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研究生: NHAT DINH TRUONG
NHAT D.TRUONG
論文名稱: 創新啟發式水母優化演算法於工程管理之應用
Jellyfish Inspired Optimizer: A Novel Metaheuristic Algorithm For Engineering Applications
指導教授: 周瑞生
Jui-Sheng Chou
口試委員: 王維志
Wei-Chih Wang
曾仁杰
Reng-Jye Dzeng
曾惠斌
Hui-Ping Tserng
陳柏翰
Po-Han Chen
鄭明淵
Min-Yuan Cheng
楊亦東
I-Tung Yang
周瑞生
Jui-Sheng Chou
學位類別: 博士
Doctor
系所名稱: 工程學院 - 營建工程系
Department of Civil and Construction Engineering
論文出版年: 2020
畢業學年度: 109
語文別: 英文
論文頁數: 312
中文關鍵詞: 元啟發式算法的設計群智能優化水母搜索優化器多目標水母搜索基準功能帕累托優勢結構設計優化人工智能纖維增強土壤峰值剪切強度
外文關鍵詞: Design of metaheuristic algorithm, Swarm intelligence optimization, Jellyfish search optimizer, Multi-objective jellyfish search, Benchmark functions, Pareto dominance, Structural design optimization, Artificial intelligent, Fiber-reinforced soil, Peak shear strength
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  • ABTRACT i ACKNOWLEDGEMENTS iii TABLE OF CONTENTS iv LIST OF FIGURES ix LIST OF TABLES xi ABBREVIATIONS AND SYMBOLS xiii Abbreviations xiii Symbols xv CHAPTER 1: INTRODUCTION 1 1.1 Research Background and Motivations 1 1.2 Research Objectives 3 1.3 Research Scope 3 1.4 Structure of Dissertation 4 CHAPTER 2: LITERATURE REVIEW 5 2.1 Background of Optimization Problem 5 2.2 Review of Single Metaheuristic Optimization 7 2.3 Review of Multi-Objective Optimization 10 2.4 Enhancing Machine Learning Performance by Metaheuristic Optimization 12 CHAPTER 3: SINGLE JELLYFISH SEARCH OPTIMIZER 18 3.1 Inspiration 18 3.2 Mathematical Model for the Optimization Algorithm 20 3.2.1 Ocean Current 21 3.2.2 Jellyfish Swarm 23 3.2.3 Time Control Mechanism 24 3.3 Population initialization 26 3.4 Boundary Conditions 31 3.5 Schematic Representation of OSOJS Optimizer 31 3.6 Schematic Representation of SOJS Optimizer 31 CHAPTER 4: MULTIOBJECTIVE JELLYFISH SEARCH OPTIMIZER 32 4.1 Mechanisms of Motion of MOJS 32 4.1.1 Elite Population 32 4.1.2 Elitist Selection 32 4.1.3 Lévy Flight 34 4.1.4 Update Archive Population 34 4.1.5 Mathematical Multi-Objective Jellyfish Search Optimizer 35 4.2 Increase Diversification by Opposition-Based Jumping 36 4.3 Pseudo-Code and Flowchart of MOJS 36 CHAPTER 5: EVALUATION JS ALGORITHM BY MATHEMATICAL TEST 39 5.1 Mathematical Benchmark Functions 39 5.1.1 Single Objective Functions 39 a. Small/Average-scale Functions 40 b. Large-scale Functions (CEC2005) 43 5.1.2 Multi-Objective Functions 44 5.2 Performance Metrics 45 5.2.1 Evaluation of Single-Objective Algorithm 45 a. Hit Rate 45 b. Wilcoxon Rank-sum Test 45 c. Computation Time 46 5.2.2 Evaluation of Multi-Objective Algorithm 46 a. Hypervolume Index 46 b. Generational Distance 46 c. Spacing (SP) 47 d. Wilcoxon Rank-sum Test 47 5.3 Parameter settings 48 5.3.1 Parameters for Single Objective Algorithm 48 a. Effect of Population Initialization Techniques on Solution Quality 48 b. Balancing exploration and exploitation 48 c. Parameters for Single-Objective Algorithm 54 5.3.2 Parameters for Multi-Objective Algorithm 54 5.4 Algorithm Comparison on Solving Benchmark Functions 55 5.4.1 Comparision of OSOJS