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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Veröffentlicht in:	Engineering applications of artificial intelligence Jg. 103; S. 104309
Hauptverfasser:	Houssein, Essam H., Helmy, Bahaa El-din, Rezk, Hegazy, Nassef, Ahmed M.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 01.08.2021
Schlagworte:	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-Zugang:	Volltext
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