Abstract
Stochastic global optimization refers an iterative procedure in attempt to find the global optima in the parameter space when the objective function can be estimated with noise. Due to the noise inherent in the objective value, the problem is difficult to be solved, especially when the time given to solve the problem is limited, which is usually the case in practice. In this research, we propose a framework that allows the stochastic global optimization problem to be solved efficiently. The proposed framework sequentially builds a Kriging metamodel based on kernel density estimation for predicting the functional behavior of the objective function and solves for the optimal solution of the metamodel. Numerical experiments show that its efficiency is satisfactory.