Applied Intelligence

, Volume 27, Issue 2, pp 101–111 | Cite as

A comparative study of stochastic optimization methods in electric motor design

  • Tea Tušar
  • Peter Korošec
  • Gregor Papa
  • Bogdan Filipič
  • Jurij Šilc


The efficiency of universal electric motors that are widely used in home appliances can be improved by optimizing the geometry of the rotor and the stator. Expert designers traditionally approach this task by iteratively evaluating candidate designs and improving them according to their experience. However, the existence of reliable numerical simulators and powerful stochastic optimization techniques make it possible to automate the design procedure. We present a comparative study of six stochastic optimization algorithms in designing optimal rotor and stator geometries of a universal electric motor where the primary objective is to minimize the motor power losses. We compare three methods from the domain of evolutionary computation, generational evolutionary algorithm, steady-state evolutionary algorithm and differential evolution, two particle-based methods, particle-swarm optimization and electromagnetism-like algorithm, and a recently proposed multilevel ant stigmergy algorithm. By comparing their performance, the most efficient method for solving the problem is identified and an explanation of its success is offered.


Electric motor Geometry parameters Power losses Numerical simulation Stochastic optimization Empirical study 


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  1. 1.
    ANSYS User’s Manual (2000) ANSYS version 5.6. ANSYS Inc. Canonsburg, PAGoogle Scholar
  2. 2.
    Birbil SI, Fang SC (2003) An electromagnetism-like mechanism for global optimization. J Global Optim 25(3):263–282zbMATHCrossRefGoogle Scholar
  3. 3.
    Dorigo M (1992) Optimization, learning and natural algorithms. Ph.D. Thesis, Dipartimento di Elettronica, Politecnico di Milano, Milan, Italy (in Italian)Google Scholar
  4. 4.
    Dorigo M, Maniezzo V, Colorni A (1996) The ant system: Optimization by a colony of cooperating agents. IEEE Trans Syst, Man, and Cybern–Part B 26(1):1–13Google Scholar
  5. 5.
    Dorigo M, Di Caro G, Gambardella LM (1999) Ant algorithms for discrete optimization. Artif Life 5(2):137–172CrossRefGoogle Scholar
  6. 6.
    Eberhart RC, Kennedy J (1995) A new optimizer using particle swarm theory. In: Proceedings of the 6th international symposium micro machine and human science, Nagoya, Japan, pp 39–43Google Scholar
  7. 7.
    Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning. Addison-Wesley, Reading, MAzbMATHGoogle Scholar
  8. 8.
    Holland JH (1975) Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor, MIGoogle Scholar
  9. 9.
    Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: Proceedings of the IEEE international conference neural networks. Perth, Australia, pp 1942–1948Google Scholar
  10. 10.
    Korošec P, Šilc J (2005) The multilevel ant stigmergy algorithm: An industrial case study. In: Proceedings of the 7th international conference on computational intelligence and natural computing. Salt Lake City, UT, pp 475–478Google Scholar
  11. 11.
    Korošec P, Papa G, Šilc J (2005) Optimization algorithms inspired by electromagnetism and stigmergy in electro-technical engineering. WSEAS Trans Inf Sci Appl 5(2):587–591Google Scholar
  12. 12.
    Papa G, Koroušić-Seljak B, Benedičič B, Kmecl T (2003) Universal motor efficiency improvement using evolutionary optimization. IEEE Trans Ind Electron 50(3):602–611CrossRefGoogle Scholar
  13. 13.
    Price KV, Storn R (1997) Differential evolution—a simple evolution strategy for fast optimization. Dr. Dobb’s J 22(4):18–24Google Scholar
  14. 14.
    Price KV, Storn R, Lampinen JA (2005) Differential evolution: a practical approach to global optimization. Springer-Verlag, Secaucus, NJzbMATHGoogle Scholar
  15. 15.
    Puternicki P, Rudnicki M (1999) Optimal design methodologies with application to small commutator motors. In: Proceedings of the international symposium on electromagnetic fields in electrical engineering, Pavia, Italy, pp 397–400Google Scholar
  16. 16.
    Robič T, Filipič B (2004) In search for an efficient parameter tuning method for steel casting. In: Proceedings of the international conference on bioinspired optimization methods and their applications. Ljubljana, Slovenia, pp 83–94Google Scholar
  17. 17.
    Sen PC (1996) Principles of electric machines and power electronics. John Wiley & Sons, New YorkGoogle Scholar
  18. 18.
    Shaked NT (2004) Optimization of switched reluctance motors using genetic algorithms. M.Sc. Thesis, Ben-Gurion University of the Negev, Beer Sheva, IsraelGoogle Scholar
  19. 19.
    Shi Y, Eberhart RC (1998) A modified particle swarm optimizer. In: Proceedings of the IEEE international conference evolutionary computation. Anchorage, AK, pp 69–73Google Scholar
  20. 20.
    Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1):67–82CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2006

Authors and Affiliations

  • Tea Tušar
    • 1
  • Peter Korošec
    • 2
  • Gregor Papa
    • 2
  • Bogdan Filipič
    • 1
  • Jurij Šilc
    • 2
  1. 1.Department of Intelligent SystemsJožef Stefan InstituteLjubljanaSlovenia
  2. 2.Department of Computer SystemsJožef Stefan InstituteLjubljanaSlovenia

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