04443nam a22005535i 4500 978-3-031-63053-8 DE-He213 20251030112334.0 cr nn 008mamaa 240626s2024 sz | s |||| 0|eng d 9783031630538 978-3-031-63053-8 10.1007/978-3-031-63053-8 doi Q342 UYQ bicssc COM004000 bisacsh UYQ thema Cuevas, Erik. author. aut http://id.loc.gov/vocabulary/relators/aut 65714 Metaheuristic Algorithms: New Methods, Evaluation, and Performance Analysis [electronic resource] / by Erik Cuevas, Alberto Luque, Bernardo Morales Castañeda, Beatriz Rivera. 1st ed. 2024. Cham : Springer Nature Switzerland : Imprint: Springer, 2024. XV, 297 p. 83 illus., 39 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Studies in Computational Intelligence, 1860-9503 ; 1163 -- 1. Introduction to Metaheuristic methods. -- 2. A novel method for initializing populations using the Metropolis-Hastings (MH) technique. -- 3. A measure of diversity for metaheuristic algorithms employing population-based approaches. -- 4. Population Control in Metaheuristic Algorithms: Can Fewer Be Better?. -- 5. Exploration Paths Derived from Trajectories Extracted from Second-Order System Responses. -- 6. Utilizing the Moth Swarm Algorithm to Improve Image Contrast. -- 7. Enhancing Anisotropic Diffusion Filtering via Multi-Objective Optimization. -- 8. Fractional Fuzzy Controller Calibration Using metaheuristic Techniques. -- 9. Striving for Optimal Equilibrium in Metaheuristic Algorithms: Is It Attainable?. This book encompasses three distinct yet interconnected objectives. Firstly, it aims to present and elucidate novel metaheuristic algorithms that feature innovative search mechanisms, setting them apart from conventional metaheuristic methods. Secondly, this book endeavors to systematically assess the performance of well-established algorithms across a spectrum of intricate and real-world problems. Finally, this book serves as a vital resource for the analysis and evaluation of metaheuristic algorithms. It provides a foundational framework for assessing their performance, particularly in terms of the balance between exploration and exploitation, as well as their capacity to obtain optimal solutions. Collectively, these objectives contribute to advancing our understanding of metaheuristic methods and their applicability in addressing diverse and demanding optimization tasks. The materials were compiled from a teaching perspective. For this reason, the book is primarily intended for undergraduate and postgraduate students of Science, Electrical Engineering, or Computational Mathematics. Additionally, engineering practitioners who are not familiar with metaheuristic computation concepts will appreciate that the techniques discussed are beyond simple theoretical tools because they have been adapted to solve significant problems that commonly arise in engineering areas. Computational intelligence. 64052 Artificial intelligence. 58441 Computational Intelligence. 64052 Artificial Intelligence. 58441 Luque, Alberto. author. aut http://id.loc.gov/vocabulary/relators/aut 65719 Morales Castañeda, Bernardo. author. aut http://id.loc.gov/vocabulary/relators/aut 79391 Rivera, Beatriz. author. aut http://id.loc.gov/vocabulary/relators/aut 79392 SpringerLink (Online service) Springer Nature eBook Printed edition: 9783031630521 Printed edition: 9783031630545 Printed edition: 9783031630552 Studies in Computational Intelligence, 1860-9503 ; 1163 68854 https://doi.org/10.1007/978-3-031-63053-8 ZDB-2-INR ZDB-2-SXIT 61054 61054