Modern Optimization Methods for Science, Engineering and Technology: (IOP ebooks)

Achieving a better solution or improving the performance of existing system design is an ongoing process for which scientists, engineers, mathematicians and researchers have been striving for many years. The purpose of this book is to describe the fundamentals, background and theoretical concepts of optimization principles in a comprehensive manner, along with their potential applications and implementation strategies. Focus is on case studies and real time applications as well as current research directions. Useful for a wide spectrum of target readers including students and researchers in academia and industry, discussion on linear programming is included, along with multi-variable methods for risk assessment, nonlinear methods, ant colony optimization, particle swarm optimization, multi-criterion and topology optimization, learning classifier, case studies on six sigma, performance measures and evaluation, multi-objective optimization problems, machine learning approaches, genetic algorithms and quality of service optimizations.

Table of Contents:
Preface Acknowledgement Editor biography List of contributors 1 Introduction and background of optimization theory 2 Linear programming 3 Multivariable optimization methods of risk assessment of business processes of manufacturing enterprises 4 Non-linear optimization methods - overview and future scope 5 Implementing travelling salesman problem using modified ant colony optimization algorithm 6 Application of particle swarm optimization technique in motor imagery classification problem 7 Multi-criterion and topology optimization using Lie symmetries for differential equations 8 Learning classifier systems 9 A case study on implementation of six sigma tools for process improvement 10 Performance evaluation and measures 11 Evolutionary techniques in design of PID controllers 12 A variational approach to substantial efficiency for linear multi-objective optimization problems with implications to market problems 13 Machine learning approach to engineering optimization tasks 14 Simulation of the process of being formed of spatial fine structures in environmental safety management systems and optimization of parameters of dispersive devices 15 Future Directions: IoT, robotics, and AI based applications 16 Efficacy of genetic algorithms for computationally intractable problems 17 A novel approach for QoS optimization in 4G cellular networks

About the Author :
G R Sinha is an adjunct professor at the International Institute of Information Technology, Bangalore, currently deputed as a professor at Myanmar Institute of Information Technology, Mandalay. He obtained his BE and MTech with gold medal from the National Institute of Technology, Raipur and his PhD in electronics and telecommunication engineering from Chhattisgarh Swami Vivekanand Technical University, Bhilai. He has published more than 200 research papers in various international and national journals and conferences, is an active reviewer and editorial member of numerous international journals, and has authored or edited six books.