A Hybrid Optimization Algorithm for Electric Motor Design

被引：1

作者：

Essaid, Mokhtar ^{[1
]}

Idoumghar, Lhassane ^{[1
]}

Lepagnot, Julien ^{[1
]}

Brevilliers, Mathieu ^{[1
]}

Fodorean, Daniel ^{[2
]}

机构：

[1] Univ Haute Alsace, IRIMAS, F-68093 Mulhouse, France

[2] Tech Univ Cluj Napoca, Dept Elect Machines & Drives, Cluj Napoca 400114, Romania

来源：

COMPUTATIONAL SCIENCE - ICCS 2018, PT II | 2018年 / 10861卷

关键词：

Electric motors; Hybridization; Cuckoo search; CMAES; DIFFERENTIAL EVOLUTION; GLOBAL OPTIMIZATION; STRATEGY;

D O I：

10.1007/978-3-319-93701-4_39

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

This paper presents a hybrid algorithm employed to reduce the weight of an electric motor, designed for electric vehicle (EV) propulsion. The approach uses a hybridization between Cuckoo Search and CMAES to generate an initial population. Then, the population is transferred to a new procedure which adaptively switches between two search strategies, i.e. one for exploration and one for exploitation. Besides the electric motor optimization, the proposed algorithm performance is also evaluated using the 15 functions of the CEC 2015 competition benchmark. The results reveal that the proposed approach can show a very competitive performance when compared with different state-of-the-art algorithms.

引用

页码：501 / 517

页数：17

共 20 条

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