Modern Optimization Methods for Science, Engineering and Technology

Optimization methods are designed to provide the best possible solutions to an engineering problem or a system design. These methods are intended to improve the performance of a system in terms of several performance evaluation factors such as cost, time, computational complexity and raw materials required etc. Optimization techniques have found a range of applications across numerous disciplines including robotics; artificial intelligence based innovations; the chemical, electrical and manufacturing industries; and many others. 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. The book includes discussion on linear programming, 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. The book will be very useful for a wide spectrum of target readers including students and researchers in academia and industry.