A Practical Approach to Metaheuristics using LabVIEW and MATLAB®

1st Edition

Pedro Ponce-Cruz, Arturo Molina Gutiérrez, Ricardo A. Ramírez-Mendoza, Efraín Méndez Flores, Alexandro Antonio Ortiz Espinoza, David Christopher Balderas Silva

Chapman and Hall/CRC
July 1, 2020 Forthcoming
Reference - 176 Pages - 126 B/W Illustrations
ISBN 9780367337049 - CAT# 316880
Series: Chapman & Hall/CRC Computer and Information Science Series

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Metaheuristic optimization has become a prime alternative for solving complex optimization problems in several areas. Hence, practitioners and researchers have been paying extensive attention to those metaheuristic algorithms that are mainly based on natural phenomena. However, when those algorithms are implemented, there are not enough textbooks that deal with theoretical and experimental problems in a friendly manner so this textbook presents a novel structure which includes a complete description of the most important metaheuristic optimization algorithms as well as a new proposal of a new metaheuristic optimization named earthquake optimization. Besides, this textbook also has several practical exercises. In addition, a Toolbox for Matlab and a Toolkit for LabVIEW are integrated as a complementary material of this book. Those toolkits allow readers to move from a simulation environment to an experimentation one very fast.  This textbook is suitable for researchers, students, and professionals in several areas as economics, architecture, computer science, electrical engineering, control system and so on.

  • Unique features of this textbook integrate:
  • Developed for researchers, undergraduate, and graduate students and practitioners 
  • A friendly description of the main metaheuristic optimization algorithms 
  • Theoretical and practical optimization examples
  • A new earthquake optimization algorithm
  • Update state of the art and research optimization projects 

The authors are multi/interdisciplinary lecturers and researchers who write a structured-friendly learning methodology to understand each metaheuristic optimization algorithm presented in this textbook.


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