Vector shape optimization of an electrostatic micromotor using a genetic algorithm

The automated shape optimization of an electrostatic micromotor with radial field is tackled. Two objectives in mutual contrast, i.e. static torque and torque ripple, depending on two design variables, are considered. An innovative procedure for vector optimization, which aims at obtaining as many optimal solutions as possible, is presented. To this end, a non-dominated sorting genetic algorithm (NSGA) is set up, linking pareto optima theory and genetic algorithms. This way, 50 different optimal solution, each of which corresponds to a different degree of preference with respect to the single objectives.