Annealing. The algorithm is called Hybrid Simulated Annealing(HSA). The approach increases the chance of exchanging information without losing the benefit of the evolutionary strategy for avoiding early convergence. By introducing the ideas of new neighborhood search and family retaining, the selection of initial temperature and criterion in the process of simulated annealing becomes easy and effective. Numerical studies using a set of test functions show that the approach is effective and robust in solving function optimization problems.
Furthermore, we consider optimum structural design, optimum topological design and training of neural network for applications of the proposed HSA. In combination with the exterior penalty function method, HSA may solve the optimum structural design and optimum topological design problem successfully. Some design examples such as truss and stepped cantilever beam are studied to show that the approach is effective and robust. To improve the robustness of learning capability of a neural network, a method is proposed merging HAS into backward error propagation.
中文摘要 …………………………………………………………Ⅰ
英文摘要 …………………………………………………………Ⅱ
致謝 ………………………………………………………………Ⅲ
目錄 ………………………………………………………………Ⅳ
表目錄 ……………………………………………………………Ⅵ
圖目錄 ……………………………………………………………Ⅷ
第一章 緒 論 ……………………………………………………1
1.1前言 ………………………………………………………1
1.2文獻回顧 …………………………………………………2
1.3研究目的與方法 …………………………………………4
1.4論文內容 …………………………………………………5
第二章 模擬退火法與模擬演化法之介紹 ………………………6
2.1前言 ………………………………………………………6
2.2模擬退火法 ………………………………………………7
2.3模擬演化法 ………………………………………………12
2.4數值模擬 …………………………………………………17
2.4本章結論 ………………………………………………………19
第三章 混合型模擬退火法 ………………………………………20
3.1前言 ………………………………………………………20
3.2引導演化模擬退火法 …………………………………………21
3.3混合型模擬退火法 ………………………………………26
3.3.1演化策略 ……………………………………………………26
3.3.2家族子代產生機制 …………………………………………29
3.3.3收斂準則 ……………………………………………………30
3.4控制參數之探討 ………………………………………………34
3.4.1初始溫度 ……………………………………………………35
3.4.2降溫比例 ……………………………………………………35
3.4.3家族數量與初始子代數量 …………………………………36
3.5混合型模擬退火法於最佳化函數之驗證 ……………………37
3.6混合型模擬退火法於多重極值函數之驗證 …………………39
3.7本章結論 ………………………………………………………40
第四章 混合型模擬退火法於結構工程之應用 …………………41
4.1前言 ………………………………………………………41
4.2結構最佳化設計 ………………………………………………42
4.2.1十桿件桁架結構最佳化設計 ………………………………45
4.2.2二十五桿件桁架結構最佳化設計 …………………………46
4.4.3五階段懸臂樑結構最佳化設計 ……………………………47
4.3拓樸最佳化設計 ………………………………………………48
4.4倒傳遞神經網路訓練 …………………………………………50
第五章 結論 ………………………………………………………54
參考文獻 ……………………………………………………………56
附表 ………………………………………………………………60
附圖 ………………………………………………………………76
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