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Recent metaheuristics algorithms for...

Avalos, Omar.

Recent metaheuristics algorithms for parameter identification

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Recent metaheuristics algorithms for parameter identificationby Erik Cuevas, Jorge Galvez, Omar Avalos.
作者:	Cuevas, Erik.
其他作者:	Galvez, Jorge.
出版者:	Cham :Springer International Publishing :2020.
面頁冊數:	xiv, 297 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Heuristic algorithms.
電子資源:	https://doi.org/10.1007/978-3-030-28917-1
ISBN:	9783030289171$q(electronic bk.)

Recent metaheuristics algorithms for parameter identification
Cuevas, Erik.

Recent metaheuristics algorithms for parameter identification[electronic resource] /by Erik Cuevas, Jorge Galvez, Omar Avalos. - Cham :Springer International Publishing :2020. - xiv, 297 p. :ill., digital ;24 cm. - Studies in computational intelligence,v.8541860-949X ;. - Studies in computational intelligence ;v. 216..

Introduction to optimization and metaheuristic methods -- Optimization techniques in parameters setting for Induction Motor -- An enhanced crow search algorithm applied to energy approaches -- Comparison of solar cells parameters estimation using several optimization algorithms -- Gravitational search algorithm for non-linear system identification using ANFIS-Hammerstein approach -- Fuzzy Logic Based Optimization Algorithm -- Neighborhood Based Optimization Algorithm -- Knowledge-Based Optimization Algorithm.

This book presents new, alternative metaheuristic developments that have proved to be effective in various complex problems to help researchers, lecturers, engineers, and practitioners solve their own optimization problems. It also bridges the gap between recent metaheuristic techniques and interesting identification system methods that benefit from the convenience of metaheuristic schemes by explaining basic ideas of the proposed applications in ways that can be understood by readers new to these fields. As such it is a valuable resource for energy practitioners who are not researchers in metaheuristics. In addition, it offers members of the metaheuristic community insights into how system identification and energy problems can be translated into optimization tasks.

ISBN: 9783030289171$q(electronic bk.)

Standard No.: 10.1007/978-3-030-28917-1doiSubjects--Topical Terms:

455932
Heuristic algorithms.

LC Class. No.: QA76.9.A43 / C84 2020

Dewey Class. No.: 005.1

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