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

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.