New Advancements in Swarm Algorithms: Operators and Applications

This book presents advances in alternative swarm development that have proved to be effective in several complex problems. Swarm intelligence (SI) is a problem-solving methodology that results from the cooperation between a set of agents with similar characteristics. The study of biological entities, such as animals and insects, manifesting social behavior has resulted in several computational models of swarm intelligence. While there are numerous books addressing the most widely known swarm methods, namely ant colony algorithms and particle swarm optimization, those discussing new alternative approaches are rare. The focus on developments based on the simple modification of popular swarm methods overlooks the opportunity to discover new techniques and procedures that can be useful in solving problems formulated by the academic and industrial communities. Presenting various novel swarm methods and their practical applications, the book helps researchers, lecturers, engineersand practitioners solve their own optimization problems.

Presents advances in new, alternative swarm developments Demonstrates the potential of new swarm alternative algorithms from a practical perspective Discusses various novel metaheuristic methods and their practical applications

Autorentext
Erik Cuevas received his B.S. degree with distinction in Electronics and Communications Engineering from the University of Guadalajara, Mexico, in 1995, the M.Sc. degree in Industrial Electronics from ITESO, Mexico, in 2000, and the Ph.D. degree from Freie Universität Berlin, Germany in 2006. Since 2006 he has been with the University of Guadalajara, where he is currently a full-time Professor in the Department of Computer Science. Since 2008, he is a member of the Mexican National Research System (SNI III). He is the author of several books and articles. A list of his books and publications can be seen in the CV attached to this application. His current research interest includes Meta-heuristics, computer vision, and mathematical methods. He serves as an editor in Expert System with Applications, ISA Transactions, and Applied Soft Computing, Applied Mathematical Modeling and Mathematics and Computers in Simulation. Alberto Luque Chang graduated with a Bachelor's Degree in Communications and Electronics Engineering (2013), a Master of Science in Electronic Engineering and Computing (2016), and a Doctorate in Electronics and Computing Sciences (2021) in the University of Guadalajara (UdeG). He is currently a professor in the Division of Technologies for Cyber-Human Integration at the University Center for Exact Sciences and Engineering (CUCEI) of the UdeG. Likewise, since 2021, Dr. Luque is a member of the National System of Researchers, having the distinction of National Researcher Level 1. His areas of interest in research are Metaheuristic Algorithms, Artificial Intelligence, Optimization, Machine Learning and its applications. to Image Processing. Héctor Escobar received a B.S. degree with honors in Information Systems Engineering from the Autonomous University of Sinaloa, Mexico, in 2018 and an M.S. degree in Electronics and Computer Engineering from the University of Guadalajara, Mexico, in 2021. He is part of the Universityof Guadalajara, where he is a full-time Ph.D. student in the Electronics and Computer Science program. His current research interests include Metaheuristics, computer vision, artificial intelligence, and Agent-Based Modeling.

Inhalt

An Introduction to Nature-Inspired Metaheuristics and swarm methods.