In this talk, we explore the application of metaheuristics through large systems of interacting particles to address complex optimization challenges, with a particular focus on the Particle Swarm Optimization (PSO) method. This approach harnesses the power of collective intelligence, where individual particles adjust their movement based on personal success and the influence of their neighbours, guiding the swarm towards the optimal solution. We will investigate the continuous model developed by Grassi and Pareschi, presenting evidence of how it ensures convergence to global minimizers and connects to Consensus-based Optimization (CBO) through the limit of zero inertia.