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Omid Bozorg-Haddad
(2017)
John Wiley & Sons
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Meta-Heuristic and Evolutionary Algorithms for Engineering Optimization
Omid Bozorg-Haddad, Mohammad Solgi og Hugo A. Loáiciga
(2017)
Sprog: Engelsk
John Wiley & Sons, Limited
1.513,00 kr.
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Detaljer om varen
- 1. Udgave
- Vital Source searchable e-book (Reflowable pages)
- Udgiver: John Wiley & Sons (September 2017)
- ISBN: 9781119387060
A detailed review of a wide range of meta-heuristic and evolutionary algorithms in a systematic manner and how they relate to engineering optimization problems This book introduces the main metaheuristic algorithms and their applications in optimization. It describes 20 leading meta-heuristic and evolutionary algorithms and presents discussions and assessments of their performance in solving optimization problems from several fields of engineering. The book features clear and concise principles and presents detailed descriptions of leading methods such as the pattern search (PS) algorithm, the genetic algorithm (GA), the simulated annealing (SA) algorithm, the Tabu search (TS) algorithm, the ant colony optimization (ACO), and the particle swarm optimization (PSO) technique. Chapter 1 of Meta-heuristic and Evolutionary Algorithms for Engineering Optimization provides an overview of optimization and defines it by presenting examples of optimization problems in different engineering domains. Chapter 2 presents an introduction to meta-heuristic and evolutionary algorithms and links them to engineering problems. Chapters 3 to 22 are each devoted to a separate algorithm— and they each start with a brief literature review of the development of the algorithm, and its applications to engineering problems. The principles, steps, and execution of the algorithms are described in detail, and a pseudo code of the algorithm is presented, which serves as a guideline for coding the algorithm to solve specific applications. This book: Introduces state-of-the-art metaheuristic algorithms and their applications to engineering optimization; Fills a gap in the current literature by compiling and explaining the various meta-heuristic and evolutionary algorithms in a clear and systematic manner; Provides a step-by-step presentation of each algorithm and guidelines for practical implementation and coding of algorithms; Discusses and assesses the performance of metaheuristic algorithms in multiple problems from many fields of engineering; Relates optimization algorithms to engineering problems employing a unifying approach. Meta-heuristic and Evolutionary Algorithms for Engineering Optimization is a reference intended for students, engineers, researchers, and instructors in the fields of industrial engineering, operations research, optimization/mathematics, engineering optimization, and computer science. OMID BOZORG-HADDAD, PhD, is Professor in the Department of Irrigation and Reclamation Engineering at the University of Tehran, Iran. MOHAMMAD SOLGI, M.Sc., is Teacher Assistant for M.Sc. courses at the University of Tehran, Iran. HUGO A. LOÁICIGA, PhD, is Professor in the Department of Geography at the University of California, Santa Barbara, United States of America.
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Detaljer om varen
- Hardback: 304 sider
- Udgiver: John Wiley & Sons, Limited (December 2017)
- Forfattere: Omid Bozorg-Haddad, Mohammad Solgi og Hugo A. Loáiciga
- ISBN: 9781119386995
A detailed review of a wide range of meta-heuristic and evolutionary algorithms in a systematic manner and how they relate to engineering optimization problems
This book introduces the main metaheuristic algorithms and their applications in optimization. It describes 20 leading meta-heuristic and evolutionary algorithms and presents discussions and assessments of their performance in solving optimization problems from several fields of engineering. The book features clear and concise principles and presents detailed descriptions of leading methods such as the pattern search (PS) algorithm, the genetic algorithm (GA), the simulated annealing (SA) algorithm, the Tabu search (TS) algorithm, the ant colony optimization (ACO), and the particle swarm optimization (PSO) technique.
