Coupling of the evolution strategy algorithm and genetic algorithm with finite element mesh adaptation

• Krzysztof Komeza ^[2] ; Ewa Napieralska Juszczak ^[3] ; Piotr Napieralski ^[2] ; Paolo Di Barba ^[1]
  1. [1] University of Pavia

     University of Pavia

     Pavía, Italia

     
  2. [2] Technical University of Lodz
  3. [3] Universite Artois

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• Localización: Compel: International journal for computation and mathematics in electrical and electronic engineering, ISSN 0332-1649, Vol. 31, Nº 5 (Special Issue: Electromagnetic Fields in Electrical Engineering), 2012, págs. 1396-1407
• Idioma: inglés
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  • Purpose – The purpose of this paper is to find a more performing and automated procedure for linking an identification algorithm implemented in a general‐purpose environment with a commercial finite‐element code for magnetic field analysis. In particular, the use of a multiprocessor computer makes it possible to perform parallel computations keeping the calculation time reasonably low.

    Design/methodology/approach – The method is applied to identify the B‐H curve of anisotropic magnetic laminations in the direction normal to the sheet surface. In total, three different optimization methods have been applied. First an evolution strategy algorithm for solving the identification problem was used; then genetic algorithm (GA) was applied. The results obtained using different methods were compared and discussed. The computation time is reduced by adjusting the refinement of the FEM mesh.

    Findings – The key point has been the use of a derivative‐free and global‐search oriented algorithm. Even if a starting point far from the solution is chosen, a suitably large initial value of the search radius makes the convergence possible. The effect of the historical parameter of the minimization algorithm on convergence has also been investigated.

    Originality/value – The main new idea presented in this paper is equipping a GA‐based identification procedure with an additional objective function describing the sensitivity of the flux density against a small perturbation in parameters. This approach gives a multiple objective problem which introduces possibility of choosing a compromise solution among many optimal solutions instead of only one, as in classical GA optimization algorithm. The paper is mainly addressed to readers interested in the efficient use of GA‐based identification.