Swarm intelligence is a algorithmic methodology that mimics the behavior of nature-inspired, multi-participant, coordinated systems. Such systems include ants, 2 bees, fireflies, bacteria, and spider monkeys. In practice, swarm intelligence has been successfully applied to the investigation of multiple different processes such as protein folding, 7 mono-and polyatomic cluster optimization, adsorption, and transition path searches. In this work, we use a swarm intelligence algorithm that mimics the behavior of bees foraging for nectar, known as the artificial bee colony (ABC) algorithm, to perform a global optimisation (GO) of our chosen system. This algorithm is used to optimize an objective function with respect to certain independent variables. It has previously been applied to job scheduling, drug design, and yeast fermentation pathways. For our purposes, we choose the objective function to be the potential energy and a function representing a configuration of atomic positions to be the independent variables. This is achieved practically by optimising the total energy with respect to collective variables of the atomic positions that represent the location and orientation of each cluster in the system. Then, all atomic positions are relaxed in a computational chemistry code.