Chapter 1 of Meta-heuristic and Evolutionary Algorithms for Engineering Optimization provides an overview of optimization and defines it by presenting examples of optimization problems in different engineering domains. Chapter 2 presents an introduction to meta-heuristic and evolutionary algorithms and links them to engineering problems. Chapters 3 to 22 are each devoted to a separate algorithm-- and they each start with a brief literature review of the development of the algorithm, and its applications to engineering problems. The principles, steps, and execution of the algorithms are described in detail, and a pseudo code of the algorithm is presented, which serves as a guideline for coding the algorithm to solve specific applications. This book:
- Introduces state-of-the-art metaheuristic algorithms and their applications to engineering optimization;
- Fills a gap in the current literature by compiling and explaining the various meta-heuristic and evolutionary algorithms in a clear and systematic manner;
- Provides a step-by-step presentation of each algorithm and guidelines for practical implementation and coding of algorithms;
- Discusses and assesses the performance of metaheuristic algorithms in multiple problems from many fields of engineering;
- Relates optimization algorithms to engineering problems employing a unifying approach.
Meta-heuristic and Evolutionary Algorithms for Engineering Optimization is a reference intended for students, engineers, researchers, and instructors in the fields of industrial engineering, operations research, optimization/mathematics, engineering optimization, and computer science.
OMID BOZORG-HADDAD, PhD, is Professor in the Department of Irrigation and Reclamation Engineering at the University of Tehran, Iran.
MOHAMMAD SOLGI, M.Sc., is Teacher Assistant for M.Sc. courses at the University of Tehran, Iran.
HUGO A. LOÁICIGA, PhD, is Professor in the Department of Geography at the University of California, Santa Barbara, United States of America.
Preface xv About the Authors xvii List of Figures xix 1 Overview of Optimization 1 Summary 1
1.1 Optimization 1
1.1.1 Objective Function 2
1.1.2 Decision Variables 2
1.1.3 Solutions of an Optimization Problem 3
1.1.4 Decision Space 3
1.1.5 Constraints or Restrictions 3
1.1.6 State Variables 3
1.1.7 Local and Global Optima 4
1.1.8 Near-Optimal Solutions 5
1.1.9 Simulation 6
1.2 Examples of the Formulation of Various Engineering Optimization Problems 7
1.2.1 Mechanical Design 7
1.2.2 Structural Design 9
1.2.3 Electrical Engineering Optimization 10
1.2.4 Water Resources Optimization 11
1.2.5 Calibration of Hydrologic Models 13
1.3 Conclusion 15 2 Introduction to Meta - Heuristic and Evolutionary Algorithms 17 Summary 17
2.1 Searching the Decision Space for Optimal Solutions 17
2.2 Definition of Terms of Meta-Heuristic and Evolutionary Algorithms 21
2.2.1 Initial State 21
2.2.2 Iterations 21
2.2.3 Final State 21
2.2.4 Initial Data (Information) 21
2.2.5 Decision Variables 22
2.2.6 State Variables 23
2.2.7 Objective Function 23
2.2.8 Simulation Model 24
2.2.9 Constraints 24
2.2.10 Fitness Function 24
2.3 Principles of Meta-Heuristic and Evolutionary Algorithms 25
2.4 Classification of Meta-Heuristic and Evolutionary Algorithms 27
2.4.1 Nature-Inspired and Non-Nature-Inspired Algorithms 27
2.4.2 Population-Based and Single-Point Search Algorithms 28
2.4.3 Memory-Based and Memory-Less Algorithms 28
2.5 Meta-Heuristic and Evolutionary Algorithms in Discrete or Continuous Domains 28
2.6 Generating Random Values of the Decision Variables 29
