An enhanced Archimedes optimization algorithm based on Local escaping operator and Orthogonal learning for PEM fuel cell parameter identification

Meta-heuristic optimization algorithms aim to tackle real world problems through maximizing some specific criteria such as performance, profit, and quality or minimizing others such as cost, time, and error. Accordingly, this paper introduces an improved version of a well-known optimization algorith...

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Bibliographic Details

Published in:	Engineering applications of artificial intelligence Vol. 103; p. 104309
Main Authors:	Houssein, Essam H., Helmy, Bahaa El-din, Rezk, Hegazy, Nassef, Ahmed M.
Format:	Journal Article
Language:	English
Published:	Elsevier Ltd 01.08.2021
Subjects:	Archimedes optimization algorithm (AOA) Energy efficiency Fuel cell Local escaping operator Meta-heuristic optimization algorithms Orthogonal learning Local escaping operator Orthogonal learning Energy efficiency Archimedes optimization algorithm (AOA) Meta-heuristic optimization algorithms Fuel cell
ISSN:	0952-1976, 1873-6769
Online Access:	Get full text
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