Self-adaptive NGSA algorithm and optimal design of inductors for magneto-fluid hyperthermia

Abstract

Purpose

This paper aims to present the optimal design of an inductor used to heat a magnetic nanoparticle fluid injected in a cell culture inside a Petri dish.

Design/methodology/approach

The inductor design is driven by means of a multi-objective optimization algorithm that generalizes the migration-non-dominated sorting genetic algorithm (NSGA); it is called self-adapting migration-NSGA.

Findings

The optimized device is able to synthesize a uniform magnetic field in a nanoparticle fluid, substantially helping its heating capability. The ultimate scope is to assist the cancer therapy based on magnetic fluid hyperthermia (MFH).

Originality/value

The optimal design of an inductor for MFH applications has been carried out by applying an improved version of migration-based NSGA-II algorithm including automatic stop and a self-adapting concept. The modified optimization algorithm is suitable to find better optimal solutions with respect to a standard version of NSGA-II.

Citation

Di Barba, P., Dughiero, F., Forzan, M. and Sieni, E. (2017), "Self-adaptive NGSA algorithm and optimal design of inductors for magneto-fluid hyperthermia", COMPEL - The international journal for computation and mathematics in electrical and electronic engineering, Vol. 36 No. 2, pp. 535-545. https://doi.org/10.1108/COMPEL-05-2016-0188