請用此 Handle URI 來引用此文件: http://hdl.handle.net/11455/1546
標題: 使用資料挖掘類神經網路和演化策略之全域最佳化方法
The global optimization method using data mining, artificial neural network and evolution strategy
作者: 鄭義良
Cheng, Yi-Liang
關鍵字: data mining
資料挖掘
artificial neural network
evolution strategies
類神經網路
演化策略法
出版社: 機械工程學系所
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摘要: 演化式演算法具有對各類型的問題都能表現出一定求解能力,且較易找到全域最佳解的優點,但其計算次數過於繁多之缺點,卻導致演化式演算法對工程應用的大型結構最佳化而言其實用性不佳。為此,本文提出以資料挖掘技術及類神經網路技術來結合演化式演算法,以解決演化式演算法在工程上應用的窘境。本文之方法,以資料挖掘技術學習分類規則以區分可行解與不可行解,達到在縮小的設計範圍進行搜尋以提高找到全域最佳解的機會;以類神經網路技術建構網路模型以對目標函數或限制函數進行近似分析,取代原本大量繁瑣的工程計算;另外保留外部菁英以留下最好的可行解,最後再以傳統數學規劃法搜尋以找到更精確的全域最佳解。本文方法的測試結果顯示,對大多數測試例題都可找到全域最佳解,對部份題目本文的方法所使用的電腦時間也遠低於某些文獻的方法。
The evolutionary algorithm has a general capability to solve all kinds of problems and is easier to find the global solution. But due to a huge number of function evaluations it is hard to be applied to complex engineering optimization problems. Therefore this thesis proposes an approach of integrating data mining, artificial neural network and evolution strategy to solve structural optimization problems. The data mining will be used to separate the feasible region from the infeasible one in order to reduce the searching space and hence increase the probability of finding global solution. The neural network is created to replace the exact finite element analysis to save computational time. An external elite set keeps some best designs when ES search is completed. A mathematical programming method is used to find exact global solution from there elite designs. For most test problems the proposed approach finds global solutions and for some problems the cpu time consumed by this proposed method is much less than that by other methods.
URI: http://hdl.handle.net/11455/1546
其他識別: U0005-1001200704080800
文章連結: http://www.airitilibrary.com/Publication/alDetailedMesh1?DocID=U0005-1001200704080800
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