Meta-heuristic and Evolutionary Algorithms for Engineering Optimization

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.

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. Lo??iciga, 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

16.3 Creating an Initial Population 198

16.4 Attractiveness 199

16.5 Distance and Movement 199

16.6 Termination Criteria 200

16.7 User-Defined Parameters of the FA 200

16.8 Pseudocode of the FA 201

16.9 Conclusion 201

References 201

17 Gravity Search Algorithm 203

Summary 203

17.1 Introduction 203

17.2 Mapping the Gravity Search Algorithm (GSA) to the Law of Gravity 204

17.3 Creating an Initial Population 205

17.4 Evaluation of Particle Masses 207

17.5 UpdatingVelocities and Positions 207

17.6 Updating Newton’s Gravitational Factor 208

17.7 Termination Criteria 209

17.8 User- Defined Parameters of the GSA 209

17.9 Pseudocode of the GSA 209

17.10 Conclusion 210

References 210

18 Bat Algorithm 213

Summary 213

18.1 Introduction 213

18.2 Mapping the Bat Algorithm (BA) to the Behavior of Microbats 214

18.3 Creating an Initial Population 215

18.4 Movement of Virtual Bats 217

18.5 Local Search and Random Flying 218

18.6 Loudness and Pulse Emission 218

18.7 Termination Criteria 219

18.8 User-Defined Parameters of the BA 219

18.9 Pseudocode of the BA 219

18.10 Conclusion 220

References 220

19 Plant Propagation Algorithm 223

Summary 223

19.1 Introduction 223

19.2 Mapping the Natural Process to the Planet Propagation Algorithm (PPA) 223

19.3 Creating an Initial Population of Plants 226

19.4 Normalizing the Fitness Function 226

19.5 Propagation 227

19.6 Elimination of Extra Solutions 228

19.7 Termination Criteria 228

19.8 User-Defined Parameters of the PPA 228

19.9 Pseudocode of the PPA 229

19.10 Conclusion 230

References 230

20 Water Cycle Algorithm 231

Summary 231

20.1 Introduction 231

20.2 Mapping the Water Cycle Algorithm (WCA) to the Water Cycle 232

20.3 Creating an Initial Population 233

20.4 Classification of Raindrops 235

20.5 Streams Flowing to the Rivers or Sea 236

20.6 Evaporation 237

20.7 Raining Process 238

20.8 Termination Criteria 239

20.9 User-Defined Parameters of the WCA 239

20.10 Pseudocode of the WCA 239

20.11 Conclusion 240

References 240

21 Symbiotic Organisms Search 241

Summary 241

21.1 Introduction 241

21.2 Mapping Symbiotic Relations to the Symbiotic Organisms Search (SOS) 241

21.3 Creating an Initial Ecosystem 242

21.4 Mutualism 244

21.5 Commensalism 245

21.6 Parasitism 245

21.7 Termination Criteria 246

21.8 Pseudocode of the SOS 246

21.9 Conclusion 247

References 247

22 Comprehensive Evolutionary Algorithm 249

Summary 249

22.1 Introduction 249

22.2 Fundamentals of the Comprehensive Evolutionary Algorithm (CEA) 250

22.3 Generating an Initial Population of Solutions 253

22.4 Selection 253

22.5 Reproduction 255

22.5.1 Crossover Operators 255

22.5.2 Mutation Operators 261

22.6 Roles of Operators 262

22.7 Input Data to the CEA 263

22.8 Termination Criteria 264

22.9 Pseudocode of the CEA 265

22.10 Conclusion 265

References 266

Wiley Series in Operations Research and Management Science 267

Index 269

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