A significant challenge in materials science is predicting the stability of a hypothetical material before it is synthesized. To address this, one must assume that the native structure of the material corresponds to the global minimum on a highly nonlinear energy surface. The problem can be solved by accurately modeling the free energy with a potential energy function and employing a global optimization algorithm capable of finding the global minimum of this function. This work attempts to solve the latter problem, using perturbation, trigonometric, and random operators to search for the best solution of the potential energy surface (PES) while simultaneously smoothing it and preventing it from being trapped in the local minimum.