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作者(中文):林政文
作者(外文):Cheng-Wen Lin
論文名稱(中文):遺傳演算法求解 Linear Consecutive-k-out-of-n 和 Linear Consecutive-(r, s)-out-of-(m, n): F 系統最佳化問題
論文名稱(外文):Solving Optimization Problem with Linear Consecutive-k-out-of-n and Linear Consecutive-(r, s)-out-of-(m, n): F system by Using Genetic Algorithms
指導教授(中文):陳茂生
指導教授(外文):Maw-Sheng Chen
學位類別:碩士
校院名稱:國立清華大學
系所名稱:工業工程與工程管理學系
學號:933821
出版年(民國):95
畢業學年度:94
語文別:英文
論文頁數:92
中文關鍵詞:遺傳演算法
外文關鍵詞:Linear consecutive-k-out-of-n: F systemLinear consecutive-(r,s)-out-of-(m,n): F systemGenetic AlgorithmsBirnbum importanceReliability importanceLK heuristic
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對於 linear consecutive-k-out-of-n 和 linear consecutive-(r, s)-out-of-(m, n): F 系統最佳化的問題,目前已經有一些趨近的方法,這些方法都是以 reliability importance 為基礎;我們將使用遺傳演算法來求解 linear consecutive-k-out-of-n 和 linear consecutive-(r, s)-out-of-(m, n): F 系統最佳化的問題。
傳統的遺傳演算法在長時間之後,母體會有收斂到區域最佳解的情況,使得交配和突變的機制弱化。因此,我們在母體出現有可能收斂到區域最佳解時,對母體進行擾動,增加交配和突變的搜尋能力。
The linear consecutive-k-out-of-n and the linear consecutive-(r, s)-out-of-(m, n): F system both are enumerated minimum cuts easily. The optimization problem with linear consecutive-k-out-of-n: F system is discussed by many people. They proposed some heuristic methods for solving problem that are based on reliability importance. We propose the genetic algorithm for solving the optimization problem with linear consecutive-k-out-of-n: F system by introducing with reliability importance.
The population in a genetic algorithm may converge on a local optimal solution and the mechanisms of crossover and mutation are not very effective to search other solution when the algorithm runs long time. We propose a perturbation mechanism to let the population get diverge and solution search scope gets larger to escape the population converges on a local optimal solution.
Abstract i
List of Figures iv
List of Tables vi
List of Notations viii

Chapter 1 Introduction 1
1.1 Background and Motivation 1
1.2 Research Framework 6

Chapter 2 Literature Review 8
2.1 Consecutive-k-out-of-n: F System 8
2.1.1 Reliability Evaluation of Linear Consecutive-k-out-of-n: F System 8
2.1.2 The Optimal Assignment with Consecutive-k-out-of-n: F System 9
2.2 Reliability Importance 9
2.2.1 Birnbaum Importance and Invariant System 12
2.2.2 LK Heuristic 14
2.2.3 Pairwise Rearrangements 18
2.3 Genetic Algorithms 20

Chapter 3 Methodology 22
3.1 Problem Definition of One-dimensional System 22
3.2 Genetic Algorithms and Numerical Example (Programming Validation) 25
3.2.1 Chromosome Representation 26
3.2.2 Initial Population 26
3.2.3 Evaluation of Chromosome 29
3.2.4 Selection for crossover 31
3.2.5 Crossover 32
3.2.6 Selection for mutation 35
3.2.7 Mutation 36
3.2.8 Population Selection 38
3.3 Problem Definition of Two-dimensional System 42
3.3.1 Birnbaum Importance 45
3.3.2 Reliability Importance 47
3.4 Genetic Algorithms and Numerical Example (Programming Validation) 48
3.4.1 Chromosome Representation 49
3.4.2 Initial Population 50
3.4.3 Evaluation of Chromosome by Decomposition Approach 52
3.4.4 Evaluation of Chromosome by Crude Monte Carlo Simulation 54
3.4.5 Selection for crossover 56
3.4.6 Crossover 56
3.2.7 Selection for mutation 59
3.4.8 Mutation 59
3.4.9 Population Selection 61

Chapter 4 Computational Experiments and Analysis 63
4.1 Test Problem Generation and Solution Quality Measurement 63
4.2 Computational Experiments on One-dimensional System 65
4.2.1 Population Convergence and Perturbation 68
4.3 Computational Experiments on Two-dimensional System 72
4.3.1 The Gap between Crude Monte Carlo and Exact Evaluation 72
4.3.2 Result and Analysis 73

Chapter 5 Conclusion and Future 78

References 79
Appendix 83
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