An efficient global optimization algorithm based on augmented radial basis function

In the structural optimization, the accuracy of approximation for the established mathematical model will directly affect the solution efficiency, even the convergence. The global optimization model based on the augmented Gaussian radial basis function h as a high approximation accuracy, but the solution efficiency will not be increased without a matched optimization algorithm. In this paper, we adopt the information at the interpolating points in large extent and the augmented Gaussian radial basis function to construct the approximate mathematical model. Using the explicit derivatives of the model for the sensitivities and sequential quadratic programming (SQP) algorithm for the optimization solving, an efficient algorithm of global optimization is proposed. It is simple to be realized and converges quickly. Two examples will illustrate the stability and efficiency of the present algorithm.