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系統識別號 U0026-1211201213242500
論文名稱(中文) 混合田口─混沌方法、免疫演算法及人工蜂群演算法之研究與其應用
論文名稱(英文) Study of Hybrid Taguchi-Chaos Method, Immune Algorithm and Artificial Bee Colony Algorithm and Its Applications
校院名稱 成功大學
系所名稱(中) 電機工程學系碩博士班
系所名稱(英) Department of Electrical Engineering
學年度 101
學期 1
出版年 101
研究生(中文) 田佳平
研究生(英文) Jia-Ping Tien
學號 N28951154
學位類別 博士
語文別 英文
論文頁數 90頁
口試委員 口試委員-王振興
口試委員-蔡聖鴻
口試委員-姚賀騰
召集委員-李祖添
指導教授-李祖聖
口試委員-王伯群
口試委員-莊智清
口試委員-鄭銘揚
口試委員-張永昌
中文關鍵字 人工蜂群演算法  混沌擾動操作法  克隆選擇演算法  模糊控制  免疫演算法  比例─積分─微分控制  田口方法 
英文關鍵字 Artificial bee colony algorithm  Chaos operation  Clonal selection algorithm  Fuzzy control  Immune algorithm  PID control  Taguchi method 
學科別分類
中文摘要 本論文經由混合田口方法、混沌擾動方法、多層次的免疫演算法 (MIA)、克隆選擇演算法 (CLONALG)以及人工蜂群演算法 (ABC),提出三個不同的進化演算法。其中,多層次的免疫演算法及克隆選擇演算法是由免疫演算法衍生而來。因此,這些算法分別稱為混合田口─混沌、多層次免疫人工蜂群演算法 (HTCMIABC)、混合田口-混沌人工蜂群演算法 (HTCABC) 及混合克隆選擇人工蜂群演算法 (HCABC)。HTCMIABC演算法包含兩個主要階段。首先,我們利用多層次的免疫演算法作為識別階段以平衡局部和全局搜索,並加快搜索速度,進而增強進化階段。其次,進化階段是建立在人工蜂群演算法和混沌擾動操作,使計算具有勘探和開發能力。此外,於識別階段與進化階段之間插入田口方法和交配操作,將一些抗體可以再重組及轉變,更加改進演算法搜索能力。HTCABC演算法,運用混沌搜索演算法和自適調整搜索範圍法,以提昇人工蜂群演算法性能。然後,導入田口方法和交配操作於混沌人工蜂群中,以增進搜索能力。更因為HTCABC演算法採用精英策略的自然現象和招募新偵察蜜蜂,故能夠保持種群的多樣性及逃離局部最佳解的能力。此外,演算法沒有複雜的參數設定。HCABC演算法,使用人工蜂群演算法的優點改進克隆選擇演算法的突變機制,以提高勘探和開發的能力。
最後,HTCMIABC演算法用於非線性混沌系統的參數識別。模擬結果顯示,HTCMIABC較現有一些典型演算法更有效。HTCABC演算法經由一些標準測試函數對其進行檢測,並且該方法也應用於解決混沌系統的參數識別。模擬結果顯示,HTCABC比文獻記述的一些演算法更為有效。另外,模擬結果也顯示HCABC演算法可以有效地實現最佳化比例─積分─微分型的模糊控制器的結構和參數。
英文摘要 In this dissertation, three different evolutionary algorithms are proposed by hybridizing the Taguchi method, chaos disturbance method, multilevel immune algorithm (MIA), clonal selection algorithm (CLONALG), and artificial bee colony algorithm (ABC). Including multi-level immune algorithm and clonal selection algorithm is derived by the immune algorithm. These algorithms are thus called HTCMIABC, HTCABC, and HCABC. The HTCMIABC comprises two main different phases. We use the MIA as the recognition phase to balance local and global search and accelerate the search speed to enhance the evolutionary phase. Second, the evolutionary phase is built on the ABC and chaos disturbance operation to have the capabilities of exploration and exploitation. Moreover, the Taguchi method and crossover operation are inserted between the recognition phase and evolutionary phase for the recombination and diversification of several antibodies to improve the searching ability. In the HTCABC, the chaos search algorithm and adaptive bound method are adopted to improve the ABC performance. Then, the Taguchi method and crossover operation are incorporated into the chaos artificial bee colony (CABC) to accelerate the search capacity. Moreover, the natural phenomenon of the elite strategy is adopted and the recruitment of new scout bees is used for HTCABC, which can maintain the diversity of the population, and escape from local optima. Additionally, there is no complex parameter setting in the algorithm design. In the HCABC, the mutation mechanism of the CLONALG by using the advantages of ABC can improve the capabilities of exploration and exploitation.