2.7 Dealing with Constraints 29
2.7.1 Removal Method 30
2.7.2 Refinement Method 30
2.7.3 Penalty Functions 31
2.8 Fitness Function 33
2.9 Selection of Solutions in Each Iteration 33
2.10 Generating New Solutions 34
2.11 The Best Solution in Each Algorithmic Iteration 35
2.12 Termination Criteria 35
2.13 General Algorithm 36
2.14 Performance Evaluation of Meta-Heuristic and Evolutionary Algorithms 36
2.15 Search Strategies 39
2.16 Conclusion 41 References 41 3 Pattern Search 43 Summary 43
3.1 Introduction 43
3.2 Pattern Search (PS) Fundamentals 44
3.3 Generating an Initial Solution 47
3.4 Generating Trial Solutions 47
3.4.1 Exploratory Move 47
3.4.2 Pattern Move 49
3.5 Updating the Mesh Size 50
3.6 Termination Criteria 50
3.7 User-Defined Parameters of the PS 51
3.8 Pseudocode of the PS 51
3.9 Conclusion 52 References 52 4 Genetic Algorithm 53 Summary 53
4.1 Introduction 53
4.2 Mapping the Genetic Algorithm (GA) to Natural Evolution 54
4.3 Creating an Initial Population 56
4.4 Selection of Parents to Create a New Generation 56
4.4.1 Proportionate Selection 57
4.4.2 Ranking Selection 58
4.4.3 Tournament Selection 59
4.5 Population Diversity and Selective Pressure 59
4.6 Reproduction 59
4.6.1 Crossover 60
4.6.2 Mutation 62
4.7 Termination Criteria 63
4.8 User- Defined Parameters of the GA 63
4.9 Pseudocode of the GA 64
4.10 Conclusion 65 References 65 5 Simulated Annealing 69 Summary 69
5.1 Introduction 69
5.2 Mapping the Simulated Annealing (SA) Algorithm to the Physical Annealing Process 70
5.3 Generating an Initial State 72
5.4 Generating a New State 72
5.5 Acceptance Function 74
5.6 Thermal Equilibrium 75
5.7 Temperature Reduction 75
5.8 Termination Criteria 76
5.9 User- Defined Parameters of the SA 76
5.10 Pseudocode of the SA 77
5.11 Conclusion 77 References 77 6 Tabu Search 79 Summary 79
6.1 Introduction 79
6.2 Tabu Search (TS) Foundation 80
6.3 Generating an Initial Searching Point 82
6.4 Neighboring Points 82
6.5 Tabu Lists 84
6.6 Updating the Tabu List 84
6.7 Attributive Memory 85
6.7.1 Frequency-Based Memory 85
6.7.2 Recency-Based Memory 85
6.8 Aspiration Criteria 87
6.9 Intensification and Diversification Strategies 87
6.10 Termination Criteria 87
6.11 User- Defined Parameters of the TS 87
6.12 Pseudocode of the TS 88
6.13 Conclusion 89 References 89 7 Ant Colony Optimization 91 Summary 91
7.1 Introduction 91
7.2 Mapping Ant Colony Optimization (ACO) to Ants'' Foraging Behavior 92
7.3 Creating an Initial Population 94
7.4 Allocating Pheromone to the Decision Space 96
7.5 Generation of New Solutions 98
7.6 Termination Criteria 99
7.7 User- Defined Parameters of the ACO 99
7.8 Pseudocode of the ACO 100
7.9 Conclusion 100 References 101 8 Particle Swarm Optimization 103 Summary 103
8.1 Introduction 103
8.2 Mapping Particle Swarm Optimization (PSO) to the Social Behavior of Some Animals 104
8.3 Creating an Initial Population of Particles 107
8.4 The Individual and Global Best Positions 107
8.5 Velocities of Particles 109
8.6 Updating the Positions of Particles 110
8.7 Termination Criteria 110
8.8 User- Defined Parameters of the PSO 110
8.9 Pseudocode of the PSO 111
8.10 Conclusion 112 References 112 9 Differential Evolution 115 Summary 115
9.1 Introduction 115
9.2 Differential Evolution (DE) Fundamentals 116
9.3 Creating an Initial Population 118
9.4 Generating Trial Solutions 119
9.4.1 Mutation 119
9.4.2 Crossover 119
9.5 Greedy Criteria 120
9.6 Termination Criteria 120
9.7 User-Defined Parameters of the DE 120
9.8 Pseudocode of the DE 121
9.9 Conclusion 121 References 121 10 Harmony Search 123 Summary 123
