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Produktbild: Optimization and Machine Learning

Optimization and Machine Learning Optimization for Machine Learning and Machine Learning for Optimization

197,99 €

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Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

19.04.2022

Herausgeber

Rachid Chelouah + weitere

Verlag

John Wiley & Sons

Seitenzahl

256

Maße (L/B/H)

24/16,1/1,8 cm

Gewicht

548 g

Auflage

1. Auflage

Sprache

Englisch

ISBN

978-1-78945-071-2

Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

19.04.2022

Herausgeber

Verlag

John Wiley & Sons

Seitenzahl

256

Maße (L/B/H)

24/16,1/1,8 cm

Gewicht

548 g

Auflage

1. Auflage

Sprache

Englisch

ISBN

978-1-78945-071-2

Herstelleradresse

Libri GmbH
Europaallee 1
36244 Bad Hersfeld
DE

Email: GPSR Kontakt

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  • Produktbild: Optimization and Machine Learning
  • 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
    &nb