Improved solution to a multi-objective benchmark problem of inverse induction heating

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.