Metaheuristic Algorithms in Industry 4.0 Advances in Metaheuristics Series

Due to increasing industry 4.0 practices, massive industrial process data is now available for researchers for modelling and optimization. Artificial Intelligence methods can be applied to the ever-increasing process data to achieve robust control against foreseen and unforeseen system fluctuations. Smart computing techniques, machine learning, deep learning, computer vision, for example, will be inseparable from the highly automated factories of tomorrow. Effective cybersecurity will be a must for all Internet of Things (IoT) enabled work and office spaces.

This book addresses metaheuristics in all aspects of Industry 4.0. It covers metaheuristic applications in IoT, cyber physical systems, control systems, smart computing, artificial intelligence, sensor networks, robotics, cybersecurity, smart factory, predictive analytics and more.

Key features:

• Includes industrial case studies.
• Includes chapters on cyber physical systems, machine learning, deep learning, cybersecurity, robotics, smart manufacturing and predictive analytics.
• surveys current trends and challenges in metaheuristics and industry 4.0.

Metaheuristic Algorithms in Industry 4.0 provides a guiding light to engineers, researchers, students, faculty and other professionals engaged in exploring and implementing industry 4.0 solutions in various systems and processes.

Contents

Preface

Editors

Contributors

1. A Review on Cyber Physical Systems and Smart Computing: Bibliometric Analysis

Deepak Sharma, Prashant K. Gupta, and Javier Andreu-Perez

2. Design Optimization of Close-Fitting Free-Standing Acoustic Enclosure Using Jaya Algorithm

Ashish Khachane and Vijaykumar S. Jatti

3. A Metaheuristic Scheme for Secure Control of Cyber-Physical Systems

Tua A. Tamba

4. Application of Salp Swarm Algorithm to Solve Constrained Optimization Problems with Dynamic Penalty Approach in Real-Life Problems

Omkar Kulkarni, G. M. Kakandikar, and V. M. Nandedkar

5. Optimization of Robot Path Planning Using Advanced Optimization Techniques

R. V. Rao and S. Patel

6. Semi-Empirical Modeling and Jaya Optimization of White Layer Thickness during Electrical Discharge Machining of NiTi Alloy

Mahendra Uttam Gaikwad, A. Krishnamoorthy, and Vijaykumar S. Jatti

7. Analysis of Convolution Neural Network Architectures and

Their Applications in Industry 4.0

Gaurav Bansod, Shardul Khandekar, and Soumya Khurana

8. EMD-Based Triaging of Pulmonary Diseases Using Chest Radiographs (X-Rays)

Niranjan Chavan, Priya Ranjan, Uday Kumar, Kumar Dron Shrivastav, and Rajiv Janardhanan

9. Adaptive Neuro Fuzzy Inference System to Predict Material Removal Rate during Cryo-Treated Electric Discharge Machining

Vaibhav S. Gaikwad, Vijaykumar S. Jatti, Satish S. Chinchanikar, and Keshav N. Nandurkar

10. A Metaheuristic Optimization Algorithm-Based Speed Controller for Brushless DC Motor: Industrial Case Study

K. Vanchinathan, P. Sathiskumar, and N. Selvaganesan

11. Predictive Analysis of Cellular Networks: A Survey

Nilakshee Rajule, Radhika Menon, and Anju Kulkarni

12. Optimization Techniques and Algorithms for Dental Implants – A Comprehensive Review

Niharika Karnik and Pankaj Dhatrak

Index

Pritesh Shah is an Associate Professor at the Symbiosis Institute of Technology, Symbiosis International (Deemed University), India.

Ravi Sekhar is an Assistant Professor at the Symbiosis Institute of Technology, Symbiosis International (Deemed University), India.

Anand J. Kulkarni is an Associate Professor at the Symbiosis Center for Research and Innovation, Symbiosis International (Deemed University), India.

Patrick Siarry is a Professor of Automatics and Informatics at the University of Paris-Est Creteil, where he leads the Image and Signal Processing team in the Laboratoire Images, Signaux et Systemes Intelligents (LiSSi).