粒子蜂群演算法於營建場址配置最佳化之研究｜國立臺灣科技大學博碩士論文系統

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研究生：	連立川 Li-Chuan Lien
論文名稱：	粒子蜂群演算法於營建場址配置最佳化之研究 Particle Bee Algorithm for Construction Site Layout Optimization
指導教授：	鄭明淵 Min-Yuan Cheng
口試委員:	張陸滿 Luh-Maan Chang 姚乃嘉 Nie-Jia Yau 曾仁杰 Ren-Jye Dzeng 晁立中 Li-Chung Chao 楊亦東 I-Tung Yang
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：	2011
畢業學年度：	99
語文別：	英文
論文頁數：	231
中文關鍵詞：	營建場址配置、群智慧演算法、蜂群演算法、粒子(鳥群)演算法、粒子蜂群演算法
外文關鍵詞：	construction site layout, swarm intelligence, bee algorithm, particle swarm optimization, particle bee algorithm
相關次數：	點閱：373 下載：7
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好的營建場址配置(Construction Site Layout, CSL)除了可使工程成本與進度節省更為明顯，也因其可能牽涉相對高水準的美學和實用性的特質，成為一個特別有趣的研究領域。然而對工程師而言，執行營建場址配置是一種相對複雜的組合排列問題。群智慧演算法(Swarm Intelligence, SI)，諸如蜂群演算法(Bee Algorithm, BA)與粒子(鳥群)演算法(Particle Swarm Optimization, PSO)，皆是靈感來自於群體行為模式的最佳化方法，至今這些方法已越來越多的被用在處理各種複雜的優化問題上。為了整合蜂群演算法全域搜索與鳥群演算法局部搜索的能力，本研究提出了混合蜂群和鳥群行為的群智慧演算法–粒子蜂群演算法(Particle Bee Algorithm, PBA)；另外為提高搜索效率及防止陷入局部解的問題，本研究提出鄰近視窗(neighborhood-windows, NW)搜尋及自參數更新(self-parameter-updating, SPU)技術。本研究以多維度的基準函數(multi-dimensional benchmark functions)來比較粒子蜂群演算法與著名最佳化演算法，諸如遺傳演算法(Genetic Algorithm, GA)、差分進化法(Differential Evolution, DE)、蜂群演算法與鳥群演算法的性能；此外，本研究另以樓面層級(Floor Level, FL)與工址層級(Site Level, SL)的營建場址配置假設問題，來進行粒子蜂群演算法、蜂群演算法與鳥群演算法的性能驗證。研究結果顯示，在多維度基準函數的性能比較及假設的營建場址配置最佳化問題驗證，粒子蜂群演算法皆有較其它演算法不錯的表現。

The construction site layout (CSL) design presents a particularly interesting area of study because of its relatively high level of attention to aesthetics and usability qualities, in addition to common engineering objectives such as cost and performance. However, they present a difficult combinatorial optimization problem for engineers. Swarm intelligence (SI), an approach to decision making that integrates collective social behavior models such as the bee algorithm (BA) and particle swarm optimization (PSO), is being increasingly used to resolve various complex optimization problems. In order to integrate BA global search ability with the local search advantages of PSO, this study proposes a new optimization hybrid swarm algorithm – the particle bee algorithm (PBA) which imitates the intelligent swarming behavior of honeybees and birds. This study also proposes a neighborhood-windows (NW) technique for improving searching efficiency as well as a self-parameter-updating (SPU) technique for preventing trapping into a local optimum in high dimensional problems. This study compares the performance of PBA with that of genetic algorithm (GA), evolutionary algorithms (EA), differential evolution (DE), bee algorithm (BA) and particle swarm optimization (PSO) for multi-dimensional benchmark function problems. Besides, this study compares PBA performance against bee algorithm (BA) and particle swarm optimization (PSO) performance in those hypothetical floor level (FL) and site level (SL) CSL problems. Results show PBA performance is comparable to those of the mentioned algorithms in the benchmark functions and can be efficiently employed to solve those hypothetical floor level and site level CSL problems with high dimensionality.

