臺灣博碩士論文加值系統

　　近年來，使用啟發式方法解決資訊科學領域中各種複雜問題已有非常多顯著的成果。這些方法包含模擬退火演算法 (SA)、 禁忌搜尋演算法 (TS)、螞蟻系統 (AS)、類神經網路 (NN)以及遺傳基因演算法 (GA)等等。在啟發式方法解決資訊科學領域問題中，GA是這些有效方法中較為著名的一種。其原因是因為GA的架構在複雜問題中搜尋最佳解的能力。但於它類數學問題時GA卻可能須重新設計其結構。由於其演化特性，將傳統GA應用於實際問題及工程問題時，其彈性仍略顯不足。上述問題同時也成為一個難以解決、相矛盾及多工之問題。因此，修改GA的結構、適應性函數、交配及突變的設定以及微調的問題，以改善GA之效能，是這類研究中較常見的方式。在本論文中，我們針對這些方式的效益評估比較，並提出一有效的方法，稱為多重搜尋基因演算法 (MSGA)。此一方法在經由實驗證明，其成效超越傳統GA、疫苗式(immune) GA及其它GA之方法。這些被驗証之問題包含旅行銷售員問題、群播繞送問題、固定頻道分配問題及資料分群問題等。經由實際電腦測試及驗証結果顯示，本論文所提之方法在這些問題中確實優於其它現存的GA方法。

　　Recently, using the heuristic method to solve problems of computer science has many remarkable results. These are simulated annealing (SA), Tabu Search (TS), ant system (AS), neural network (NN), genetic algorithm (GA) and so forth. GA is known as one of the most efficient algorithm for various problems of computer science. The GA mechanism possesses the unique ability to search and optimize a solution for a complex system, where other mathematical oriented techniques may have failed to compile the necessary design specification. Due to its evolutionary characteristics, a standard GA may not be flexible enough for a practical application, and an engineering insight is always required whenever a GA is applied. This becomes more apparent where the problem to be tackled is complicated, conflicting and multi-tasking. Therefore, a means of modifying the GA structure, fitness function, operator settings of crossover and mutation, and fine-tuned problem, are sought in order to meet the design requirements. In this thesis we modify the GA structure to improve the performance significantly. We also present a deceptively simple in this thesis, yet empirically powerful metaheuristic, called multiple-searching genetic algorithm (MSGA), that has been found to more effective than traditional genetic algorithm, immune genetic algorithm, and other existing GA approaches for the TSP on benchmark test problems, multicast routing, fixed channel assignment and data clustering from the literature. According to our simulation results, our proposed algorithm is robust and outperforms the other existing GA’s in the previous mentioned problems such as TSP, multicast routing and data clustering problems.

