• 中国计算机学会会刊
  • 中国科技核心期刊
  • 中文核心期刊

计算机工程与科学 ›› 2021, Vol. 43 ›› Issue (03): 542-550.

• 人工智能与数据挖掘 • 上一篇    下一篇

基于模糊逻辑NSGA-Ⅲ的开关磁阻发电机多目标优化算法

李艺辉,刘作军,李洁   

  1. (河北工业大学人工智能与数据科学学院,天津 300132)
  • 收稿日期:2020-02-11 修回日期:2020-04-20 接受日期:2021-03-25 出版日期:2021-03-25 发布日期:2021-03-29

A multi-objective optimization algorithm of switched reluctance generator based on fuzzy logic NSGA-Ⅲ

LI Yi-hui,LIU Zuo-jun,LI Jie   

  1. (School of Artificial Intelligence,Hebei University of Technology,Tianjin 300132,China)
  • Received:2020-02-11 Revised:2020-04-20 Accepted:2021-03-25 Online:2021-03-25 Published:2021-03-29

摘要: 为了减小开关磁阻发电机SRG转矩脉动,提高发电机系统运行效率及发电机功率密度,提出一种基于模糊逻辑NSGA-Ⅲ的开关磁阻发电机多目标优化算法。搭建1 kW四相8/6极SRG多目标优化设计模型;采用响应面法RSM搭建SRG优化目标的响应面模型;基于模糊逻辑搭建了模糊推理系统,完成了在SRG优化过程中对NSGA-Ⅲ算法内种群个体相对强度值的赋值,在SRG多目标优化过程中引入决策者的偏好信息,生成满足决策者偏好的Pareto最优解集。通过与NSGA-Ⅲ的对比实验验证了考虑决策者偏好的模糊逻辑NSGA-Ⅲ的优越性,对比SRG优化前后的运行性能,验证了所提SRG多目标优化算法的有效性。


关键词: 开关磁阻发电机;响应面法;NSGA-Ⅲ;模糊逻辑;多目标优化 

Abstract: A multi-objective optimization method based on fuzzy logic NSGA-Ⅲ is proposed to optimize torque ripple, efficiency of system operations and output power density of switched reluctance ge-nerator (SRG). The multi-objective optimization design model of 1kW four-phase 8/6 pole SRG is built. Then, the SRG optimization objectives models are established by using response surface methodology (RSM). Mamdani fuzzy reasoning system based on fuzzy logic is established, so that intensity value assignment of population individuals is achieved, and the decision maker's preference information is introduced into the algorithm. The optimization direction of the algorithm is guided through the value. Finally, optimal Pareto set of SRG optimization that satisfies the preferences of decision maker is generated based on improved elitist NSGA-III, and the solution with largest S is determined as the optimal solution for SRG multi-objective optimization. Experiments verify that fuzzy logic NSGA-Ⅲ is superior to NSGA-III when considering the preference of decision maker. SRG's finite element simulation results prove the validity and feasibility of the proposed multi-objective optimization design method.


Key words: switched reluctance generator, response surface methodology, NSGA-III, fuzzy logic, multi-objective optimization