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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Vydáno v:	Engineering applications of artificial intelligence Ročník 103; s. 104309
Hlavní autoři:	Houssein, Essam H., Helmy, Bahaa El-din, Rezk, Hegazy, Nassef, Ahmed M.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier Ltd 01.08.2021
Témata:	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
On-line přístup:	Získat plný text
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