目 錄
中文摘要……………………………………………………Ⅰ
英文摘要……………………………………………………Ⅲ
誌謝……………………………………………………Ⅴ
目錄 ……………………………………………………………………Ⅵ
圖目錄 ……………………………………………………………… Ⅸ
表目錄 ……………………………………………………………ⅩⅢ
第一章 緒論……………………………………………………………01
第二章 多重搜尋演算法 ………………………………………..05
第一節 傳統基因演算法……………………………………………05
一、Representation.……………………………………………......08
二、Evaluation (Fitness) function…………………………..…....08
三、Operator setting of crossover and mutation……………..08
四、Fine-tuning…………………………………………………….09
第二節 多重搜尋基因演算法 .………………………………….09
一、理論及概念 …………………………………………………...09
二、MSGA演算法…………………………………………………..12
三、MSGA的程序…………………………………………………..13
四、建立探險者染色體的方法 ………….………………….....15
第三節 MSGA與傳統GA的比較………………………………..18
第四節 結論 ………………………………………………………….19
第三章 應用MSGA於旅行銷售員問題 ………………….20
第一節 問題定義…………………………………………………….20
第二節 相關研究…………………………………………………….21
第三節 實作程序…………………………………………………….22
一、利用最近鄰居及候選人機制建立探險者染色體………..23
二、使用較佳的方式避免落入區域解 …………………………24
第四節 實驗結果……………………………………………………25
第五節 結論…………………………………………………………….30
第四章 利用MSGA求解群播繞送問題……………………32
第一節 問題定義……………………………………………………...32
第二節 相關研究………………………………………………….34
第三節 實作程序…………………………………………………….35
第四節 實驗結果…………………………………………………….37
第五節 結論……………………………………………………………42
第五章 以MSGA解固定頻道分配之問題…………….43
第一節 問題定義…………………………………………………….43
第二節 相關研究…………………………………………………….47
第三節 實作程序………………………………………….…………48
第四節 實驗結果……………………………………………….…....63
第五節 結論………………………………………………………….70
第六章 使用MSGA求解資料分群問題…………………72
第一節 問題定義………………………………….…………………72
第二節 相關研究……………………………………………………75
第三節 實作程序…………………………………………………….78
第四節 實驗結果……………………………………………………80
第五節 結論……………………………………………………………84
第七章 結論及未來研究方向………………………………….85
參考文獻…….……………………………………………………….…89
作者簡介 ….…………………………………..…………………….…103
圖目錄
圖2-1：Selection…………………………………………………………..06
圖2-2：Crossover…………………………..……………………………..06
圖2-3：Mutation…………………………………………………….……06
圖2-4：Process of the Genetic Algorithm……………………………07
圖2-5：在求解的最後階段，所有的解會成為相似的一群………10
圖2-6：解空間及最佳解的關係………………………………………11
圖2-7A：TGA收斂程序一……………………………………………...11
圖2-7B：TGA收斂程序二……………………………………………...11
圖2-8A：MSGA收斂程序一………………………………………….12
圖2-8B：MSGA收斂程序二………………………………………….12
圖2-9：Process of the Genetic Algorithm………………………..…13
圖2-10：MSGA計算程序步驟1及步驟2…………………………..14
圖2-11：MSGA計算程序步驟3…………………….………………..15
圖2-12：候選人機制……………………………………………..……….16
圖2-13：路徑的建立……………………………………………………..17
圖2-14：MSGA建立探險者的程序………………………………….17
圖2-15：傳統GA染色體進行演化的程序…………………………18
圖2-16：MSGA染色體進行演化的程序…………………………….18
圖3-1：染色體狀態………………………………………………………24
圖3-2：發生衝突…………………………………………………………25
圖3-3：MSGA1.0發生衝突的解決方法…………………………….25
圖3-4：MSGA1.1發生衝突的解決方法…………………………….25
圖3-5：Benchmark-ei151………….……………………………......27
圖3-6：Benchmark-ei176………..…………………………………….28
圖3-7：Benchmark-kroa100…..……………………………………….28
圖3-8：Benchmark-d198……….………………………………………29
圖3-9：Benchmark-ts225……………………………………………….29
圖3-10：Benchmark-pcb442……………………………………….…30
圖4-1：Ren-Hung Hwang 的表示法……..………………………….36
圖4-2：Qingfu Zhang的表示法……………………………………..36
圖4-3：Yee Leung的表示法…………………………………………..36
圖4-4：16 nodes (|D|=8)………………….……………………………38
圖4-5：20 nodes (|D|=10)…………………………………………….38
圖4-6：32 nodes (|D|=16) ……………………….…………………….38
圖4-7：50 nodes (|D|=25) ……………………………………………….38
圖4-8：64 nodes (|D|=32) ….………………………………………….38
圖4-9：20點問題之結果………………………………………………...40
圖4-10：32點問題之結果…….……………………………………...40
圖4-11：50點問題之結果…………………………………………….41
圖4-12：64點問題之結果…………………………………………….41
圖4-13：50點問題之結果…………………………………………….41
圖4-14：64點問題之結果……………………………………………41
圖5-1：matrix C…………………………………………………………44
圖5-2：demand d……………………………………………………….44
圖5-3：the optimum for this FCA problem……………………….45
圖5-4：Channel配置前之狀態…………………………………….50
圖5-5：Channel配置前……………………………………………....50
圖5-6：Initialize 程序1-1…………………………………………….51
圖5-7：Initialize 程序1-2…………………………………………….51
圖5-8：Initialize 程序2-1…………………………………………….52
圖5-9：Initialize 程序2-2…………………………………………….52
圖5-10：Initialize 程序3-1 ……………………………………………53
圖5-11：Initialize 程序3-2 ……………………………………………53
圖5-12：Initialize 程序4-1……………………………………………54
圖5-13：Initialize 程序4-2……………………………………………54
圖5-14：Initialize 程序5………………………………………………54
圖5-15：Uniform crossover…………………………………………….56
圖5-16：One point crossover………………………………………….56
圖5-17：Two point crossover………………………………………….57
圖5-18：New local search example………………………………….60
圖5-19：New local search algorithm………………………………61
圖5-20：MSGA algorithm……………………….……………………62
圖5-21：MSGA process………………….……………………………..63
圖5-22：Specifications of Simulated Problems……………………..63
圖5-23：Compatibility matrices and demand vectors in simulated
problems……………………………………………………….65
圖5-24：Compare MSGA (Local Search) with GFA
in Problem 8………………….……………………………69圖6-1：Hierarchical的分群方法………………………………….…..76
圖6-2：MS-GKA algorithm…………………………………………...79
圖6-3：579 node-from Mu-Chun Su…….………………………...80
圖6-4：演化過程(579點)………………………………………………81
圖6-5：演化過程(1000點)……………………………………………..82
圖6-6：演化過程(2000點)……………………………………………..83
表目錄
表3-1：基本參數設定…………………………………………………26
表3-2：演化代數設定…………………………………………………26
表 3-3：TGA、IGA、MSGA1.0、MSGA1.1及
MSGA1.1+2-opt的比較…........................................……….27
表4-1：參數設定1………………………………………………………39
表4-2：參數設定2………………………………………………………39
表4-3：HGA、MHGA、LGA、MLGA、OGA及MOGA的比較…39
表4-4：HGA、MHGA(MSGA)的比較………………………………40
表4-5：LGA、MLGA(MSGA)的比較…………………………………41
表4-6：OGA、MOGA(MSGA)的比較………………………………42
表5-1：參數設定…………………………………………………………66
表5-2：TGA with the proposed initialization method…………….66
表5-3：MSGA with the proposed initialization method……………67
表5-4：Compare MSGA with TGA、KGA and NN……………….68
表5-5：參數設定…………………………………………………………68
表5-6：比較Neural Network[6]、TGA[17]、GFA[2]、MSGA及
MSGA+Local Search…………………………………………69
表6-1：實驗結果-within cluster (579點)…………………………….81
表6-2：實驗結果-between cluster (579點) ………………..………81
表6-3：實驗結果-within cluster (1000點)…………………………82
表6-4：實驗結果-between cluster (1000點) ……………………….82
表6-5：實驗結果-within cluster (2000點) …………….………….83
表6-6：實驗結果-between cluster (2000點) ……………………83

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