LIST OF CONTENTS
摘要	I
ABSTRACT	III
ACKNOWLEDGEMENTS	V
ABBREVIATIONS AND SYMBOLS	VII
LIST OF CONTENTS	XIII
LIST OF FIGURES	XVII
LIST OF TABLES	XXI
1. INTRODUCTION	1
1.1 Background of this research	1
1.2 Research motivation and objectives	2
1.3 Significance of this research	5
1.4 Research scope, assumptions and hypotheses	7
1.4.1 Research scope	7
1.4.2 Research assumptions and hypotheses	8
1.5 Research methodology	9
1.5.1 Problem formulation	12
1.5.2 Literature review	12
1.5.3 Model construction	13
1.5.4 System development	14
1.5.5 Assessment	14
1.6 Outline of this thesis	15
2. LITERATURE REVIEW	17
2.1 The bee algorithm (BA)	18
2.1.1 The inspiration of BA	18
2.1.2 The flowchart of BA	20
2.1.3 Advantages and disadvantages	22
2.2 The particle swarm optimization (PSO)	23
2.2.1 The inspiration of PSO	23
2.2.2 The flowchart of PSO	24
2.2.3 Advantages and disadvantages	26
2.3 The construction site layout (CSL)	27
2.3.1 The definition of CSL	27
2.3.2 The significance of CSL	29
2.3.3 The modeling of CSL	31
2.4 Research works on CSL problems	33
2.4.1 Artificial intelligence (AI) on CSL problems	34
2.4.2 Evolutional computing (EC) algorithms on CSL problems	36
2.5 Object-oriented system development (OOSD)	39
2.5.1 The concept of OO	39
2.5.2 Advantages and disadvantages	41
3. PARTICLE BEE ALGORITHM INFERENCE MODEL	45
3.1 Model architecture	45
3.2 Model adaptation process	47
3.3 Model application process	54
3.3.1 Input stage	55
3.3.2 Design stage	57
3.3.3 Evaluation & selection stage	58
3.3.4 Output stage	59
4. SYSTEM DEVELOPMENT	61
4.1 Planning phase	62
4.2 Building phase	63
4.2.1 System analysis	63
4.2.2 System design	68
4.2.3 System construction	73
4.2.4 System testing	73
4.3 Deploying phase	74
5. BENCHMARK FUNCTIONS APPROXIMATION FOR PERFORMANCE VALIDATION AND SENSITIVITY ANALYSIS	77
5.1 Performance validation	77
5.1.1 Problem statement	77
5.1.2 Modeling of benchmark functions	79
5.1.3 Model application	83
5.1.4 Finding and discussion	85
5.2 Sensitivity analysis	86
5.2.1 Problem statement	86
5.2.2 Modeling of benchmark functions for sensitivity analysis	88
5.2.3 Model sensitivity analysis application	89
5.2.4 Finding and discussion	96
6. FLOOR LEVEL CONSTRUCTION SITE LAYOUT CASE STUDY	97
6.1 Problem statement	97
6.2 Modeling of floor level construction site layout case study	100
6.3 Model application	100
6.4 Finding and discussion	105
7. SITE LEVEL CONSTRUCTION SITE LAYOUT CASE STUDY	107
7.1 Site level construction site layout case study I	108
7.1.1 Problem statement	108
7.1.2 Modeling of site level construction site layout case study I	110
7.1.3 Model application	111
7.1.4 Finding and discussion	113
7.2 Site level construction site layout case study II	114
7.2.1 Problem statement	114
7.2.2 Modeling of site level construction site layout case study II	117
7.2.3 Model application	117
7.2.4 Finding and discussion	120
8. CONCLUSIONS AND RECOMMENDATIONS	123
8.1 The results of this study proposed	123
8.2 The advantages of this study proposed	125
8.3 The limitations of the proposed PBAIS	125
8.4 Future research works	126
BIBLIOGRAPHY	127
APPENDIX A (Matlab code of algorithms)	137
A.1 Matlab code of bee algorithm	137
A.2 Matlab code of particle swarm algorithm	140
A.3 Matlab code of particle bee algorithm	142
A.4 Matlab code of benchmark functions drawing	151
APPENDIX B (Matlab code of case studies)	179
B.1 Matlab code of benchmark functions	179
B.2 Matlab code of facility layout case study	184
B.3 Matlab code of construction site layout case study I	192
B.4 Matlab code of construction site layout case study II	194
CURRICULUM VITAE	199

