Self-adaptive migration NSGA and optimal design of inductors for magneto-fluid hyperthermia
ISSN: 0264-4401
Article publication date: 6 July 2018
Issue publication date: 23 July 2018
Abstract
Purpose
The purpose of this paper is to present a modified version of the non-dominated sorted genetic algorithm with an application in the design optimization of a power inductor for magneto-fluid hyperthermia (MFH).
Design/methodology/approach
The proposed evolutionary algorithm is a modified version of migration-non-dominated sorting genetic algorithms (M-NSGA) that now includes the self-adaption of migration events- non-dominated sorting genetic algorithms (SA-M-NSGA). Moreover, a criterion based on the evolution of the approximated Pareto front has been activated for the automatic stop of the computation. Numerical experiments have been based on both an analytical benchmark and a real-life case study; the latter, which deals with the design of a class of power inductors for tests of MFH, is characterized by finite element analysis of the magnetic field.
Findings
The SA-M-NSGA substantially varies the genetic heritage of the population during the optimization process and allows for a faster convergence.
Originality/value
The proposed SA-M-NSGA is able to find a wider Pareto front with a computational effort comparable to a standard NSGA-II implementation.
Keywords
Acknowledgements
The authors would like to thank Mr. Luca Bonin Aselt spa, VI, Italy, for the realization of the inductor.
Citation
Sieni, E., Di Barba, P., Dughiero, F. and Forzan, M. (2018), "Self-adaptive migration NSGA and optimal design of inductors for magneto-fluid hyperthermia", Engineering Computations, Vol. 35 No. 4, pp. 1727-1746. https://doi.org/10.1108/EC-05-2016-0186
Publisher
:Emerald Publishing Limited
Copyright © 2018, Emerald Publishing Limited