Self-adaptive NGSA algorithm and optimal design of inductors for magneto-fluid hyperthermia. Issue 2 (6th March 2017)

• Record Type: Journal Article
• Authors: Di Barba, Paolo; Dughiero, Fabrizio; Forzan, Michele; Sieni, Elisabetta
• Is Part Of: Compel. Volume 36:Issue 2(2017)
• Page Start: 535
• Page End: 545
• Publication Date: 2017-03-06
• Subjects: Magnetic fields -- Optimal design -- Multi-objective optimization -- Finite element analysis -- Genetic algorithms -- Pareto optimization; Electrical engineering -- Data Processing -- Periodicals; Electrical engineering -- Mathematics -- Periodicals; Electrical engineering -- Periodicals; Electronics -- Data Processing -- Periodicals; Electronics -- Mathematics -- Periodicals; 621.3
• DOI: 10.1108/COMPEL-05-2016-0188
• Languages: English
• ISSNs: 0332-1649

Abstract

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.