电子测量与仪器学报

饶盛华,张小平,张铸,赵轩.基于果蝇算法的开关磁阻电机多目标优化研究[J].电子测量与仪器学报,2017,31(7):1152-1158

基于果蝇算法的开关磁阻电机多目标优化研究

Study on multi objective optimization of SRM based on FOA

DOI：10.13382/j.jemi.2017.07.023

英文关键词:SRM fruit fly optimization algorithm(FOA) extreme learning machine

基金项目:国家自然科学基金（51477047，61503132)、湖南省自然科学湘潭联合基金（2016JJ5026)、湖南省研究生科研创新项目（CX2016B604）资助

作者	单位
饶盛华	湖南科技大学海洋矿产资源探采装备与安全技术国家地方联合工程实验室湘潭411201
张小平	湖南科技大学海洋矿产资源探采装备与安全技术国家地方联合工程实验室湘潭411201
张铸	湖南科技大学信息与电气工程学院湘潭411201
赵轩	湖南科技大学海洋矿产资源探采装备与安全技术国家地方联合工程实验室湘潭411201

Author	Institution
Rao Shenghua	NationalLocal Joint Engineering Laboratory of Marine Mineral Resources Exploration Equipment and Safety Technology, Hunan University of Science and Technology, Xiangtan 411201, China
Zhang Xiaoping	NationalLocal Joint Engineering Laboratory of Marine Mineral Resources Exploration Equipment and Safety Technology, Hunan University of Science and Technology, Xiangtan 411201, China
Zhang Zhu	College of Information and Electrical Engineering, Hunan University of Science and Technology, Xiangtan 411201, China
Zhao Xuan	NationalLocal Joint Engineering Laboratory of Marine Mineral Resources Exploration Equipment and Safety Technology, Hunan University of Science and Technology, Xiangtan 411201, China

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中文摘要:

针对采用传统算法对开关磁阻电机(SRM)本体进行多目标优化时存在算法复杂、调节参数多、计算量大且易陷入局部最优解等问题，提出一种基于果蝇算法（FOA）的SRM本体多目标优化设计方法。利用极限学习机算法建立SRM非参数模型，采用FOA算法对其进行优化，实现了SRM定转子极弧的全局最优设计，最后对该优化算法的效果进行了仿真验证，同时与传统粒子群优化算法(PSO)进行了对比分析，结果表明,FOA算法不仅获得了较PSO算法更好的转矩波动系数和效率指标，而且具有参数设置少、收敛速度快且不易陷入局部最优解等特点，具有较好的应用价值。

英文摘要:

The traditional multi objective optimization algorithms are complex with too many regulation parameters and large amount of calculation, and they are easily trapped in the local optimal solution. Aiming at the problem above, a novel optimization method based on fruit fly optimization algorithm(FOA) is proposed in the paper. The switched reluctance motor (SRM) is then modeled by extreme learning machine algorithm, and optimized by FOA. Finally, the proposed algorithm is verified by various simulations, and the comparative analysis with traditional PSO are carried out. It is demonstrated that better optimization results of torque ripple and efficiency are achieved by the proposed algorithm, and it has fewer regulation parameters, faster convergence speed and avoidance of local optimal solution. Therefore, the proposed algorithm has better application value in the optimization of SRM.

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