Omid Bozorg-Haddad, Mohammad Solgi, Hugo A. Loáiciga

Meta-heuristic and Evolutionary Algorithms for Engineering Optimization (eBook, ePUB)

• Format: ePub

Produktbeschreibung

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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Produktdetails

• Verlag: John Wiley & Sons
• Seitenzahl: 304
• Erscheinungstermin: 5. September 2017
• Englisch
• ISBN-13: 9781119387060
• Artikelnr.: 52560643

Autorenporträt

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.

Inhaltsangabe

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

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