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創新啟發式水母優化演算法於工程管理之應用

為了解決CBO infrastructure的問題，作者NHAT DINH TRUONG 這樣論述：

ABTRACT iACKNOWLEDGEMENTS iiiTABLE OF CONTENTS ivLIST OF FIGURES ixLIST OF TABLES xiABBREVIATIONS AND SYMBOLS xiiiAbbreviations xiiiSymbols xvCHAPTER 1: INTRODUCTION 11.1 Research Background and Motivations 11.2 Research Objectives 31.3 Research Scope 31.4 Struct

ure of Dissertation 4CHAPTER 2: LITERATURE REVIEW 52.1 Background of Optimization Problem 52.2 Review of Single Metaheuristic Optimization 72.3 Review of Multi-Objective Optimization 102.4 Enhancing Machine Learning Performance by Metaheuristic Optimization 12CHAPTER 3: SINGLE JELL

YFISH SEARCH OPTIMIZER 183.1 Inspiration 183.2 Mathematical Model for the Optimization Algorithm 203.2.1 Ocean Current 213.2.2 Jellyfish Swarm 233.2.3 Time Control Mechanism 243.3 Population initialization 263.4 Boundary Conditions 313.5 Schematic Representation of OSOJS Opti

mizer 313.6 Schematic Representation of SOJS Optimizer 31CHAPTER 4: MULTIOBJECTIVE JELLYFISH SEARCH OPTIMIZER 324.1 Mechanisms of Motion of MOJS 324.1.1 Elite Population 324.1.2 Elitist Selection 324.1.3 Lévy Flight 344.1.4 Update Archive Population 344.1.5 Mathematical Multi

-Objective Jellyfish Search Optimizer 354.2 Increase Diversification by Opposition-Based Jumping 364.3 Pseudo-Code and Flowchart of MOJS 36CHAPTER 5: EVALUATION JS ALGORITHM BY MATHEMATICAL TEST 395.1 Mathematical Benchmark Functions 395.1.1 Single Objective Functions 39a. Small/Av

erage-scale Functions 40b. Large-scale Functions (CEC2005) 435.1.2 Multi-Objective Functions 445.2 Performance Metrics 455.2.1 Evaluation of Single-Objective Algorithm 45a. Hit Rate 45b. Wilcoxon Rank-sum Test 45c. Computation Time 465.2.2 Evaluation of Multi-Objective Algori

thm 46a. Hypervolume Index 46b. Generational Distance 46c. Spacing (SP) 47d. Wilcoxon Rank-sum Test 475.3 Parameter settings 485.3.1 Parameters for Single Objective Algorithm 48a. Effect of Population Initialization Techniques on Solution Quality 48b. Balancing exploration an

d exploitation 48c. Parameters for Single-Objective Algorithm 545.3.2 Parameters for Multi-Objective Algorithm 545.4 Algorithm Comparison on Solving Benchmark Functions 555.4.1 Comparision of OSOJS with SOJS 555.4.2 Comparision of Single Objective Algorithms 555.4.3 Comparions of M

ultiple Objective Algorithms 755.5 Performance of SOJS and MOJS algorithms 905.5.1 Performance of Single Objective Algorithm (SOJS) 90a. Capacity of Exploration 90b. Capacity of Exploitation 90c. Convergence Capability 915.5.2 Performance of Multiple Objective Algorithm (MOJS) 9

3CHAPTER 6: SINGLE OBJECTIVE JELLYFISH SEARCH FOR SOLVING STRUCTURAL PROBLEMS 946.1 Discrete Design Optimization of Tower Structures 946.1.1 Formulation of Optimization Problem 946.1.2 Determination of Population Size and Number of Iterations 966.1.3 Sensitivity analysis of Initial Value

εo 986.2 Structural Tower Designs 986.2.1 25-Bar Tower 986.2.2 52-Bar Tower 1036.2.3 582-Bar Tower 1036.3 Discussion 109CHAPTER 7: MULTIOBJETCIVE JELLYFISH SEARCH FOR SOLVING STRUCTURAL PROBLEMS 1107.1 Constrained Structural Problem 1107.2 Structural tower designs 1127.2.