Finally, the HTCMIABC algorithm is examined by parameter identification of nonlinear chaotic system. Simulation results show that the HTCMIABC is more efficient than some typical existing algorithms. The HTCABC algorithm is examined by using a set of benchmarks and the proposed approach is also applied to solve the parameter identification of a chaotic system. Simulation results show that the HTCABC is more efficient than some existing algorithms reported in the literature. In addition, the simulation results also demonstrate that HCABC can effectively achieve the best PID-like fuzzy controller structure and parameters.
論文目次 Abstract (Chinese) I
Abstract (English) III
Acknowledgment (Chinese) V
Contents VI
List of Tables IX
List of Figures XI
List of Acronyms XIII
Nomenclature XV
Chapter 1. Introduction
1.1 Preliminary 1
1.2 Dissertation Contributions 5
1.3 Dissertation Organization 7
Chapter 2. Hybrid Taguchi-Chaos Method, Multilevel Immune, and Artificial Bee Colony Algorithm
2.1 Introduction 9
2.2 Problem Statement 11
2.3 The Artificial Bee Colony Algorithm and Biological Immune System 12
2.3.1 Overview of the Artificial Bee Colony Algorithm 12
2.3.2 Biological Immune System 13
2.4 The Proposed HTCMIABC Algorithm 15
2.5 Simulation Results 23
2.5.1 Study 1: Comparison of CLONALG, ABC, and HTCMIABC 24
2.5.2 Study 2: Searching Efficiency of HTCMIABC 25
2.5.3 Study 3: Effect of Population Size 25
2.5.4 Study 4: Effect of Noise 25
2.6 Summary 28
Chapter 3. Hybrid Taguchi-Chaos Method and Artificial Bee Colony Algorithm
3.1 Introduction 29
3.2 The Proposed HTCABC Algorithm 30
3.3 Simulation Results 39
3.3.1 Study 1: Comparison of ABC and HTCABC 44
3.3.2 Study 2: Comparison of CABC3 and HTCABC 45
3.3.3 Study 3: Comparison of GABC and HTCABC 46
3.3.4 Study 4: Comparison of RABC and HTCABC 47
3.3.5 Study 5: Comparison of IABC and HTCABC 48
3.3.6 Study 6: Simulation on Lorenz System 50
3.3.7 Study 7: Effect of Noise 52
3.3.8 Study 8: Online Chaotic System Parameter Identification 53
3.4 Summary 55
Chapter 4. Hybrid Clonal Selection Algorithm and the Artificial Bee Colony Algorithm for a Variable PID-like Fuzzy Controller Design
4.1 Introduction 56
4.2 Structure of the PID-like Fuzzy Controller 57
4.3 The Proposed HCABC Algorithm 60
4.4 Simulation Results 69
4.4.1 Study 1: Comparison of CLONALG, ABC and HCABC 69
4.4.2 Study 2: The HCABC for the PID-like Fuzzy Controller Simulation 71
4.4.3 Study 3: Effect of External Disturbance 74
4.4.4 Study 4: Comparison of HTCMIABC, HTCABC, and HCABC 74
4.5 Summary 76
Chapter 5. Conclusions and Future Works
5.1 Conclusions 77
5.2 Future Works 79

References 81
Biography 89
Publication List 90
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