Particle swarm optimization based integrative optimal design method for surface motor with multi-degree of freedom

A method for an optimal design of a surface motor with multi-degree of freedom based on integrative optimization is developed in this paper. While the optimal design problem of a surface motor is formulated as a continuous optimization problem of design parameters, Particle Swarm Optimization (PSO) which is a population-based optimization technique that utilizes a population of individuals to search for an optimal solution, Radial Basis Function Network (RBFN) which is one of the multi-layered neural networks, and electromagnetic field simulation are used in the developed integrative optimization. While the developed approach is applied to an optimal design of a stator of a surface motor with multi-degree of freedom, the advantage of the developed approach is verified.