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 capabilities and ability to escapes from local optima. Experimental validations across 12 benchmark functions demonstrate ISSA outperforming the standard Sparrow Search Algorithm (SSA) and other advanced optimization algorithms in terms of exploratory and exploitative effectiveness. Specifically, ISSA proves exceptionally effective in autonomously handling the HPO for three typical machine learning (ML) algorithms, demonstrating superior performance over SSA and random search methods. A novel ISSA-ML surrogate model for ECP, incorporating structural and operational parameters, showcases significant improvements in predictive accuracy and robustness over traditional polynomial regression and ML models under conventional HPO methods. Furthermore, the application of this surrogate model in multi-objective optimization design for ECPs significantly reduces development time and computational costs, offering a streamlined and cost-effective solution for optimizing ECP performance. This study highlights potential future research directions, including the integration of other ML enhancements and the inclusion of more comprehensive feature parameters, to further improve the model's universality and applicability.