In this work, we tested and validated different optimisation routines, namely particle swarm optimisation (PSO) , competitive particle swarm optimisation (CPSO) , differential evolution (DE) , covariance matrix adaptation -evolution strategy (CMA-ES) , and linear variant of covariance matrix adaptation (VD-CMA) . An overview of the algorithms used for the force field optimisation is given below. At the beginning of the optimisation, a swarm of multiple particles is generated in the model parameter space. The initial particle positions can be defined a priori or be determined by a (uniform) random distribution. Next, the fitness values of the individual particles are computed before updating the individual and global best values. Finally, the velocities v and positions x of the respective particles will be updated following