with SOJS 55 5.4.2 Comparision of Single Objective Algorithms 55 5.4.3 Comparions of Multiple Objective Algorithms 75 5.5 Performance of SOJS and MOJS algorithms 90 5.5.1 Performance of Single Objective Algorithm (SOJS) 90 a. Capacity of Exploration 90 b. Capacity of Exploitation 90 c. Convergence Capability 91 5.5.2 Performance of Multiple Objective Algorithm (MOJS) 93 CHAPTER 6: SINGLE OBJECTIVE JELLYFISH SEARCH FOR SOLVING STRUCTURAL PROBLEMS 94 6.1 Discrete Design Optimization of Tower Structures 94 6.1.1 Formulation of Optimization Problem 94 6.1.2 Determination of Population Size and Number of Iterations 96 6.1.3 Sensitivity analysis of Initial Value εo 98 6.2 Structural Tower Designs 98 6.2.1 25-Bar Tower 98 6.2.2 52-Bar Tower 103 6.2.3 582-Bar Tower 103 6.3 Discussion 109 CHAPTER 7: MULTIOBJETCIVE JELLYFISH SEARCH FOR SOLVING STRUCTURAL PROBLEMS 110 7.1 Constrained Structural Problem 110 7.2 Structural tower designs 112 7.2.1 25-Bar Tower Design 112 7.2.2 160-Bar Tower Design 115 7.2.3 942-Bar Tower Design 119 7.3 Discussion 121 CHAPTER 8: APPLICATION OF METAHEURISTIC OPTIMIZATION IN MACHINE LEARNING 123 8.1 Least Squares Support Vector Regression and Feature–Based Regressions 123 8.1.1 Least Squares Support Vector Regression 123 8.1.2 Feature–Based Regressions 124 8.1.3 Evaluation 124 a. Cross-Fold Validation 124 b. Performance Metrics 125 8.2 Optimized Regression System 126 8.2.1 JS-WFLSSVR System 126 8.2.2 Graphical User Interface 126 8.3 Experiment and Results 131 8.3.1 Data Collection 131 8.3.2 System Evaluation 132 8.3.3 Sensitivity of Performance Metrics to Feature Selection 133 8.4 Discussion 139 CHAPTER 9: CONCLUTIONS AND RECOMMNEDATIONS 142 9.1 Review Research Purposes 142 9.2 Research Contributions 142 9.3 Research Limitation 144 9.4 Future Research Works 144 REFERENCES 146 Appendix A. Used Hardware and Software 159 Appendix B. Tutorial of SOJS 160 B.1 SOJS for Benchmark Functions 160 B.2 SOJS for Structural Problems 162 B.2.1 25 Bar Tower Design 162 B.2.2 52 Bar Tower Design 165 B.2.3 582 Bar Tower Design 166 Appendix C. Tutorial of MOJS 167 C.1 MOJS for Benchmark Functions 167 C.2 MOJS for Structural Problems 169 C.2.1 25 Bar Tower Design 169 C.2.2 160 Bar Tower Design 171 C.2.3 942 Bar Tower Design 173 Appendix D. Tutorial of JS-WFLSSVR 175 D.1 Map of tutorial 175 D.2 Design Interface of JS-WFLLSVR 175 D.2.1 Main 175 D.2.2 weightlssvr_evaluation_prediction 179 D.2.3 view_result 188 D.3 Guide of using JS-WFLLSVR 190 D.2.1 Evaluation 190 D.2.2 Prediction 200 Appendix E. MATLAB Codes 207 E.1 MATLAB Codes of SOJS Algorithm 207 E.2 MATLAB Codes of MOJS Algorithm 231 E.3 MATLAB Codes of SOJS for Sovling Tower Design Problems 244 E.3.1 25 Bar Tower Design 244 E.3.2 52 Bar Tower Design 248 E.3.3 582 Bar Tower Design 252 E.4 MATLAB Codes of MOJS for Sovling Tower Design Problems 257 E.4.1 25 Bar Tower Design 257 E.4.2 160 Bar Tower Design 261 E.4.3 942 Bar Tower Design 268 E.5 MATLAB Codes of JS-WFLSSVR Model 273 Appendix F. Data for JS-WFLSSVR Model 284

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