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

机译：基于LightGBM技术和基于差分演进算法的DS-APMM的多目标优化设计

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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.
机译：本文提出了一种多目标优化方法，用于新型双定子弧形永磁机（DS-APMM）的优化设计，可应用于具有有限角运动的直接驱动扫描系统，例如雷达，大望远镜。所提出的优化方法将光梯度升压机（LightGBM）与差分演进算法（DEA）集成，以实现高背电动势，低总谐波失真，高平均扭矩和不同转子速度的低扭矩脉动的最佳设计目标。首先提出了DS-APMM的机器拓扑和分析模型以确定优化的结构参数。基于Shap（福芙添加剂解释）值分析每个结构参数对优化目标的灵敏度。然后，开发了基于有限元分析（FEA）的基于DS-APMM模型以获取样本数据。基于所获取的样本数据，引入了一种机器学习算法，LightGBM，以建立替代模型，可以符合设计目标与结构参数之间的功能关系。随后，采用名为DEA的智能搜索算法来搜索结构参数的最佳组合，从而获得DS-APMM的最佳机器性能。最后，比较和分析了DS-APMM的初始模型，中间模型和最佳模型的电磁特性，FEA和原型实验验证了所提出的多目标优化方法的可行性和优越性。

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