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.