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 watermarking
Explains the impact of CI on power systems, control systems, industrial automation, and image processing through the above-mentioned applications
Shows how to apply CI algorithms to constraint-based optimization problems using MATLAB®m-files and Simulink® models
Includes experimental analyses and results of test systems
Computational Intelligence Paradigms for Optimization Problems Using MATLAB®/ Simulink® provides a valuable reference for industry professionals and advanced undergraduate, postgraduate, and research students.
S. Sumathi, L. Kumar, A., and P. Surekha, Computational Intelligence Paradigms for Optimization Problems Using MATLAB/SIMULINK. CRC Press, 2015.