The geometry optimization involves minimization of the energy with respect to each independent geometrical parameter. While in the case of small molecules, this procedure is very likely to find the global minimum, in larger systems this is not always the case and the optimized structure often depends on the starting structure. In large systems with many degrees of freedom or materials with numerous ways of arranging and packing their structural units, such as supported nanoclusters and large molecular assemblies, finding the global minimum is not straightforward. In these cases, it is also beneficial to know energetically adjacent structures that can become relevant at experimentally relevant conditions. In contrast, a global optimization should be able to find the global minimum (GM) regardless of the chosen starting point. However, the lack of a mathematical condition that defines the global minimum often makes such a task challenging. As a result, a stochastic algorithm is required to solve these types of problems.