A Novel Adaptive Dynamic Taylor Kriging and Its Application to Optimal Design of Electromagnetic Devices

An adaptive dynamic Taylor Kriging (ADTK) is developed and combined with the particle swarm optimization algorithm to get a numerically efficient optimization strategy. For given sampling data, the ADTK always minimizes its fitting error without regard to a problem through optimal selection of basis functions among Taylor polynomials. An adaptive sampling method is also proposed based on the fitting error estimation, through which a minimum number of sampling data for a desired level of fitting error can be controlled. The proposed approaches are tested with an analytic function and the TEAM 25.