Our method addresses this by integrating trigonometric, perturbation, and random operators-akin to the behavior of onlookers, employed, and scout bees-to explore and optimize the PES. This approach not only seeks the global minimum but also mitigates the risk of being trapped in local minima. Additionally, it employs techniques designed for full-dimensional problem spaces to provide a viable alternative to traditional methods. The method uses a two-phase approach which creates initial population space in the first phase and improves upon it in the second phase. It has been successfully applied to predict the global minimum structures of cluster assemblies both in the gas phase and on surfaces. In the first phase, pyGlobOpt software initializes the cluster population and then passes to a second phase for improvement (see Figure ). We employ a coarse optimization method to pre-relax each generated structure, helping to stabilize the system and offering close approximations that simplify local optimization, ultimately making the global optimization process more efficient (see Methodology section for details). Combining these strategies ensures that the method not only identifies promising candidates but also refines them efficiently, ultimately leading to a more accurate prediction of the global minimum structures.