Electromagnetic optimization based on an improved diversity‐guided differential evolution approach and adaptive mutation factor

• Leandro dos Santos Coelho ^[1] ; Piergiorgio Alotto ^[2]
  1. [1] Pontifícia Universidade Católica do Paraná

     Pontifícia Universidade Católica do Paraná

     Brasil

     
  2. [2] Universitá di Padova
• Localización: Compel: International journal for computation and mathematics in electrical and electronic engineering, ISSN 0332-1649, Vol. 28, Nº 5 (Special Issue: Selected Papers from OIPE 2008), 2009, págs. 1112-1120
• Idioma: inglés
• Enlaces
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• Resumen

  • Purpose – The purpose of this paper is to show, on a widely used benchmark problem, that adaptive mutation factors and attractive/repulsive phases guided by population diversity can improve the search ability of differential evolution (DE) algorithms.

    Design/methodology/approach – An adaptive mutation factor and attractive/repulsive phases guided by population diversity are used within the framework of DE algorithms.

    Findings – The paper shows that the combined use of adaptive mutation factors and population diversity in order to guide the attractive/repulsive behavior of DE algorithms can provide high‐quality solutions with small standard deviation on the selected benchmark problem.

    Research limitations/implications – Although the chosen benchmark is considered to be representative of typical electromagnetic problems, different test cases may give less satisfactory results.

    Practical implications – The proposed approach appears to be an efficient general purpose stochastic optimizer for electromagnetic design problems.

    Originality/value – This paper introduces the use of population diversity in order to guide the attractive/repulsive behavior of DE algorithms.