Abstract
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.
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