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研究生:胡振嘉
研究生(外文):Jhen-JiaHu
論文名稱:共生演化及群智慧為基礎的最佳化演算法之研究與其應用
論文名稱(外文):Study of Symbiotic Evolution-Based and Swarm Intelligence-Based Optimization Algorithms and Their Applications
指導教授:李祖聖
指導教授(外文):Tzuu-Hseng S. Li
學位類別:博士
校院名稱:國立成功大學
系所名稱:電機工程學系碩博士班
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2010
畢業學年度:99
語文別:英文
論文頁數:203
中文關鍵詞:共生演化群智慧最佳化演算法演化計算基因調控網路論理式成長模型共生關係粒子群最佳化串級式非線性系統模糊滑動模式控制李亞普諾夫定理
外文關鍵詞:symbiotic evolutionswarm intelligenceoptimization algorithmevolutionary computationgenetic regulatory networklogical growth modelsymbiotic relationshipparticle swarm optimizationcascade nonlinear systemfuzzy sliding-mode controlLyapunov theory
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本論文針對演化計算的結構和設計方法,研究共生演化和群智慧為基礎的最佳化演算法及其應用。首先,引用基因調控網路建立基因調控為基礎之共生演化演算法,並配合驗證函數測試性能與獲得模糊控制倒單擺系統和類神經控制天線臂系統之最佳化參數。其次,針對共生演化所獲得之平均適應值,利用群智慧的方式和基因階級來設計粒子群為基礎之共生演化演算法並針對實驗結果進行定量分析與定性分析。接著,本論文提出以生態學的論理式成長模型和生物學的共生關係之生態-生物行為為基礎之粒子群最佳化演算法,並應用在串級式非線性系統模糊滑動模式控制上。在模糊滑動模式控制方面,其模糊系統的設計方式,係運用李亞普諾夫定理所提供的能量衰減原理和受控對象的數學模型,針對模糊滑動模式控制器的控制輸入做即時的調整。在基於演化計算之模糊滑動模式控制器方面,乃採用所提出演化演算法將模糊滑動模式控制器之輸入和輸出尺度因子予以最佳化。最後,根據李亞普諾夫定理分析閉迴路系統的穩定性和推導模糊系統的規則和封閉數學式。本論文所提出的共生演化為基礎和群智慧為基礎的演化計算,經驗證函數模擬測試並與傳統演化計算在基因演算法、共生演化及粒子群最佳化等方面相比較,均有更佳的性能。
In this dissertation, studies and applications on the symbiotic evolution (SE)-based and the swarm intelligence (SI)-based optimization algorithms of structures and design methodologies for evolutionary computation (EC) are presented. Firstly, the genetic regulatory network-based symbiotic evolutionary (GRNSE) algorithm is proposed by using a genetic regulatory network (GRN). The performance of the proposed optimization algorithm is verified by using benchmark problem. Besides, the parameters in the fuzzy control scheme of the inverted pendulum system and the neural network control scheme of the antenna arm system are determined by the optimization algorithm. Secondly, the design schemes of the particle swarm-based symbiotic evolutionary (PSSE) algorithm are addressed by using the swarm intelligence and gene hierarchy according to the average fitness value of symbiotic evolution. The quantitative analysis and the qualitative analysis of experimental results are performed. Finally, we adopt the logical growth model of ecology and symbiotic relationship of biology then present the ecological-biological behavior-based particle swarm optimization (EBB-PSO) algorithm. The proposed algorithm is verified by the benchmark problem and it is applied to optimize fuzzy sliding-mode control (FSMC) for a cascade nonlinear system. In the FSMC, the control input of sliding-mode for the FSMC is tuned by the fuzzy system which is proved by the Lyapunov stability theory and by the mathematic model of sliding surface. In the EC-based FSMC, the EBB-PSO algorithm is utilized to find the global optimization scaling factors of the input and output variables for the FSMC. Finally, the global asymptotical stability of the EC-based FSMC, the closed-from of fuzzy system, and the fuzzy rule of fuzzy system are confirmed by the Lyapunov stability theory. Verifications of benchmarks demonstrate that three proposed evolutionary computation algorithms are effective and can provide much better performance in comparison with conventional evolutionary computations on the genetic algorithm (GA), symbiotic evolution (SE), and particle swarm optimization (PSO).
