Meta-heuristic and evolutionary algorithms for engineering optimization. (2017)

• Record Type: Book
• Further Information: Note: Omid Bozorg-Haddad, Mohammad Solgi, Hugo A Loaiciga.
• Authors: Bozorg-Haddad, Omid, 1974-; Solgi, Mohammad, 1989-; Loaiciga, Hugo A
• Contents: 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 Summar
• Edition: 1st
• Publisher Details: Hoboken, New Jersey : John Wiley & Sons, Inc
• Publication Date: 2017
• Extent: 1 online resource
• Subjects: 620.0042015196; Mathematical optimization; Engineering design -- Mathematics
• Languages: English
• ISBNs: 9781119387060; 9781119387077
• Related ISBNs: 9781119386995
• Notes: Note: Includes bibliographical references and index.; Note: Description based on CIP data; resource not viewed.
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