SELF-adaptive Migration-NSGA algorithm: An application in uncertainty-tolerant magnetic field synthesis for MFH inductor design

Article type: Research Article

Authors: Di Barba, Paolo^a | Dughiero, Fabrizio^b | Forzan, Michele^b | Sieni, Elisabetta^{b; *}

Affiliations: [a] Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy | [b] Department of Industrial Engineering, University of Padova, Padova, Italy

Correspondence: [*] Corresponding author: Elisabetta Sieni, Department of Industrial Engineering, University of Padova, Via Gradenigo 6/A, 35131 Padova, Italy. Tel.: +39 049 8277514; Fax: +39 049 8277599; E-mail: elisabetta.sieni@unipd.it.

Abstract: In the paper the optimal synthesis of the magnetic field in a device for Magneto-Fluid Hyperthermia (MFH) tests on cell cultured in Petri dishes is presented. Magneto-Fluid Hyperthermia is a cancer therapy that exploits magnetic fluid and time-harmonic magnetic field to heat nanoparticles localized in the target region. To this end, a modified version of Non-Dominated Sorting Genetic Algorithm (NSGA-II) is presented. The proposed optimization algorithm includes the periodic migration of a new population that enhances the genetic heritage of the current population. The convergence of the algorithm is automatically controlled by applying a suitable ‘stop criterion’. The optimal design of a class of power inductors for MFH is considered.

Keywords: Multiobjective optimization, genetic algorithm, Pareto optimality, magnetic field, finite-element analysis

DOI: 10.3233/JAE-172283

Journal: International Journal of Applied Electromagnetics and Mechanics, vol. 56, no. S1, pp. 17-32, 2018