Metaheuristic Algorithms in Industry 4.0 (Advances in Metaheuristics)

Cover
Half Title
Series Page
Title Page
Copyright Page
Table of Contents
Preface
Editors
Contributors
1. A Review on Cyber Physical Systems and Smart Computing: Bibliometric Analysis
	1.1 Introduction
	1.2 Data Selection and Extraction
	1.3 Distribution of Publications along Different Verticals
		1.3.1 Publications Analysed: Year-on-Year Basis
		1.3.2 Research Directions of Publications
		1.3.3 Popular Places
		1.3.4 Productive Organizations and Researchers
	1.4 Analysis along the Collaboration Vertical
		1.4.1 Collaboration Strength amongst the Researchers
		1.4.2 Collaboration Strength of Organizations
		1.4.3 Collaborative Strength of Places
	1.5 Analysis along the Citation Landscape
		1.5.1 The Citation Landscape for Research Papers
		1.5.2 The Citation Landscape for Researchers
		1.5.3 The Citation Landscape for Organizations
		1.5.4 The Citation Landscape for Places
	1.6 Timeline Analysis and Burst Detection
		1.6.1 Timeline Review Analysis
		1.6.2 Keyword Burst Detection
		1.6.3 References Burst Detection
	1.7 Conclusion
	References
2. Design Optimization of Close-Fitting Free-Standing Acoustic Enclosure Using Jaya Algorithm
	2.1 Introduction
	2.2 Insertion Loss
		2.2.1 Mathematical Model for Prediction of Insertion Loss
		2.2.2 Need for Optimization
			2.2.2.1 Effect of Variation in Panel Thickness (h) on IL
			2.2.2.2 Effect of Variation in Source to Panel Distance (d) on IL
			2.2.2.3 Effect of Variation in Internal Damping Coefficient (η) on IL
		2.2.3 Optimization
			2.2.3.1 Formulation of Optimization Problem
			2.2.3.2 Optimization by Jaya Algorithm
			2.2.3.3 Final Dimensions of the Enclosure
	2.3 Experimentation
		2.3.1 Experimental Set-up
		2.3.2 Experimental Procedure
	2.4 Results and Discussion
		2.4.1 Theoretically Predicted Vs Experimentally Obtained Results
	2.5 Conclusions and Future Scope
	References
3. A Metaheuristic Scheme for Secure Control of Cyber-Physical Systems
	3.1 Introduction
	3.2 Setup and Preliminaries
		3.2.1 System Description
		3.2.2 A Moving Target Defense Scheme Using Switching Controllers
		3.2.3 Problem Formulation
	3.3 System Analysis in the Absence of Cyber Attack
	3.4 System Analysis in the Presence of Cyber Attack
		3.4.1 A Detection Scheme for the Presence of Actuator Intrusion
		3.4.2 An MTD Control Scheme to Mitigate Cyber Attack
	3.5 Optimization of the Proposed MTD Control Scheme
		3.5.1 Basic Algorithm of PSO
		3.5.2 PSO-Based LQR Tuning
	3.6 Simulation Example
	3.7 Conclusions and Future Scope
	Acknowledgments
	References
4. Application of Salp Swarm Algorithm to Solve Constrained Optimization Problems with Dynamic Penalty Approach in Real-Life Problems
	4.1 Introduction
	4.2 Framework of Salp Swarm Algorithm
	4.3 Dynamic Penalty Approach
	4.4 Proposed Methodology
	4.5 Design of Experiments
		4.5.1 Selection of Orthogonal Array
		4.5.2 Experimental Data
		4.5.3 Problem Formulation
	4.6 Results and Discussion
	4.7 Conclusion
	References
5. Optimization of Robot Path Planning Using Advanced Optimization Techniques
	Notation
	5.1 Introduction
		5.1.1 Navigational Methodology Used for Mobile Robot Path Planning
			5.1.1.1 Classical Approaches for Mobile Robot Navigation
			5.1.1.2 Reactive Approaches for Mobile Robot Navigation
		5.1.2 Classification of Navigation Strategy
	5.2 Literature Review
	5.3 Optimization Algorithms
		5.3.1 Jaya Algorithm
		5.3.2 Rao Algorithms
	5.4 Applications of the Jaya and Rao Algorithms to Robot Path Planning Optimization
		5.4.1 Case Study 1
			5.4.1.1 Objective Function Formulation for Case Study 1
			5.4.1.2 Obstacle Avoidance Behavior for Case Study 1
			5.4.1.3 Goal Searching Behavior for Case Study 1
			5.4.1.4 Case Study 1: Example 1
			5.4.1.5 Case Study 1: Example 2
		5.4.2 Case Study 2
			5.4.2.1 Objective Function Formulation for Case Study 2
