Performance of Kriging and Cokriging based surrogate models within the unified framework for surrogate assisted optimization

Abstract

In this paper, we report the behavior of Kriging and Cokriging based surrogate models within the optimization framework. The framework is built upon a stochastic, zero order, population-based optimization algorithm embedded with controlled elitism to ensure convergence in the actual function space. The model accuracy is maintained via periodic retraining and the number of data points required to create the surrogate model is adaptively identified using Calinski Harabasz (CH) index. Results of Kriging and Cokriging are compared with Radial Basis Function models on a set of numerical and engineering design optimization problems.