Usually, the tuning of BP in the CG models is performed manually, by repeating simulations of the CG system until the agreement between CG and AA-mapped models is deemed satisfactory. Provided that a well-sampled AA-MD reference trajectory is available, the bonded parametrization of CG models is essentially an optimization problem, which requires to iterate CG-MD while the BP are optimized. To free molecular modelers from manually performing this time-consuming operation, Swarm-CG is built around a state-of-the-art metaheuristic, Fuzzy-Self Tuning Particle Swarm Optimization (FST-PSO), which is employed to iteratively tune the BP to improve geometrical features of the CG model throughout successive CG-MD simulations. At each iteration step (i.e., in each successive CG-MD run), a scoring function evaluates the current set of BP by comparing the resulting geoms distributions with those of the AA-mapped reference trajectory. After a defined number of iteration steps, the best matching set of BP is selected. The workflow implemented in Swarm-CG is summarized in Figure . The following sections describe the protocol and heuristics implemented in Swarm-CG that allowed to make the software parameters-free and versatile to deal with different modeling requirements and designs. Iterative CG-MD simulations are performed, while at each iteration Swarm-CG, starting from a "swarm particle" (a set of BP), changes the BP to optimize the consistency with the reference AA-MD trajectory. The resulting set of CG bond parameters is then obtained as the output.