			5.4.2.2 Obstacle Avoidance Behavior for Case Study 2
			5.4.2.3 Goal Searching Behavior for Case Study 2
			5.4.2.4 Case Study 2: Example 1
			5.4.2.5 Case Study 2: Example 2
			5.4.2.6 Case Study 2: Example 3
			5.4.2.7 Case Study 2: Example 4
			5.4.2.8 Case Study 2: Example 5
		5.4.3 Case Study 3
			5.4.3.1 Objective Function Formulation for Case Study 3
			5.4.3.2 Obstacle Avoidance Behavior for Case Study 3
			5.4.3.3 Target-Seeking Behavior for Case Study 3
			5.4.3.4 Case Study 3: Example 1
			5.4.3.5 Case Study 3: Example 2
			5.4.3.6 Case Study 3: Example 3
		5.4.4 Case Study 4
			5.4.4.1 Objective Function Formulation for Case Study 4
			5.4.4.2 Case Study 4: Example 1
			5.4.4.3 Case Study 4: Example 2
	5.5 Conclusions
	References
6. Semi-Empirical Modeling and Jaya Optimization of White Layer Thickness during Electrical Discharge Machining of NiTi Alloy
	Abbreviations
	6.1 Introduction
		6.1.1 Research Novelty
	6.2 Method and Material
		6.2.1 Experimental Details
		6.2.2 Empirical Modeling
		6.2.3 Jaya Optimization
		6.2.4 Convergence Analysis for WLT
	6.3 Results and Discussions
		6.3.1 Comparative Analysis of WLT
		6.3.2 WLT Evaluation Using ImageJ Software
		6.3.3 Optimum Parameter Setting Using Jaya Technique
	6.4 Conclusions
	References
7. Analysis of Convolution Neural Network Architectures and Their Applications in Industry 4.0
	7.1 Introduction
	7.2 Evolution of Convolution Neural Network Architectures
		7.2.1 LeNet
			7.2.1.1 Architecture Description
			7.2.1.2 Limitations
		7.2.2 AlexNet
			7.2.2.1 Architecture Description
			7.2.2.2 Limitations
		7.2.3 GoogLeNet
			7.2.3.1 Architecture Description
			7.2.3.2 Limitations
		7.2.4 VGG
			7.2.4.1 Architecture Description
			7.2.4.2 Limitations
		7.2.5 ResNet
			7.2.5.1 Architecture Description
			7.2.5.2 Limitations
		7.2.6 R-CNN
			7.2.6.1 Architecture Description
			7.2.6.2 Limitations
		7.2.7 You Only Look Once (YOLO)
			7.2.7.1 Architecture Description
			7.2.7.2 Limitations
		7.2.8 Generative Adversarial Networks (GANs)
			7.2.8.1 Limitations
	7.3 Applications of Convolution Neural Networks in Industry 4.0
		7.3.1 Healthcare Sector
		7.3.2 Automotive Sector
		7.3.3 Fault Detection
	7.4 Conclusion
	References
8. EMD-Based Triaging of Pulmonary Diseases Using Chest Radiographs (X-Rays)
	Abbreviations
	Symbols
	Chapter Organization
	8.1 Introduction
		8.1.1 Motivation for Building This Tool
		8.1.2 Earth Mover’s Distance
		8.1.3 Dataset
		8.1.4 Parameter Settings
	8.2 Results and Discussion
		8.2.1 Conclusion and Anticipated Outcomes
		8.2.2 Anticipated Outcomes
	References
9. Adaptive Neuro Fuzzy Inference System to Predict Material Removal Rate during Cryo-Treated Electric Discharge Machining
	Abbreviations
	9.1 Introduction
	9.2 Materials and Experimental Set-up
	9.3 Results and Discussion
	9.4 Conclusions
	References
10. A Metaheuristic Optimization Algorithm-Based Speed Controller for Brushless DC Motor: Industrial Case Study
	10.1 Introduction
	10.2 Speed Control of Sensorless BLDC Motor Drives
		10.2.1 Mathematical Model of BLDC Motors
		10.2.2 Sensorless Speed Control Scheme of BLDC Motors
			10.2.2.1 Principle of Sensorless Position Detection
			10.2.2.2 Sensorless Speed Control of BLDC Motors
			10.2.2.3 Sensorless Control Strategy
	10.3 Analysis of Metaheuristics Optimization Algorithm-Based Controller
		10.3.1 Methods of Optimal Tuning of Controller
			10.3.1.1 Analysis of Optimization Techniques Based on PID Controller
			10.3.1.2 Analysis of Optimization Techniques Based on FOPID Controller
	10.4 Metaheuristic Optimization Algorithm-Based Controller Tuning
		10.4.1 Controller Design
		10.4.2 Basic Structure of Optimal Tuning of Controller
		10.4.3 Optimization Techniques Based on Controller Tuning for Brushless DC Motor
		10.4.4 Effect of Controller Parameters
		10.4.5 Problem Formulation
	10.5 Results and Discussions
		10.5.1 Simulink Model
