Rachid Chelouah, Patrick Siarry

Optimization and Machine Learning

Optimization for Machine Learning and Machine Learning for Optimization

• Gebundenes Buch

Produktbeschreibung

Machine learning and optimization techniques are revolutionizing our world. Other types of information technology have not progressed as rapidly in recent years, in terms of real impact. The aim of this book is to present some of the innovative techniques in the field of optimization and machine learning, and to demonstrate how to apply them in the fields of engineering.

Optimization and Machine Learning presents modern advances in the selection, configuration and engineering of algorithms that rely on machine learning and optimization. The first part of the book is dedicated to applications where optimization plays a major role, and the second part describes and implements several applications that are mainly based on machine learning techniques. The methods addressed in these chapters are compared against their competitors, and their effectiveness in their chosen field of application is illustrated.

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Produktdetails

• Verlag: Wiley & Sons / Wiley-ISTE
• Artikelnr. des Verlages: 1W789450710
• 1. Auflage
• Seitenzahl: 256
• Erscheinungstermin: 19. April 2022
• Englisch
• Abmessung: 240mm x 161mm x 18mm
• Gewicht: 518g
• ISBN-13: 9781789450712
• ISBN-10: 1789450713
• Artikelnr.: 63400176

Autorenporträt

Rachid Chelouah has a PhD and a Doctorate of Sciences (Habilitation) from CY Cergy Paris University, France. His main research interests are data science optimization and artificial intelligence methods and their applications in various fields of IT engineering, health, energy and security. Patrick Siarry is a Professor in automatics and informatics at Paris-East Creteil University, France. His main research interests are the design of stochastic global optimization heuristics and their applications in various engineering fields. He has coordinated several books in the field of optimization.

Inhaltsangabe

Introduction xi
Rachid CHELOUAH

Part 1 Optimization 1

Chapter 1 Vehicle Routing Problems with Loading Constraints: An Overview of Variants and Solution Methods 3
Ines SBAI and Saoussen KRICHEN

1.1 Introduction 3

1.2 The capacitated vehicle routing problem with two-dimensional loading constraints 5

1.2.1 Solution methods 6

1.2.2 Problem description 8

1.2.3 The 2L-CVRP variants 9

1.2.4 Computational analysis 10

1.3 The capacitated vehicle routing problem with three-dimensional loading constraints 11

1.3.1 Solution methods 11

1.3.2 Problem description 13

1.3.3 3L-CVRP variants 14

1.3.4 Computational analysis 16

1.4 Perspectives on future research 18

1.5 References 18

Chapter 2 MAS-aware Approach for QoS-based IoT Workflow Scheduling in Fog-Cloud Computing 25
Marwa MOKNI and Sonia YASSA

2.1 Introduction 26

2.2 Related works 27

2.3 Problem formulation 29

2.3.1 IoT-workflow modeling 31

2.3.2 Resources modeling 31

2.3.3 QoS-based workflow scheduling modeling 31

2.4 MAS-GA-based approach for IoT workflow scheduling 33

2.4.1 Architecture model 33

2.4.2 Multi-agent system model 34

2.4.3 MAS-based workflow scheduling process 35

2.5 GA-based workflow scheduling plan 38

2.5.1 Solution encoding 39

2.5.2 Fitness function 41

2.5.3 Mutation operator 41

2.6 Experimental study and analysis of the results 43

2.6.1 Experimental results 45

2.7 Conclusion 51

2.8 References 51

Chapter 3 Solving Feature Selection Problems Built on Population-based Metaheuristic Algorithms 55
Mohamed SASSI

3.1 Introduction 56

3.2 Algorithm inspiration 57

3.2.1 Wolf pack hierarchy 57

3.2.2 The four phases of pack hunting 58

3.3 Mathematical modeling 59

3.3.1 Pack hierarchy 59

3.3.2 Four phases of hunt modeling 61

3.3.3 Research phase - exploration 64

3.3.4 Attack phase - exploitation 65

3.3.5 Grey wolf optimization algorithm pseudocode 66

3.4 Theoretical fundamentals of feature selection 67

3.4.1 Feature selection definition 67

3.4.2 Feature selection methods 68

3.4.3 Filter method 68

3.4.4 Wrapper method 69

3.4.5 Binary feature selection movement 69

3.4.6 Benefits of feature selection for machine learning classification algorithms 70

3.5 Mathematical modeling of the feature selection optimization problem 70

3.5.1 Optimization problem definition 71

3.5.2 Binary discrete search space 71

3.5.3 Objective functions for the feature selection 72

3.6 Adaptation of metaheuristics for optimization in a binary search space 76

3.6.1 Module M1 77

3.6.2 Module M2 78

3.7 Adaptation of the grey wolf algorithm to feature selection in a binary search space 81

3.7.1 First algorithm bGWO1 81

3.7.2 Second algorithm bGWO2 83

3.7.3 Algorithm 2: first approach of the binary GWO 84

3.7.4 Algorithm 3: second approach of the binary GWO 85

3.8 Experimental implementation of bGWO1 and bGWO2 and discussion 86

3.9 Conclusion 87

3.10 References 88

Chapter 4 Solving the Mixed-model Assembly Line Balancing Problem by using a Hybrid Reactive Greedy Randomized Adaptive Search Procedure 91
Belkharroubi LAKHDAR and Khadidja YAHYAOUI

4.1 Introduction 92

4.2 Related works from the literature 95
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