10.1 Introduction 123
10.2 Inspiration of the Harmony Search (HS) 124
10.3 Initializing the Harmony Memory 125
10.4 Generating New Harmonies (Solutions) 127
10.4.1 Memory Strategy 127
10.4.2 Random Selection 128
10.4.3 Pitch Adjustment 129
10.5 Updating the Harmony Memory 129
10.6 Termination Criteria 130
10.7 User- Defined Parameters of the HS 130
10.8 Pseudocode of the HS 130
10.9 Conclusion 131 References 131 11 Shuffled Frog - Leaping Algorithm 133 Summary 133
11.1 Introduction 133
11.2 Mapping Memetic Evolution of Frogs to the Shuffled Frog Leaping Algorithm (SFLA) 134
11.3 Creating an Initial Population 137
11.4 Classifying Frogs into Memeplexes 137
11.5 Frog Leaping 138
11.6 Shuffling Process 140
11.7 Termination Criteria 141
11.8 User-Defined Parameters of the SFLA 141
11.9 Pseudocode of the SFLA 141
11.10 Conclusion 142 References 142 12 Honey - Bee Mating Optimization 145 Summary 145
12.1 Introduction 145
12.2 Mapping Honey-Bee Mating Optimization (HBMO) to the Honey- Bee Colony Structure 146
12.3 Creating an Initial Population 148
12.4 The Queen 150
12.5 Drone Selection 150
12.5.1 Mating Flights 151
12.5.2 Trial Solutions 152
12.6 Brood (New Solution) Production 152
12.7 Improving Broods (New Solutions) by Workers 155
12.8 Termination Criteria 156
12.9 User-Defined Parameters of the HBMO 156
12.10 Pseudocode of the HBMO 156
12.11 Conclusion 158 References 158 13 Invasive Weed Optimization 163 Summary 163
13.1 Introduction 163
13.2 Mapping Invasive Weed Optimization (IWO) to Weeds'' Biology 164
13.3 Creating an Initial Population 167
13.4 Reproduction 167
13.5 The Spread of Seeds 168
13.6 Eliminating Weeds with Low Fitness 169
13.7 Termination Criteria 170
13.8 User- Defined Parameters of the IWO 170
13.9 Pseudocode of the IWO 170
13.10 Conclusion 171 References 171 14 Central Force Optimization 175 Summary 175
14.1 Introduction 175
14.2 Mapping Central Force Optimization (CFO) to Newtons Gravitational Law 176
14.3 Initializing the Position of Probes 177
14.4 Calculation of Accelerations 180
14.5 Movement of Probes 181
14.6 Modification of Deviated Probes 181
14.7 Termination Criteria 182
14.8 User-Defined Parameters of the CFO 182
14.9 Pseudocode of the CFO 183
14.10 Conclusion 183 References 183 15 Biogeography - Based Optimization 185 Summary 185
15.1 Introduction 185
15.2 Mapping Biogeography-Based Optimization (BBO) to Biogeography Concepts 186
15.3 Creating an Initial Population 188
15.4 Migration Process 189
15.5 Mutation 191
15.6 Termination Criteria 192
15.7 User- Defined Parameters of the BBO 192
15.8 Pseudocode of the BBO 193
15.9 Conclusion 193 References 194 16 Firefly Algorithm 195 Summary 195
16.1 Introduction 195
16.2 Mapping the Firefly Algorithm (FA) to the Flashing Characteristics of Fireflies 196<
1.1 Optimization 1
1.1.1 Objective Function 2
1.1.2 Decision Variables 2
1.1.3 Solutions of an Optimization Problem 3
1.1.4 Decision Space 3
1.1.5 Constraints or Restrictions 3
1.1.6 State Variables 3
1.1.7 Local and Global Optima 4
1.1.8 Near-Optimal Solutions 5
1.1.9 Simulation 6
1.2 Examples of the Formulation of Various Engineering Optimization Problems 7
1.2.1 Mechanical Design 7
1.2.2 Structural Design 9
1.2.3 Electrical Engineering Optimization 10
1.2.4 Water Resources Optimization 11
1.2.5 Calibration of Hydrologic Models 13
1.3 Conclusion 15 2 Introduction to Meta - Heuristic and Evolutionary Algorithms 17 Summary 17
2.1 Searching the Decision Space for Optimal Solutions 17
2.2 Definition of Terms of Meta-Heuristic and Evolutionary Algorithms 21
