This book is to explore the development of metaheuristic algorithms over the past decade, focusing on key advancements in their components and structural features, which have driven progress in search techniques. This analysis aims to provide readers with a thorough understanding of the fundamental aspects of these methods, which are essential for their practical application. To offer a broad perspective on the evolution of metaheuristic algorithms, this book reviews 11 specific algorithms developed by the evolutionary computation group at the University of Guadalajara over the past 10 years.…mehr
This book is to explore the development of metaheuristic algorithms over the past decade, focusing on key advancements in their components and structural features, which have driven progress in search techniques. This analysis aims to provide readers with a thorough understanding of the fundamental aspects of these methods, which are essential for their practical application. To offer a broad perspective on the evolution of metaheuristic algorithms, this book reviews 11 specific algorithms developed by the evolutionary computation group at the University of Guadalajara over the past 10 years. These algorithms illustrate the most significant mechanisms and structures discussed in the academic and research communities during their development. By studying these examples, readers will gain valuable insights into the innovative methods and strategic improvements that have shaped the field. The book is designed from a teaching standpoint, making it suitable for undergraduate and postgraduate students in science, electrical engineering, or computational mathematics. Moreover, engineering practitioners unfamiliar with metaheuristic computation will appreciate how these techniques have been adapted to address complex real-world engineering problems, moving beyond theoretical constructs.
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.
Inhaltsangabe
1.Introductory concepts of metaheuristic techniques.- 2.An algorithm for global optimization inspired by collective animal behavior.- 3.A swarm optimization algorithm inspired in the behavior of the social-spider.- 4.An optimization algorithm inspired by the States of Matter that improves the balance between exploration and exploitation.- 5.Harnessing Locust Swarm Dynamics for Optimization Algorithms.- 6.Improving Function Evaluation Efficiency with an Enhanced Evolutionary Algorithm.- 7.A Fuzzy Logic-Inspired Metaheuristic Method for Enhanced Optimization.- 8.Modeling Optimization Techniques Inspired by Yellow Saddle Goatfish Behavior.- 9.An optimization algorithm guided by a machine learning approach.- 10.An improved Simulated Annealing algorithm based on ancient metallurgy techniques.- 11.Agent-based modeling approaches as metaheuristic methods.- 12.Evolutionary-Mean shift algorithm for dynamic multimodal function optimization.
1.Introductory concepts of metaheuristic techniques.- 2.An algorithm for global optimization inspired by collective animal behavior.- 3.A swarm optimization algorithm inspired in the behavior of the social-spider.- 4.An optimization algorithm inspired by the States of Matter that improves the balance between exploration and exploitation.- 5.Harnessing Locust Swarm Dynamics for Optimization Algorithms.- 6.Improving Function Evaluation Efficiency with an Enhanced Evolutionary Algorithm.- 7.A Fuzzy Logic-Inspired Metaheuristic Method for Enhanced Optimization.- 8.Modeling Optimization Techniques Inspired by Yellow Saddle Goatfish Behavior.- 9.An optimization algorithm guided by a machine learning approach.- 10.An improved Simulated Annealing algorithm based on ancient metallurgy techniques.- 11.Agent-based modeling approaches as metaheuristic methods.- 12.Evolutionary-Mean shift algorithm for dynamic multimodal function optimization.
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