Shape optimization of magnetic devices using Genetic Algorithms with dynamically adjustable parameters

An improved method for inverse shape optimization of magnetic devices using the Genetic Algorithms(GAs) with dynamically adjustable parameters is presented. The proposed method starts from an initial population using large number of bits per chromosome enabling searching for the optimal solution in a wider region without aggravating the computational speed. Later, as the optimization process evolves, the searching space is gradually decreased by restriction of the number of bits and by translation and reduction of the searching space according to the values of the objective function, therefore, dynamically adjusting to the best fit solution decreasing the computation resources to a minimum. The obtained results exhibit acceleration of the optimization process and increase of the solution accuracy.