		10.5.2 Speed Response under Constant Load Condition
		10.5.3 Speed Response under Varying Load Conditions
		10.5.4 Speed Response under Varying Set Speed Conditions
		10.5.5 Speed Response under Combined Operating Conditions
		10.5.6 Mean, Standard Deviation and Convergence
	10.6 Conclusion and Future Research Direction
	References
11. Predictive Analysis of Cellular Networks: A Survey
	List of Abbreviations
	11.1 Introduction
	11.2 Traffic Characteristics and Aspects of Analysis
		11.2.1 Traffic Characteristics
			11.2.1.1 Self-Similarity
			11.2.1.2 Seasonality
			11.2.1.3 Non-Stationarity
			11.2.1.4 Multifractal
			11.2.1.5 Long-Range Dependency (LRD)
			11.2.1.6 Short-Range Dependency (SRD)
		11.2.2 Aspects of Analysis
	11.3 Overview of Predictive Analysis
		11.3.1 Time-Series Analysis
		11.3.2 CDR Analysis
		11.3.3 Mobility and Location Analysis
	11.4 Time-Series Analysis of Network Traffic
		11.4.1 Stochastic Models
		11.4.2 Research Contribution
	11.5 CDR Analysis
		11.5.1 Predicted Outputs
			11.5.1.1 Mobility Analysis
			11.5.1.2 Anomaly Detection
			11.5.1.3 Social Influence Analysis
			11.5.1.4 Voice Traffic Analysis
		11.5.2 Big Data Analysis of CDR
	11.6 Mobility and Location Analysis
		11.6.1 Predicted Outputs
			11.6.1.1 Moving Direction
			11.6.1.2 Future Locations
			11.6.1.3 User Trajectory
			11.6.1.4 The Next Cell Id
		11.6.2 Mobility Analysis
		11.6.3 Location Analysis
	11.7 Network Analysis for Special Parameters
		11.7.1 Hotspot Detection
		11.7.2 Holiday Traffic Prediction
		11.7.3 Customer Churn Prediction
		11.7.4 Fault Prediction
		11.7.5 Anomaly Detection
	11.8 Predictive Analysis-Enabled Applications
		11.8.1 Resource Allocation
		11.8.2 Handover Management
		11.8.3 Location-Based Services
		11.8.4 Interference Management
		11.8.5 Energy Efficiency
	11.9 Deep Learning in Predictive Analysis of Cellular Networks
		11.9.1 Deep Learning State-of-the-Art
			11.9.1.1 Convolutional Neural Networks
			11.9.1.2 Recurrent Neural Networks
			11.9.1.3 Deep Belief Networks
			11.9.1.4 Autoencoders
			11.9.1.5 Long Short-Term Memory
		11.9.2 Deep Learning-Based Time-Series Analysis
		11.9.3 Deep Learning-Based CDR Analysis
		11.9.4 Deep Learning-Based Mobility & Location Analysis
	11.10 Emerging Intelligent Networks
		11.10.1 Characteristics of SON
			11.10.1.1 Scalability
			11.10.1.2 Stability
			11.10.1.3 Agility
		11.10.2 Classes of SON
			11.10.2.1 Self-Configuration
			11.10.2.2 Self-Optimization
			11.10.2.3 Self-Healing
		11.10.3 Applications of SON
			11.10.3.1 Coverage and Capacity Optimization
			11.10.3.2 Mobility Robustness Optimization
			11.10.3.3 Mobility Load Balancing
			11.10.3.4 RACH Optimization
	11.11 Conclusion
	References
12. Optimization Techniques and Algorithms for Dental Implants – A Comprehensive Review
	12.1 Introduction
		12.1.1 Structural Optimization
		12.1.2 Surface Morphology Optimization
		12.1.3 Material Properties Optimization
	12.2 FEA Aspect of Optimization Techniques
	12.3 Optimization Techniques and Algorithms
		12.3.1 Genetic Algorithm
		12.3.2 Topology Optimization Algorithm
			12.3.2.1 SKO (Soft Kill Option)
			12.3.2.2 Solid Isotropic Material with Penalization (SIMP)
		12.3.3 Particle-Swarm Optimization
		12.3.4 Multiobjective Optimization Algorithm
		12.3.5 Approximate Optimization
		12.3.6 Uncertainty Optimization Algorithm
		12.3.7 Memetic Search Optimization
	12.4 Parameters for Optimization
		12.4.1 Structural Parameters
		12.4.2 Material Properties and Surface Morphology
		12.4.3 Osseointegration, Implant Design, Surgical Technique and Excessive Loading
	12.5 Complementary Techniques Used with Optimization Algorithms
		12.5.1 Surrogate Models for Optimization Algorithms
			12.5.1.1 Artificial Neural Networks (ANN)
			12.5.1.2 Kriging Interpolation
			12.5.1.3 Use of Support Vector Regression (SVR)
	12.6 Conclusion
	Acknowledgement
	References
Index