A Hybrid Optimization Algorithm for Electric Motor Design

  • Mokhtar Essaid
  • Lhassane Idoumghar
  • Julien Lepagnot
  • Mathieu Brévilliers
  • Daniel Fodorean
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10861)


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.


Electric motors Hybridization Cuckoo search CMAES 


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Mokhtar Essaid
    • 1
  • Lhassane Idoumghar
    • 1
  • Julien Lepagnot
    • 1
  • Mathieu Brévilliers
    • 1
  • Daniel Fodorean
    • 2
  1. 1.IRIMASUniversity of Haute-AlsaceMulhouseFrance
  2. 2.Department of Electrical Machines and DrivesTechnical University of Cluj-NapocaCluj-NapocaRomania

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