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A comparative study of stochastic optimization methods in electric motor design

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Abstract

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.

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Authors and Affiliations

  1. Department of Intelligent Systems, Jožef Stefan Institute, Jamova 39, SI-1000, Ljubljana, Slovenia

    Tea Tušar & Bogdan Filipič

  2. Department of Computer Systems, Jožef Stefan Institute, Jamova 39, SI-1000, Ljubljana, Slovenia

    Peter Korošec, Gregor Papa & Jurij Šilc

Authors
  1. Tea Tušar
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  2. Peter Korošec
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  3. Gregor Papa
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  4. Bogdan Filipič
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  5. Jurij Šilc
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Correspondence to Bogdan Filipič.

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Tušar, T., Korošec, P., Papa, G. et al. A comparative study of stochastic optimization methods in electric motor design. Appl Intell 27, 101–111 (2007). https://doi.org/10.1007/s10489-006-0022-2

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  • DOI: https://doi.org/10.1007/s10489-006-0022-2

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