A novel multi-surrogate multiobjective decision-making optimization algorithm in induction heating

• Marco Baldan ^[1] ; Alexander Nikanorov ^[1] ; Bernard Nacke ^[1]
  1. [1] University of Hannover

     University of Hannover

     Region Hannover, Alemania

     
• Localización: Compel: International journal for computation and mathematics in electrical and electronic engineering, ISSN 0332-1649, Vol. 39, Nº 1, 2020, págs. 144-157
• Idioma: inglés
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• Resumen

  • Most of optimal design or control engineering problems present conflicting objectives that need to be simultaneously minimized or maximized. Often, however, it is a priori known that some functions have greater importance than other. This paper aims to present a novel multi-surrogate, multi-objective, decisionmaking (DM) optimization algorithm, which is suitable for time-consuming simulations. Its performances have been compared, on the one hand with a standard decision-making algorithm (iTDEA), on the other with a self-adaptive evolutionary algorithm (AMALGAM*). The comparison concerns numerical tests and an optimal control task in induction heating.