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Title: Global Optimization Algorithms and Design Applications, Edition 1.2
Author: Hon Keung Kwan
eBook: 403 pages
Language: English
Publisher: dfisp.org
Publication Date: March 13, 2018
ISBN13: 9781988307046
Dimensions: 6 x 9 inches
Format: Adobe Digital Editions
Platform: Windows, Machintosh, and Andriod desktop/ laptop/ tablet/ phone
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Testimonials
Pengzhao Song, M.App.Sc. (2017), Ph.D. Student, Department of Electrical and Computer Engineering, University of Windsor, Canada [29 August 2020]:
This eBook offers useful chapters on Global Optimization Algorithms and Design Applications and gives well-explained design examples for the purpose of applications, especially in engineering. The concepts are explained step by step, which is friendly to new beginners and a wealth of design examples are given to meet the requirements of readers with different backgrounds. This eBook is really an excellent choice not only for undergraduate and graduate students on global optimization in engineering and computer science, but also for thirsty readers in the field of engineering and system design using optimization algorithms.
Description
Global optimization algorithms search the minimum (or maximum) of the single objective (or multiobjective) function of a design problem without or with constraints.
This eBook covers selected evolutionary algorithms and on how these optimization algorithms can be used to solve test functions, optimization problems, and to design digital filters, neural networks, fuzzy and hybrid systems.
Matlab-based design examples on test functions, optimization problems, digital filters, neural networks, fuzzy and hybrid systems are given to illustrate how these evolutionary algorithms can be applied to solve these and other design problems. Each of the optimization algorithms is clearly described and explained, relevant equations, pseudocodes, and design examples are given.
An undergraduate-level background in linear algebra and calculus would be useful to understand the derivations involved in the optimization algorithms.
Readership
This eBook is suitable to be used as a senior undergraduate and graduate textbook on global optimization and design applications for students in engineering, computer science, science, and business, and other programs.
The book is also suitable for practising engineers, computing professionals, and anyone who are interested to self-learn, understand, and/or explore new possibilities for research and project in global optimization for design applications
About the Author
The author received his D.I.C. and Ph.D. degree in Electrical Engineering (Signal Processing) in 1981 from the Imperial College London, United Kingdom. He has served since 1989 as a Professor of Electrical and Computer Engineering at the University of Windsor where he initiated and started the development of computational intelligence research program and courses. Prior to this, he had served as a faculty member for seven years at the University of Hong Kong where he initiated and started the development of digital signal processing research program and courses since 1981. He was elected in 1996 a Fellow of the Institution of Engineering and Technology. He has written eight eBooks (published by dfisp.org) and five book chapters, co-edited two printed books, and published over 300 journal and conference papers in digital signal processing and computational intelligence. Dr. Kwan had served as the Chair (2003-2004) of the Neural Systems and Applications Technical Committee and the Chair (2004-2006) of Digital Signal Processing Technical Committee of the IEEE Circuits and Systems Society, and the Chair (2008-2009) of Intelligent Systems and Applications Technical Committee of Asia Pacific Signal and Information Processing Association. He is a registered P. Eng. in Ontario, Canada.
Table of Contents
The Edition 1.2 of this eBook contains 14 chapters.
Part I Design Optimization and Problems
Optimization Basics, Functions, and Problems
Digital Filter Design and Problems
Neural, Fuzzy, and Hybrid System Design and Problems
Constrained Handling Methods
Part II Evolutionary Optimization Algorithms
Genetic Algorithms
Differential Evolution
Particle Swarm Optimization
Artificial Bee Colony Algorithm
Ant Colony Optimization
Cuckoo Search Algorithm
Teaching-Learning-Based Optimization
Cultural Algorithms
Simulated Annealing
Harmony Search
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