Distance based Intelligent Particle Swarm Optimization for Optimal Design of Permanent Magnet Synchronous Machine

被引：0
|
作者：
Lee, Jin-Hwan [1 ]
Song, Jun-Young [1 ]
Kim, Jong-Wook [2 ]
Kim, Yong-Jac [3 ]
Jung, Sang-Yong [1 ]
机构：
[1] Sungkyunkwan Univ, Sch Elect & Elect Engn, Suwon, South Korea
[2] Dong A Univ, Dept Elect Engn, Busan, South Korea
[3] Chosun Univ, Dept Elect Engn, Gwangju, South Korea
来源：
2016 IEEE CONFERENCE ON ELECTROMAGNETIC FIELD COMPUTATION (CEFC) | 2016年
关键词：
Optimal Design; Particle Swarm Optimization; Permanent Magnet Synchronous Machine;
D O I：
暂无
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
In this study, we propose a novel intelligent particle swarm optimization(PSO). In case of PSO, when particles approach to optimum point, particles roam near global optimum. Therefore, PSO takes unnecessary function call and long convergence time. In order to solve these problems, we propose distance based intelligent particle swarm optimization(DbIPSO). DbIPSO calculate distance of every particle and when distance is lower than assigned value, best particle absorbs near particles to reduce function call and convergence time. By applying proposed algorithm to optimal design of permanent magnet synchronous machine, we validate its effectiveness and performance.
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页数：1
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