Considered one of the most innovative research directions, computational intelligence (CI) embraces techniques that use global search optimization, machine learning, approximate reasoning, and connectionist systems to develop efficient, robust, and easy-to-use solutions amidst multiple decision variables, complex constraints, and tumultuous environments. CI techniques involve a combination of learning, adaptation, and evolution used for intelligent applications. Computational Intelligence Paradigms for Optimization Problems Using MATLAB Simulink explores the performance of CI in terms of knowledge representation, adaptability, optimality, and processing speed for different real-world optimization problems.Focusing on the practical implementation of CI techniques, this book:Discusses the role of CI paradigms in engineering applications such as unit commitment and economic load dispatch, harmonic reduction, load frequency control and automatic voltage regulation, job shop scheduling, multidepot vehicle routing, and digital image watermarkingExplains the impact of CI on power systems, control systems, industrial automation, and image processing through the above-mentioned applicationsShows how to apply CI algorithms to constraint-based optimization problems using MATLAB m-files and Simulink modelsIncludes experimental analyses and results of test systemsComputational Intelligence Paradigms for Optimization Problems Using MATLAB Simulink provides a valuable reference for industry professionals and advanced undergraduate, postgraduate, and research students.
This book explores the performance of computational intelligence (CI) in terms of knowledge representation, adaptability, optimality, and processing speed for different real-world optimization problems. Focusing on the practical implementation of CI techniques, the text discusses the role of CI paradigms in engineering applications; explains the impact of CI on power systems, control systems, industrial automation, and image processing; and shows how to apply CI algorithms to constraint-based optimization problems using MATLAB® m-files and Simulink® models. Experimental analyses and results of test systems are included.