2.2.1 Initial State 21
2.2.2 Iterations 21
2.2.3 Final State 21
2.2.4 Initial Data (Information) 21
2.2.5 Decision Variables 22
2.2.6 State Variables 23
2.2.7 Objective Function 23
2.2.8 Simulation Model 24
2.2.9 Constraints 24
2.2.10 Fitness Function 24
2.3 Principles of Meta-Heuristic and Evolutionary Algorithms 25
2.4 Classification of Meta-Heuristic and Evolutionary Algorithms 27
2.4.1 Nature-Inspired and Non-Nature-Inspired Algorithms 27
2.4.2 Population-Based and Single-Point Search Algorithms 28
2.4.3 Memory-Based and Memory-Less Algorithms 28
2.5 Meta-Heuristic and Evolutionary Algorithms in Discrete or Continuous Domains 28
2.6 Generating Random Values of the Decision Variables 29
2.7 Dealing with Constraints 29
2.7.1 Removal Method 30
2.7.2 Refinement Method 30
2.7.3 Penalty Functions 31
2.8 Fitness Function 33
2.9 Selection of Solutions in Each Iteration 33
2.10 Generating New Solutions 34
2.11 The Best Solution in Each Algorithmic Iteration 35
2.12 Termination Criteria 35
2.13 General Algorithm 36
2.14 Performance Evaluation of Meta-Heuristic and Evolutionary Algorithms 36
2.15 Search Strategies 39
2.16 Conclusion 41 References 41 3 Pattern Search 43 Summary 43
3.1 Introduction 43
3.2 Pattern Search (PS) Fundamentals 44
3.3 Generating an Initial Solution 47
3.4 Generating Trial Solutions 47
3.4.1 Exploratory Move 47
3.4.2 Pattern Move 49
3.5 Updating the Mesh Size 50
3.6 Termination Criteria 50
3.7 User-Defined Parameters of the PS 51
3.8 Pseudocode of the PS 51
3.9 Conclusion 52 References 52 4 Genetic Algorithm 53 Summary 53
4.1 Introduction 53
4.2 Mapping the Genetic Algorithm (GA) to Natural Evolution 54
4.3 Creating an Initial Population 56
4.4 Selection of Parents to Create a New Generation 56
4.4.1 Proportionate Selection 57
4.4.2 Ranking Selection 58
4.4.3 Tournament Selection 59
4.5 Population Diversity and Selective Pressure 59
4.6 Reproduction 59
4.6.1 Crossover 60
4.6.2 Mutation 62
4.7 Termination Criteria 63
4.8 User- Defined Parameters of the GA 63
4.9 Pseudocode of the GA 64
4.10 Conclusion 65 References 65 5 Simulated Annealing 69 Summary 69
5.1 Introduction 69
5.2 Mapping the Simulated Annealing (SA) Algorithm to the Physical Annealing Process 70
5.3 Generating an Initial State 72
5.4 Generating a New State 72
5.5 Acceptance Function 74
5.6 Thermal Equilibrium 75
5.7 Temperature Reduction 75
5.8 Termination Criteria 76
5.9 User- Defined Parameters of the SA 76
5.10 Pseudocode of the SA 77
5.11 Conclusion 77 References 77 6 Tabu Search 79 Summary 79
6.1 Introduction 79
6.2 Tabu Search (TS) Foundation 80
6.3 Generating an Initial Searching Point 82
6.4 Neighboring Points 82
6.5 Tabu Lists 84
6.6 Updating the Tabu List 84
6.7 Attributive Memory 85
6.7.1 Frequency-Based Memory 85
6.7.2 Recency-Based Memory 85
6.8 Aspiration Criteria 87
6.9 Intensification and Diversification Strategies 87
6.10 Termination Criteria 87
6.11 User- Defined Parameters of the TS 87
6.12 Pseudocode of the TS 88
6.13 Conclusion 89 References 89 7 Ant Colony Optimization 91 Summary 91
7.1 Introduction 91
7.2 Mapping Ant Colony Optimization (ACO) to Ants'' Foraging Behavior 92
7.3 Creating an Initial Population 94
7.4 Allocating Pheromone to the Decision Space 96
7.5 Generation of New Solutions 98
7.6 Termination Criteria 99
7.7 User- Defined Parameters of the ACO 99
7.8 Pseudocode of the ACO 100
7.9 Conclusion 100 References 101 8 Particle Swarm Optimization 103 Summary 103
8.1 Introduction 103
8.2 Mapping Particle Swarm Optimization (PSO) to the Social Behavior of Some Animals 104
