基于改进的NSGA-II的圆筒型永磁直线电机的多目标优化
Multi-Objective Optimization of Tubular Permanent Magnet Linear Motor Based on Improved NSGA-II
DOI: 10.12677/MOS.2023.123255, PDF,  被引量    科研立项经费支持
作者: 彭 珍：浙江理工大学，信息科学与工程学院，浙江 杭州；刘春元*, 周振峰：嘉兴学院，信息科学与工程学院，浙江 嘉兴
关键词: 圆筒型永磁直线电机；多目标优化；功率；非支配排序遗传算法；Tubular Permanent Magnet Linear Motor； Multi-Objective Optimization； Power； Non-Nominated Sorting Genetic Algorithm
摘要: 本文改进了传统的第二代非支配排序遗传算法(Non-dominated Sorting Genetic Algorithm-II, NSGAII)，以解决在圆筒型永磁直线电机功率和效率的多目标优化中，Pareto解集分布不佳且容易陷入局部最优解的问题。首先，介绍电机的相关原理，并建立电机的二维有限元模型，根据模型计算电压、电流等数据；其次，使用敏感参数分析法从所有的电机结构参数中选择重要优化变量，并建立优化模型、计算电机的电磁参数；最后，对电磁参数数据训练，以改进的NSGA-II为优化算法的多目标优化模型，优化电机的功率和效率，并对结果进行验证。仿真结果验证了所提出的改进的NSGA-II的优越性。
Abstract: This article improves the traditional NSGA-II genetic algorithm to solve the problem of poor distri-bution of Pareto solution set and easy local optimal solution in multi-objective optimization of pow-er and efficiency of Tubular Permanent Magnet Linear Motor. Firstly, introduce the relevant princi-ples of the motor and establish a two-dimensional finite element model of the motor. Calculate voltage, current and other data based on the model. Secondly, use the sensitive parameter analysis method to select important optimization variables from all motor structural parameters and estab-lish an optimization model to calculate the motor’s electromagnetic parameters. Finally, the elec-tromagnetic parameter data is trained, and the improved NSGA-II is used as the multi-objective op-timization model of the optimization algorithm to optimize the power and efficiency of the motor, and the results are verified. The simulation results verify the superiority of the proposed improved NSGA-II.
文章引用：彭珍, 刘春元, 周振峰. 基于改进的NSGA-II的圆筒型永磁直线电机的多目标优化[J]. 建模与仿真, 2023, 12(3): 2782-2790. https://doi.org/10.12677/MOS.2023.123255

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