Inverse shape optimization of a pole face of rotating machines using dynamically adjustable genetic algorithms

In this paper, a new dynamically adjustable genetic algorithm for inverse pole face optimization of rotating machines is proposed. The algorithm starts with initial population which is not entirely randomly defined and dynamically changes the position and the width of the searching space as the searching procedure evolves with time and the objective function approaches its optimum. The proposed algorithm is successfully applied for inverse shape optimization of a pole shape of a rotating machine. To achieve smooth pole face its shape is defined using several control points and ordinary spline functions.