Contents

Abstract I
Acknowledgement IV
Contents V
List of Acronyms IX
List of Symbols XI
List of Figures XIII
List of Tables XV
Chapter 1. Introduction 1
1.1 Introduction of Evolutionary Computation 1
1.2 Genetic Algorithm 2
1.2.1 The Principle of GA 2
1.2.2 Survey of Related Works of GA 5
1.3 Symbiotic Evolution 7
1.3.1 The Principle of SE 7
1.3.2 Survey of Related Works of SE 10
1.4 Particle Swarm Optimization 14
1.4.1 The Principle of PSO 14
1.4.2 Survey of Related Works of PSO 17
1.5 The Organization of This Dissertation 20
Chapter 2. Genetic Regulatory Network-Based Symbiotic Evolution 23
2.1 Open Issue of GA-Type Evolutionary Computation 23 2.2 The Genetic Regulatory Network Model 26
2.3 The Genetic Regulatory Network-Based Offspring 29
2.4 The Proposed GRNSE Algorithm 31
2.4.1 SE-Based Population Initialization 33
2.4.2 GRN-Based Population Generation 33
2.5 The Schema Theorem for GRNSE Algorithm 34
2.5.1 Introduction of Schema Theorem 34
2.5.2 The Discussion of Schema Theorem 37
2.5.3 The GA-Type Operators 38
Chapter 3. Experimental Verifications of the GRNSE Algorithm 45
3.1 Introduction of Benchmarks 45
3.2 Test Strategies and Metrics 46
3.3 Configuration Settings 48
3.4 Results and Analyses 49
3.4.1 Study 1: Comparison of GA, SE, and GRNSE 50
3.4.2 Study 2: Influence of Dimension 53
3.4.3 Study 3: Contributions of Gene Regulatory Network 55
3.4.4 Study 4: Effect of Individual Population Size 58
3.4.5 Study 5: Proper Setting of Gene Population Size 62
3.4.6 Study 6: Effect of Various Population Rate 66
3.5 Summary 69
Chapter 4. GRNSE-Based Optimization Control Design 71
4.1 Introduction of Optimization Control 71
4.2 The Fuzzy Control Scheme of the Inverted Pendulum System 73
4.2.1 Fuzzy Control System of Inverted Pendulum 75
4.2.2 Application of GRNSE to Fuzzy Control System 78
4.3 The Neural Network Control Scheme of the Antenna Arm System 82
4.3.1 Application of GRNSE to Neural Network System 83
4.4 Results and Analyses 87
4.4.1 Study 1: Random Initial Conditions 87
4.4.2 Study 2: Constant Initial Conditions 90
4.5 Summary 92
Chapter 5. Particle Swarm-Based Symbiotic Evolution 93
5.1 The Gene Particle Swarm Model 93
5.2 The Proposed PSSE System 94
5.2.1 Prior Definition for a PSSE System 95
5.2.2 Two Strategies for PSSE System 98
5.3 The Proposed PSSE Algorithm 100
5.4 Experimental Verifications of the PSSE Algorithm 104
5.4.1 Benchmarks and Materials 104
5.4.2 Configuration Settings 105
5.4.3 Verification Strategy 106
5.4.4 Results and Analyses 118
5.4.5 Remarks 121
5.5 Summary 124
Chapter 6. Ecological Biological-Based Particle Swarm Optimization 127
6.1 Open Issue of PSO-Type Evolutionary Computation 127 6.2 The Ecological Biological-Behavior Model 129
6.3 The Proposed EBB-PSO Algorithm 131
6.4 Experimental Verifications of the EBB-PSO Algorithm 133
6.4.1 Benchmark: Uni-Modal and Multi-Modal Function 133
6.4.2 Configuration Settings 134
6.4.3 Results and Analyses 137
6.4.4 Remarks 138
6.5 EBB-PSO-Based Optimization Control Design 140
6.5.1 A Cascade Nonlinear System: RTAC/TORA System 140
6.5.2 Lyapunov-Based Fuzzy Sliding-Mode Control 142
6.5.3 Results and Analyses 150
6.5.4. Remarks 154
6.6 Summary 154
Chapter 7. Conclusions and Future Work 156
7.1 Conclusions 156
7.2 Future Work 158
References 159
Appendix A. Benchmark Problems 169
Appendix B. Design and Analysis of LFSMC 183
Biography 201
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