Hybrid Multi-Objective Optimization Algorithm for PM Motor Design

被引：0
|
作者：
Krasopoulos, Christos T. [1 ]
Armouti, Ioanna P. [1 ]
Kladas, Antonios G. [1 ]
机构：
[1] Natl Tech Univ Athens, Dept Elect & Comp Engn, Athens, Greece
来源：
2016 IEEE CONFERENCE ON ELECTROMAGNETIC FIELD COMPUTATION (CEFC) | 2016年
关键词：
Electric vehicles; finite element method; optimization techniques; permanent magnet motors;
D O I：
暂无
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
This paper proposes an innovative hybrid multiobjective (MO), fast converging optimization algorithm, enabling global optimum tracking in permanent magnet traction motor design. The methodology developed is based on Artificial Bee Colony (ABC) technique, strength pareto evolutionary algorithm (SPEA2) and Differential Evolution (DE) strategy ensuring fast and reliable convergence to the optimal Pareto front. An application example concerning an unequal teeth surface mounted permanent magnet wheel motor design is given.
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