Hysteresis model identification by the Flock-of-Starlings Optimization

This paper presents the identification of the Jiles-Atherton and Preisach hysteresis models by means of a new heuristic: the Flock-of-Starlings Optimization (FSO). The FSO can be classified as an artificial life algorithm since it takes inspiration from the Particle Swarm Optimization (PSO) and from recent naturalistic observations on real flocks of common little European birds (starlings, Sturnus Vulgaris), performed by M. Ballerini et al. The one-to-one correspondence between the real flight of starlings searching food and the virtual flight of candidate solutions searching global optima is the core of the algorithm. Validations and comparisons with other heuristics have shown that the FSO gives good performances especially in those cases in which the solution space has a huge dimension. In fact, from the analysis of the obtained results by testing the FSO on hysteresis model identification, this heuristic has shown to be very attractive in comparison with other famous heuristics.