Machine Learning Surrogate Model Optimized by Improved Sparrow Search Algorithm for Multi-Objective Optimization of Permanent Magnet Synchronous Motor Direct-Drive Pump

This study introduces an Improved Sparrow Search Algorithm (ISSA) to address the challenges of machine learning Hyperparameter Optimization (HPO) and efficient modeling of Electric Coolant Pumps (ECP) in electric vehicles. By integrating Lévy flight and Bernoulli mapping, ISSA enhances global search...

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Veröffentlicht in:	IEEE transactions on vehicular technology Jg. 73; H. 9; S. 12773 - 12786
Hauptverfasser:	Zhang, Yiming, Fei, Liangyu, Chui, Chee-Kong, Chng, Chin-Boon, Zhao, Shengdun, Li, Jingxiang
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.09.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Computational modeling Coolant pumps Design optimization Effectiveness Electric coolant pump Electric vehicles Integrated circuit modeling Machine learning Machine learning algorithms Motors multi-objective optimization Multiple objective analysis Optimization Parameter robustness Permanent magnets Polynomials Pumps Random search method Regression models Search algorithms sparrow search algorithm surrogate model Synchronous motors
ISSN:	0018-9545, 1939-9359
Online-Zugang:	Volltext
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