Global optimization methods have seen several decades of applications to a wider range of problems. The most common algorithms are characterized into two groups. Individual-based methods, that explore PES by changing a single cluster (e.g. basin hopping algorithm, 4,5 simulated annealing, Monte Carlo ) and population-based methods, which explore the PES by manipulating a set of clusters (e.g. swarm intelligence and genetic algorithms). The latter often performs optimization by modeling natural processes such as evolution and animal behaviors. In most practical cases, there is essentially no way to guarantee that the entire single algorithm is universally adequate to resolve all optimization problems. For this reason, different types of algorithms as well as their modified or hybrid versions are still being explored. The global optimization method presented in this work shares similarities with swarm intelligence algorithms, particularly the artificial bee colony (ABC) algorithm. The ABC algorithm 17 is inspired by the collaborative and communicative behavior of bees within a hive, offering a robust and efficient optimization approach, especially for problems characterized by complex, multimodal search spaces like potential energy surfaces (PES). While the theoretical guarantees of success provided by ABC are compelling, the computational effort required to achieve these guarantees is often prohibitive in practice.