To refine BP of CG models, Swarm-CG relies on FST-PSO, a recently introduced PSO variant. FST-PSO exploits fuzzy logic to dynamically adjust PSO settings independently for each particle during optimization, making it a more efficient, parameters-free and versatile PSO variant. Nonetheless, the performance of all PSO algorithms is greatly affected by the initial positioning of the swarm in the search space. If the initial candidate solutions are positioned close to the basin of attraction of a local minimum of the objective function, the swarm might converge prematurely and be unable to move out of that region. To systematically achieve global optimization while minimizing execution times, Swarm-CG uses an iterative procedure that include 3 successive optimization cycles calibrated to complement each other (Figure ). Notably, BP of the CG model are optimized from higher to lower geoms vibrational frequencies. Exclusively bonds and angles are tuned in cycle 1. Angles and dihedrals are then optimized in cycle 2. Finally, all parameters are refined altogether in optimization cycle 3 (see Table ). Accordingly, the scoring function is adapted for each cycle to include relevant components exclusively. Swarm-CG also calibrates each initialization of the swarm of particles to maximize FST-PSO performances. At start of cycle 1, initialization is performed using a single-pass BI (cf. SI section 1.1) to guess BP of the CG model, for one swarm particle, which is used as a reference to generate variations and initialize the rest of the swarm. At start of cycles 2 and 3, the best set of BP obtained in previous cycles is chosen as a reference particle to generate the rest of the swarm. For each bonded parameter, variations around the reference particle are generated randomly within adaptive ranges, which are decreased as the procedure progresses through optimization cycles (Table ). Adaptive ranges also take into account the EMD previously obtained for each pairwise CG and AA-mapped geoms distributions, which directs the optimization procedure towards reducing first the largest discrepancies between models (cf. SI section 1.2). The two first optimization cycles allow a quick exploration of relevant sets of BP using short simulation times (10 ns by default), while the third optimization cycle uses longer simulation times to perform a final merging and refinement step (25 ns by default).