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研究生：	林裕傑
論文名稱：	參數調整機制於多目標演化式演算法之效能剖析 A numerical study on parameter control mechanisms in MOEA/D-AMS
指導教授：	蔣宗哲
學位類別：	碩士 Master
系所名稱：	資訊工程學系 Department of Computer Science and Information Engineering
論文出版年：	2012
畢業學年度：	100
語文別：	中文
論文頁數：	71
中文關鍵詞：	多目標最佳化問題、演化式演算法、差分演化、參數調整機制
論文種類：	學術論文
相關次數：	點閱：97 下載：5
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查詢本校圖書館目錄查詢臺灣博碩士論文知識加值系統勘誤回報

在現實生活中，我們常常需要解決一些具有多個目標需要考量的問題，並且這些目標通常是互相衝突的，這些問題稱為多目標問題，而多目標最佳化問題的目標便是找出能最佳化這些目標的解集合。演化式演算法 (evolutionary algorithm) 是求解這類問題的常見演算法，其概念為利用族群演化的方式來尋找最佳解集合。MOEA/D 為其中一種知名的演算法，利用將多目標問題拆成單目標來求解的作法可以獲得良好的結果，而 MOEA/D-AMS 與 MOEA/D-APC 便是以該演算法為基礎所改良，其中 MOEA/D-APC 參考了差分演化 (differential evolution) 產生子代的作法，該演算法擁有兩個控制參數 F 與 CR，這兩個參數值是影響子代品質的關鍵，因此 MOEA/D-APC 加入了讓參數隨演化過程調整的機制，經過實驗證明效能有所改善，但仍然在少部分問題上輸給其他的DE演算法。
本論文挑出八個具有不同參數調整機制的DE演算法，利用 MOEA/D-AMS為主體分別結合這八種演算法與 MOEA/D-APC 的參數調整機制，藉由對17個測試問題進行實驗與分析，討論不同調整機制對效能的影響，並將主要目標放在探討 MOEA/D-APC 的弱項及改進方案上。

附圖目錄.........................................................V
附表目錄......................................................VII
第一章    緒論.................................................1
1    多目標最佳化問題.................................1
2    研究範疇.................................................2
第二章    文獻探討.........................................4
1  MOEA/D 與DE.......................................4
2  參數調整機制分類................................8
2.1  數值的分布方式...............................8
2.2  族群參數個數...................................8
2.3  參考資訊的範圍...............................8
3  具參數調整機制之演化式演算法.......10
3.1 連續數值-個別參數-沒有資訊.........10
3.1.1  NSDE...........................................10
3.2 連續數值-個別參數-個體資訊........11
3.2.1  jDE...............................................11
3.2.2  SspDE..........................................12
3.3 連續數值-個別參數-群體資訊.........13
3.3.1  SaDE.............................................13
3.3.2  jDE-2.............................................14
3.3.3  SaNSDE.........................................15
3.3.4  JADE.............................................16
3.3.5  JADE2...........................................17
3.3.6  SaJADE..........................................18
3.4  連續數值-個別參數-親代資訊.........19
3.4.1  Self-adaptive DE（SDE）..............19
3.5  連續數值-單一參數-群體資訊..........20
3.5.1  ADEA.............................................20
第三章 方法與步驟..........................................22
1    MOEA/D-AMS...........................................22
1.1  收斂評估機制.....................................22
1.2  密集度評估機制.................................22
1.3  交配池選擇機制.................................23
1.4  MOEA/D-AMS基本流程.....................23
2    MOEA/D-APC............................................23
2.1  演化過程的參數調整..........................24
2.2  參數值選擇..........................................25
2.3  MOEA/D-APC基本流程.......................26
3    各演算法之參數調整機制比較................26
第四章 實驗設計...............................................30
1    測試問題....................................................30
2    效能指標....................................................35
3    參數設定....................................................36
3.1  基礎參數設定(MOEAD-AMS).............36
3.2  各演算法之調整機制所需參數設定...36
4    效能評比.....................................................38
4.1  UF5問題效能探討................................44
4.2  F2問題效能探討...................................51
4.3  F7問題效能探討...................................54
4.4  F8問題效能探討...................................56
4.5  UF7問題效能探討................................58
第五章 結論與未來發展....................................67
參考文獻.............................................................68

                                

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