LightGBM Technique and Differential Evolution Algorithm-Based Multi-Objective Optimization Design of DS-APMM

This article proposes a multi-objective optimization method for the optimization design of a new dual-stator arc permanent magnet machine (DS-APMM) which can be applied on the direct-drive scanning systems with limited angular movement, such as radar, large telescope. The proposed optimization method integrates light gradient boosting machine (LightGBM) with differential evolution algorithm (DEA) to achieve optimal design objectives of high back electromotive force, low total harmonic distortion, high average torque, and low torque ripple at different rotor speeds. The machine topology and analytical model of DS-APMM are firstly presented to determine the structural parameters to be optimized. The sensitivity of each structural parameter to the optimization objectives is analyzed based on the SHAP (SHapley Additive exPlanations) value. Then, a finite-element analysis (FEA)-based DS-APMM model is developed to acquire sample data. Based on the acquired sample data, a machine learning algorithm, LightGBM, is introduced to establish surrogate model that can fit the function relationship between design objectives and structural parameters. Subsequently, an intelligent search algorithm named DEA is adopted to search optimal combination of the structural parameters and hence obtain optimal machine performances of DS-APMM. Finally, the electromagnetic characteristics of the initial model, middle model and optimal model of DS-APMM are compared and analyzed, both FEA and prototype experiments verify the feasibility and superiority of the proposed multi-objective optimization method.