In this study, we introduce a new global optimization technique for a multi-dimensional unconstrained optimization problem. First, we present a new smoothing auxiliary function. Second, we transform the multi-dimensional problem into a one-dimensional problem by using an auxiliary function to reduce the number of local minimizers and then we find the global minimizer of the one-dimensional problem. Finally, we find the global minimizer of the multi-dimensional smooth objective function with the help of a new algorithm.