LIST OF FIGURES
Figure 1-1 Research flow chart	10
Figure 1-2 Detailed research flow chart	11
Figure 2-1 The honey bee swarm components	19
Figure 2-2 The honey bee waggle dance	19
Figure 2-3 The bee algorithm flowchart	20
Figure 2-4 The mechanism of PSO	24
Figure 2-5 The particle swarm optimization flowchart	26
Figure 2-6 Schematic drawing of a layout design procedure	28
Figure 2-7 Permutation matrix with five facilities	33
Figure 2-8 The incremental and iterative method	41
Figure 3-1 Particle bee algorithm architecture	46
Figure 3-2 The behavior of NW in PSO	48
Figure 3-3 The Rastrigin function	50
Figure 3-4 Optimization procedure behavior in each iteration (a)	51
Figure 3-5 Optimization procedure behavior in each iteration (b)	51
Figure 3-6 The structure of the proposed PBAIM	54
Figure 3-7 The flowchart of the proposed PBAIM	55
Figure 3-8 Workflow of the evaluation & selection stage in the proposed PBAIM	58
Figure 4-1 The PBAIS OO system development process	62
Figure 4-2 System usage process	64
Figure 4-3 System concepts	66
Figure 4-4 System behaviors: (a) Sequence diagram of handle record use case; (b) Sequence diagram of search optimal solution use case; (c) Sequence diagram of infer possible results use case	67
Figure 4-5 Two-tiered object-oriented PBAIS architecture	69
Figure 4-6 Object interactions: (a) Collaboration diagram of handle record use case; (b) Collaboration diagram of search optimal solution use case; (c) Collaboration diagram of calculate optimum solution and fitness value use case	70
Figure 4-7 System software classes: (a) Class diagram: management concept; (b) Class diagram: adaptation concept; (c) Class diagram: inference concept	71
Figure 4-7 System software classes: (a) Class diagram: management concept; (b) Class diagram: adaptation concept; (c) Class diagram: inference concept (Continue)	72
Figure 4-8 System application process	76
Figure 5-1 Beale, Easom, Matyas and Bohachevsky1 numerical functions	80
Figure 5-2 Booth, Michalewicz2, Schaffer and Six HCB numerical functions	81
Figure 5-3 Boachevsky2, Boachevsky3, Shubert and Colville numerical functions	81
Figure 5-4 Michalewicz5, Zakharov, Michalewicz10 and Step numerical functions	82
Figure 5-5 Sphere, SumSquares, Quartic and Schwefel 2.22 numerical functions	82
Figure 5-6 Schwefel 1.2, Rosenbrock, Dixon-Price and Rastrigin numerical functions	83
Figure 5-7 Griewank and Ackley numerical functions	83
Figure 5-8 Schaffer function	87
Figure 5-9 Sphere function	87
Figure 5-10 Griewank function	88
Figure 5-11 Rastrigin function	88
Figure 5-12 Rosenbrock function	88
Figure 5-13 Evolution of mean best values for Schaffer function on different colony sizes	91
Figure 5-14 Evolution of mean best values for Sphere function on different colony sizes	91
Figure 5-15 Evolution of mean best values for Griewank function on different colony sizes	92
Figure 5-16 Evolution of mean best values for Rastrigin function on different colony sizes	92
Figure 5-17 Evolution of mean best values for Rosenbrock function on different colony sizes	93
Figure 5-18 Mean best values for Rastrigin function on 75 colony size and different PSO iteration sizes	94
Figure 5-19 Mean best values for Rastrigin function on 100 colony size and different PSO iteration sizes	95
Figure 5-20 Mean best values for Rosenbrock function on 75 colony size and different PSO iteration sizes	95
Figure 5-21 Mean best values for Rosenbrock function on 100 colony size and different PSO iteration sizes	96
Figure 6-1 Plane of the standard floor of hospital	98
Figure 6-2 Site neighboring index matrix for facilities on a floor	98
Figure 6-3 Evolution of mean best values for architecture layout problem	102
Figure 6-4 PBA best layout design	104
Figure 6-5 Yeh best layout design	104
Figure 7-1 A hypothetical of site layout	108
Figure 7-2 Evolution of mean best values for case I problem	112
Figure 7-3 PBA best layout design	113
Figure 7-4 Lam best layout design	113
Figure 7-5 A hypothetical of site layout	114
Figure 7-6 Evolution of mean best values for case II problem	118
Figure 7-7 PBA best layout design	119
Figure 7-8 PSO best layout design	120
Figure 7-9 Love best layout design	120

LIST OF TABLES
Table 3-1 Validation of the performance of NW and SPU in PBA	53
Table 3-2 Example of facility relationship chart	56
Table 3-3 The method to represent site grid	57
Table 3-4 Example of distance between facilities	58
Table 4-1 Type of function in the system	63
Table 4-2 High-Level use case of the PBAIS	65
Table 4-3 System operation contrast	67
Table 5-1 Numerical benchmark functions	78
Table 5-2 Parameter values used in the experiments	79
Table 5-3 The results obtained by DE, EA, PSO, BA and PBA	84
Table 5-4 Algorithm performance comparison on benchmark functions	85
Table 5-5 Algorithm performance cross-matching on benchmark functions	85
Table 5-6 Numerical benchmark functions	86
Table 5-7 Parameter values used in the experiments	89
Table 5-8 The results obtained by DE, EA, PSO, BA and PBA	90
Table 5-9 Mean of function values obtained by PBA under different colony sizes	90
Table 5-10 Mean of function values obtained by PBA under different PSO iteration sizes	94
Table 6-1 Design preferences of the hospital layout problem	99
Table 6-2 Parameter values used in the experiments	101
Table 6-3 The result of three algorithms	101
Table 6-4 Results of layout facility score	105
Table 7-1 Facilities used on the case study	109
Table 7-2 Travel distance between facilities	109
Table 7-3 Frequencies of trips between facilities	110
Table 7-4 Parameter values used in the experiments	111
Table 7-5 The result of three algorithms	111
Table 7-6 Facilities used on the case study	115
Table 7-7 Travel distance between facilities	116
Table 7-8 Frequencies of trips between facilities	116
Table 7-9 The result of three algorithms	118

                                

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