Metaheuristic Algorithms in Industry 4.0

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.

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).