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研究生： 陳勇
Albertus - Arief Herdany
論文名稱： Evolutionary Big Bang – Big Crunch Algorithm for Construction Engineering Optimization Problem
Evolutionary Big Bang – Big Crunch Algorithm for Construction Engineering Optimization Problem
指導教授： 鄭明淵
Min-Yuan Cheng
口試委員: 邱建國
Chien-Kuo Chiu
吳育偉
Yu-Wei Wu
潘南飛
Nang-fei Pan
學位類別： 碩士
Master
系所名稱： 工程學院 - 營建工程系
Department of Civil and Construction Engineering
論文出版年： 2014
畢業學年度： 102
語文別： 英文
論文頁數： 127
中文關鍵詞： Benchmark function 、Big Bang – Big Crunch 、Evolutionary 、Hybrid algorithm 、Structural optimization 、Tower crane layout optimization.
外文關鍵詞： Benchmark function, Big Bang – Big Crunch, Evolutionary, Hybrid algorithm, Structural optimization, Tower crane layout optimization.
相關次數： 點閱：395  下載：9 
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    • Construction Engineering Optimization problem is a complex optimization problem and also has many type of problem such as Structural Optimization, Tower Crane Layout Optimization, etc. Structural optimization problem is an optimization problem with large number of variables and constraint that prevent engineer to get the optimum design in reasonable time. Meanwhile, Tower Crane Layout (TCL) problem is a complex and uneasy optimization which involves arranging and determining the tower crane locations and also the locations of supply points. To solve the structural optimization and TCL problem, metaheuristic approach was applied. This study proposes a new hybrid metaheuristic algorithm named Evolutionary Big Bang – Big Crunch (EBB-BC). EBB-BC is a hybrid algorithm from Big Bang - Big Crunch algorithm, Neighborhood Search and Differential Evolution algorithm using collaborative combination hybridization concept. This study compares the performance of EBB-BC with other well-known algorithm using some unconstrained benchmark functions problems. Moreover, this study compares EBB-BC with other well-known metaheuristic algorithm in solving structural optimization problem and TCL problem. The results show the excellence performance of EBB-BC where EBB-BC outperforms all other algorithms tested both in solving benchmark functions, structural optimization and TCL problem.

    Construction Engineering Optimization problem is a complex optimization problem and also has many type of problem such as Structural Optimization, Tower Crane Layout Optimization, etc. Structural optimization problem is an optimization problem with large number of variables and constraint that prevent engineer to get the optimum design in reasonable time. Meanwhile, Tower Crane Layout (TCL) problem is a complex and uneasy optimization which involves arranging and determining the tower crane locations and also the locations of supply points. To solve the structural optimization and TCL problem, metaheuristic approach was applied. This study proposes a new hybrid metaheuristic algorithm named Evolutionary Big Bang – Big Crunch (EBB-BC). EBB-BC is a hybrid algorithm from Big Bang - Big Crunch algorithm, Neighborhood Search and Differential Evolution algorithm using collaborative combination hybridization concept. This study compares the performance of EBB-BC with other well-known algorithm using some unconstrained benchmark functions problems. Moreover, this study compares EBB-BC with other well-known metaheuristic algorithm in solving structural optimization problem and TCL problem. The results show the excellence performance of EBB-BC where EBB-BC outperforms all other algorithms tested both in solving benchmark functions, structural optimization and TCL problem.

    Table of Contents ABSTRACT i ACKNOWLEDGEMENT ii ABBREVIATIONS AND SYMBOLS vi LIST OF FIGURES viii LIST OF TABLES ix CHAPTER 1. INTRODUCTION 1 1.1 Research Motivation 1 1.2 Research Objective 4 1.3 Scope Definition 5 1.4 Research Methodology 5 1.4.1 Introduction 8 1.4.2 Literature Review 9 1.4.3 Model Construction 9 1.4.4 Model Evaluation and Application 10 1.4.5 Conclusions and Recommendations 11 1.5 Thesis Outline 11 CHAPTER 2. LITERATURE REVIEW 13 2.1 Structural Optimization Problem 13 2.2 Tower Crane Layout Optimization Problem 14 2.3 Big Bang – Big Crunch (BB-BC) Algorithm 17 2.4 Differential Evolution (DE) Algorithm 20 2.5 Neighborhood Search (NS) 26 CHAPTER 3. EVOLUTIONARY BIG BANG – BIG CRUNCH 27 CHAPTER 4. BENCHMARK FUNCTIONS AND CASE STUDY 34 4.1 Benchmark Function 34 4.1.1 Benchmark Function – Problem Definition 34 4.1.2 Benchmark Function – Model Application 40 4.1.3 Benchmark Function – Result and Discussion 42 4.2 Case Study 1 : Welded Beam Design Optimization 44 4.2.1 Case Study 1 – Problem Definition 44 4.2.2 Case Study 1 – Model Application 47 4.2.3 Case Study 1 – Result and Discussion 48 4.3 Case Study 2 : Reinforced Concrete Beam Design Optimization 50 4.3.1 Case Study 2 – Problem Definition 50 4.3.2 Case Study 2 – Model Application 52 4.3.3 Case Study 2 – Result and Discussion 54 4.4 Case Study 3 : Tower Crane Layout With Material Quantity Supply and Demand Optimization 55 4.4.1 Case Study 3 – Problem Definition 55 4.4.2 Case Study 3.1 – Model Application of Single Tower Crane 60 4.4.3 Case Study 3.1 – Result and Discussion of Single Tower Crane 62 4.4.4 Case Study 3.2 – Model Application of Multi-Tower Crane 64 4.4.5 Case Study 3.2 – Result and Discussion of Multi-Tower Crane 66 CHAPTER 5. CONCLUSIONS AND RECOMMENDATIONS 70 5.1 Conclusions 70 5.2 Recommendations 71 REFERENCES 72 APPENDIX A (Matlab Code of EBB-BC for Benchmark Function) 79 APPENDIX B (Matlab Code of EBB-BC for Case Study) 89

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