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研究生： 陳一帆
Subrata, - Evan
論文名稱： Hybrid Model of RSSA and Modified Grover Algorithm in Optimizing Tower Crane Location
Hybrid Model of RSSA and Modified Grover Algorithm in Optimizing Tower Crane Location
指導教授： 呂守陞
Sou-Sen Leu
口試委員: 林建良
none
楊亦東
I-Tung Yang
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： 2009
畢業學年度： 97
語文別： 英文
論文頁數： 170
外文關鍵詞： Grover Algorithm, RSSA, Searching Algorithm
相關次數： 點閱：237  下載：9 
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  •  上一筆

    • This thesis presents a hybrid model of a new and adopted searching algorithm from Computer Science, named Grover Algorithm with one of new established meta-heuristic algorithm, RSSA (Reduced Space Searching Algorithm). Since Grover Algorithm has to be applied in a quantum computer system which is not available at this moment, only the basic concept will be occupied, that makes it more related to enumeration. As a meta- heuristic algorithm whose basic concept is adopted from reducing search space strategy, RSSA will greatly reduce the original search space and delivers the remaining search space to Grover Algorithm to find the best solution. The combination of both algorithms is validated by solving multiple peak functions. The traditional Genetic Algorithm (GA) is used as a standard of comparison in appraising the RSSA-Grover result. Simulation result shows that RSSA-Grover performed better than GA due to its accuracy. Furthermore, this proposed algorithm is successfully applied to solve the real case of Tower Crane location selection, whose objective is to find the minimum duration due to cost minimizing, concealed by abundant constraints and decision variables.

    LIST OF CONTENTS Cover i Acknowledgement ii Abstract iv List of Contents v List of Tables viii List of Figures ix Chapter 1 Introduction 1 1.1 Research Background 1 1.2 Research Objective 2 1.3 Research Scope 2 1.4 Research Methodology 3 1.5 Research Outline 5 Chapter 2 Literature Study and Review 7 2.1 Related Research of Optimization Algorithm 7 2.2 Related Researches of Tower Crane Study 8 2.3 Chapter Summary 15 Chapter 3 Research Methodology 16 3.1 Algorithm Issues and Limitation 16 3.1.1 Meta Heuristic 17 3.1.2 Algorithm Issue Arisen 19 3.2 Computer Performance Analysis 21 3.3 Reduced Space Searching Algorithm (RSSA)23 3.4 Quantum Algorithm and Grover Implementation 31 3.5 Hybrid Algorithm Implementation 39 3.6 Genetic Algorithm Implementation 41 3.6.1 Chromosome Definition (Initialization Phase) 44 3.6.2 Roulette Wheel Selection (Selection Phase) 45 3.6.3 GA Parameters 45 3.7 Chapter Summary 46 Chapter 4 Model Construction and Verification 47 4.1 Multiple Peak Equations 47 4.2 Model Construction 50 4.2.1 Model Construction of Rastrigin equation 50 4.2.2 Model Construction of Rosenbrock equation 53 4.2.3 Model Construction of Schaffer equation 57 4.2.4 Model Construction of Griewangk equation 60 4.3 Result Summary and Verification 64 4.4 Analysis and Discussion 69 4.5 Chapter Summary 73 Chapter 5 Case Study 74 5.1 Case Background 74 5.2 Case Modifications 77 5.3 Case Objective 78 5.4 Problem Formulation 78 5.5 Assumptions and Limitations 83 5.6 Genetic Algorithm Implementation 85 5.6.1 Chromosomes Definition 85 5.6.2 GA Pseudo Code 85 5.6.3 GA Parameters 86 5.6.4 Result 87 5.6.5 Sensitivity Analysis 88 5.7 RSSA Implementation 90 5.7.1 RSSA Parameters 90 5.7.2 RSSA Pseudo Code 91 5.7.3 Result 91 5.7.4 Sensitivity Analysis 93 5.8 Research Algorithm (RSSA-Grover) Implementation 94 5.8.1 Computer Performance Analysis 94 5.8.2 Search Space Reduction by RSSA 95 5.8.3 Grover Implementation 95 5.9 Result Analysis and Discussion 96 5.10 Chapter Summary 102 Chapter 6 Conclusion and Future Research 103 6.1 Conclusion 103 6.2 Future Research 104 List of References 106 Appendix A-1

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