8.3 Creating an Initial Population of Particles 107
8.4 The Individual and Global Best Positions 107
8.5 Velocities of Particles 109
8.6 Updating the Positions of Particles 110
8.7 Termination Criteria 110
8.8 User- Defined Parameters of the PSO 110
8.9 Pseudocode of the PSO 111
8.10 Conclusion 112 References 112 9 Differential Evolution 115 Summary 115
9.1 Introduction 115
9.2 Differential Evolution (DE) Fundamentals 116
9.3 Creating an Initial Population 118
9.4 Generating Trial Solutions 119
9.4.1 Mutation 119
9.4.2 Crossover 119
9.5 Greedy Criteria 120
9.6 Termination Criteria 120
9.7 User-Defined Parameters of the DE 120
9.8 Pseudocode of the DE 121
9.9 Conclusion 121 References 121 10 Harmony Search 123 Summary 123
10.1 Introduction 123
10.2 Inspiration of the Harmony Search (HS) 124
10.3 Initializing the Harmony Memory 125
10.4 Generating New Harmonies (Solutions) 127
10.4.1 Memory Strategy 127
10.4.2 Random Selection 128
10.4.3 Pitch Adjustment 129
10.5 Updating the Harmony Memory 129
10.6 Termination Criteria 130
10.7 User- Defined Parameters of the HS 130
10.8 Pseudocode of the HS 130
10.9 Conclusion 131 References 131 11 Shuffled Frog - Leaping Algorithm 133 Summary 133
11.1 Introduction 133
11.2 Mapping Memetic Evolution of Frogs to the Shuffled Frog Leaping Algorithm (SFLA) 134
11.3 Creating an Initial Population 137
11.4 Classifying Frogs into Memeplexes 137
11.5 Frog Leaping 138
11.6 Shuffling Process 140
11.7 Termination Criteria 141
11.8 User-Defined Parameters of the SFLA 141
11.9 Pseudocode of the SFLA 141
11.10 Conclusion 142 References 142 12 Honey - Bee Mating Optimization 145 Summary 145
12.1 Introduction 145
12.2 Mapping Honey-Bee Mating Optimization (HBMO) to the Honey- Bee Colony Structure 146
12.3 Creating an Initial Population 148
12.4 The Queen 150
12.5 Drone Selection 150
12.5.1 Mating Flights 151
12.5.2 Trial Solutions 152
12.6 Brood (New Solution) Production 152
12.7 Improving Broods (New Solutions) by Workers 155
12.8 Termination Criteria 156
12.9 User-Defined Parameters of the HBMO 156
12.10 Pseudocode of the HBMO 156
12.11 Conclusion 158 References 158 13 Invasive Weed Optimization 163 Summary 163
13.1 Introduction 163
13.2 Mapping Invasive Weed Optimization (IWO) to Weeds'' Biology 164
13.3 Creating an Initial Population 167
13.4 Reproduction 167
13.5 The Spread of Seeds 168
13.6 Eliminating Weeds with Low Fitness 169
13.7 Termination Criteria 170
13.8 User- Defined Parameters of the IWO 170
13.9 Pseudocode of the IWO 170
13.10 Conclusion 171 References 171 14 Central Force Optimization 175 Summary 175
14.1 Introduction 175
14.2 Mapping Central Force Optimization (CFO) to Newtons Gravitational Law 176
14.3 Initializing the Position of Probes 177
14.4 Calculation of Accelerations 180
14.5 Movement of Probes 181
14.6 Modification of Deviated Probes 181
14.7 Termination Criteria 182
14.8 User-Defined Parameters of the CFO 182
14.9 Pseudocode of the CFO 183
14.10 Conclusion 183 References 183 15 Biogeography - Based Optimization 185 Summary 185
15.1 Introduction 185
15.2 Mapping Biogeography-Based Optimization (BBO) to Biogeography Concepts 186
15.3 Creating an Initial Population 188
15.4 Migration Process 189
15.5 Mutation 191
15.6 Termination Criteria 192
15.7 User- Defined Parameters of the BBO 192
15.8 Pseudocode of the BBO 193
15.9 Conclusion 193 References 194 16 Firefly Algorithm 195 Summary 195
16.1 Introduction 195
16.2 Mapping the Firefly Algorithm (FA) to the Flashing Characteristics of Fireflies 196<
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