Advances in Metaheuristics Algorithms: Methods and Applications - Couverture souple

Cuevas, Erik; Zaldvar, Daniel; Prez-cisneros, Marco

Couverture souple

ISBN 10 : 3030077365 ISBN 13 : 9783030077365

Editeur : Springer, 2018

Synopsis

This book explores new alternative metaheuristic developments that have proved to be effective in their application to several complex problems. Though most of the new metaheuristic algorithms considered offer promising results, they are nevertheless still in their infancy. To grow and attain their full potential, new metaheuristic methods must be applied in a great variety of problems and contexts, so that they not only perform well in their reported sets of optimization problems, but also in new complex formulations. The only way to accomplish this is to disseminate these methods in various technical areas as optimization tools. In general, once a scientist, engineer or practitioner recognizes a problem as a particular instance of a more generic class, he/she can select one of several metaheuristic algorithms that guarantee an expected optimization performance. Unfortunately, the set of options are concentrated on algorithms whose popularity and high proliferation outstrip those of the new developments. This structure is important, because the authors recognize this methodology as the best way to help researchers, lecturers, engineers and practitioners solve their own optimization problems.

A propos du livre

À propos de l'auteur

Dr. 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. 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. Daniel Zaldivar graduated from the University of Guadalajara, Mexico in 1995 with a B.S. degree in Electronics and Communications Engineering. Later, in 2000, he earned his M.Sc. degree in Industrial Electronics from ITESO, Mexico, and in 2006 he received his Ph.D. degree from Freie Universität Berlin, Germany. Since then, he has been employed as a full-time Professor in the Department of Computer Science at the University of Guadalajara, where he currently holds his position. Ernesto Ayala, originally from León, Guanajuato was born in 1982. He received the title of Electrical Mechanical Engineer in 2017 and in 2019 the master's degree in Applied Computing at the University of Guadalajara. He is currently a PhD candidate in Electronics and Computing Sciences. Since 2018, he has been teaching curricular courses in Robotics Engineering and Electronic Engineering in the Division of Technologies for Cyber-human Integration of the University Center for Exact Sciences and Engineering. His area of expertise is computer vision and evolutionary computing. Mr. Ayala collaborates with a research group atthe University of Guadalajara focused on the development of ecological and autonomous driving vehicles. Oscar González received his B.S. with distinction in Electronic Engineering and Communications from the University of Guadalajara, Mexico, in 2022. During the COVID-19 pandemic, he was a member of the advisory committee for the COVID-19 pandemic of the University of Guadalajara. For his contributions and studies on COVID-19, he has been awarded the Irene Robledo García Award, the highest distinction of the University of Guadalajara for social service in 2022. Fernando Vega received the title of technical career in electricity by C.B.E.T.I.S. in 2014. Obtained a B.S. degree in Mechatronics from the National Technologist of Mexico, campus Culiacan, Mexico, in 2019. He is part of the University of Guadalajara, a full-time student M.S. in the Electronics and Computer Science program. His current research interests include motors design, electric vehicle design, Metaheuristics.

À propos de la quatrième de couverture

This book explores new alternative metaheuristic developments that have proved to be effective in their application to several complex problems. Though most of the new metaheuristic algorithms considered offer promising results, they are nevertheless still in their infancy. To grow and attain their full potential, new metaheuristic methods must be applied in a great variety of problems and contexts, so that they not only perform well in their reported sets of optimization problems, but also in new complex formulations. The only way to accomplish this is to disseminate these methods in various technical areas as optimization tools. In general, once a scientist, engineer or practitioner recognizes a problem as a particular instance of a more generic class, he/she can select one of several metaheuristic algorithms that guarantee an expected optimization performance. Unfortunately, the set of options are concentrated on algorithms whose popularity and high proliferation outstrip those of the new developments. This structure is important, because the authors recognize this methodology as the best way to help researchers, lecturers, engineers and practitioners solve their own optimization problems.

Détails de l'édition

• Éditeur: Springer
• Date d'édition: 2018
• Langue: anglais
• ISBN 10: 3030077365
• ISBN 13: 9783030077365
• Reliure: Broché
• Nombre de pages: 218
• Coordonnées du fabricant: Springer Nature Customer Service Center GmbH; ProductSafety@springernature.com; ; Europaplatz 3; Heidelberg; 69115; Allemagne