Solving non-convex minimization problems using multi-particle metaheuristic, derivative-free optimization methods has recently gained a lot of interest. Popular approaches, such as Consensus-Based Optimization and Particle Swarm Optimization methods, iteratively update a population of particles based on dynamics inspired by social interactions. Due to their tractable analytic structure, these methods can be adapted to different scenarios, including constrained optimization problems. Furthermore, it is possible to exploit the hierarchical structure of these methods to introduce a micro-macro decomposition of them. A first approach is to write the probability density of particles as a convex combination of microscopic and macroscopic contributions, and then to evolve both parts separately. An alternative approach is to study the marginals of the particle distribution. Several simulations are performed to show the validity of the extensions of the presented metaheuristic methods. [1] M. Herty and S. Veneruso. Micro-macro decomposition of Constrained Particle Swarm Optimization Methods. Preprint arXiv:2501.10306, 2025.