1 25-Bar Tower Design 1127.2.2 160-Bar Tower Design 1157.2.3 942-Bar Tower Design 1197.3 Discussion 121CHAPTER 8: APPLICATION OF METAHEURISTIC OPTIMIZATION IN MACHINE LEARNING 1238.1 Least Squares Support Vector Regression and Feature–Based Regressions 1238.1.1 Least Squares Suppor

t Vector Regression 1238.1.2 Feature–Based Regressions 1248.1.3 Evaluation 124a. Cross-Fold Validation 124b. Performance Metrics 1258.2 Optimized Regression System 1268.2.1 JS-WFLSSVR System 1268.2.2 Graphical User Interface 1268.3 Experiment and Results 1318.3.1 Data Coll

ection 1318.3.2 System Evaluation 1328.3.3 Sensitivity of Performance Metrics to Feature Selection 1338.4 Discussion 139CHAPTER 9: CONCLUTIONS AND RECOMMNEDATIONS 1429.1 Review Research Purposes 1429.2 Research Contributions 1429.3 Research Limitation 1449.4 Future Research W

orks 144REFERENCES 146Appendix A. Used Hardware and Software 159Appendix B. Tutorial of SOJS 160B.1 SOJS for Benchmark Functions 160B.2 SOJS for Structural Problems 162B.2.1 25 Bar Tower Design 162B.2.2 52 Bar Tower Design 165B.2.3 582 Bar Tower Design 166Appendix C. Tutor

ial of MOJS 167C.1 MOJS for Benchmark Functions 167C.2 MOJS for Structural Problems 169C.2.1 25 Bar Tower Design 169C.2.2 160 Bar Tower Design 171C.2.3 942 Bar Tower Design 173Appendix D. Tutorial of JS-WFLSSVR 175D.1 Map of tutorial 175D.2 Design Interface of JS-WFLLSVR 1

75D.2.1 Main 175D.2.2 weightlssvr_evaluation_prediction 179D.2.3 view_result 188D.3 Guide of using JS-WFLLSVR 190D.2.1 Evaluation 190D.2.2 Prediction 200Appendix E. MATLAB Codes 207E.1 MATLAB Codes of SOJS Algorithm 207E.2 MATLAB Codes of MOJS Algorithm 231E.3 MATLAB Codes

of SOJS for Sovling Tower Design Problems 244E.3.1 25 Bar Tower Design 244E.3.2 52 Bar Tower Design 248E.3.3 582 Bar Tower Design 252E.4 MATLAB Codes of MOJS for Sovling Tower Design Problems 257E.4.1 25 Bar Tower Design 257E.4.2 160 Bar Tower Design 261E.4.3 942 Bar Tower Desi

gn 268E.5 MATLAB Codes of JS-WFLSSVR Model 273Appendix F. Data for JS-WFLSSVR Model 284

以機具閒置最小化為目標之非連續線性排程模式

為了解決CBO infrastructure的問題，作者Agung Budiwirawan 這樣論述：

線性調度方法 (LSM) 適合用作線性建設項目 (LCP) 的調度方法。傳統的 LSM 使用工作連續性來監控資源分配的連續性，前提是具有相同工作類型的活動使用相同的資源，而具有不同工作類型的活動將使用不同的資源。對於使用重型設備組合來完成其活動的建築工程，情況並非總是如此，例如，道路網絡維護項目。本研究提出了裝備組合與配置（ECC）的概念，即船員用來完成一項活動的概念由不同設備的組合與配置組成。在路網維護項目中，自卸車是一種設備，可用於多種不同工作類型的活動。這種情況導致工作連續性模式並不總是與設備分配連續性模式相同。為了克服這個問題，本研究提出了一個路網維護項目的優化模型，通過應用設備組合

和配置的概念來最大限度地減少閒置設備。約束編程用於構建此模型。該模型應用於使用多個場景的路網維護項目示例，並產生了令人滿意的結果。