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:
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Corresponding author: Elisabetta Sieni, Department of Industrial Engineering, University of Padova, Via Gradenigo 6/A, Padova 35131, Italy. Tel.: +39 049 8277514; Fax: +39 049 8277599; E-mail:elisabetta.sieni@unipd.it
Abstract: In the paper, a new solution to a benchmark problem of inverse induction
heating is presented. The problem refers to an industrial device for the
controlled heating of a graphite disk. In particular, the solution of a
bi-objective optimization problem characterized by a multi-physics field
analysis is investigated. The solution strategy of the bi-objective
optimization problem is based on a modified multi-objective genetic
algorithm in the class of Non-dominated Sorting Genetic Algorithm. The
expected goal of the optimization process is to improve temperature
uniformity in the disk as well as electrical efficiency of the induction
device. The proposed algorithm exploits the migration concept to vary the
population genetic characteristics during optimization process, with the
final aim of improving the approximation of the Pareto front.
Keywords: Induction heating, multi-objective optimization, Pareto front, magnetic and thermal problem, multi-physics benchmark
