The use of particle swarm optimization for dynamical analysis in chemical processes

Particle swarm optimization is employed here to evaluate the parametric regions where different dynamic phenomena (periodic oscillations, double-period oscillations, chaos) can be expected in dynamic models. The proposed algorithm comprises two fundamental steps: the rough evaluation of regions where the desired solutions can be found and solution refining. The refining step allows the search for unstable solutions that may coexist with the other stable attractors. No preliminary bifurcation analysis is required. Simulations performed for distinct dynamic models show that the proposed algorithm is indeed able to locate different dynamic phenomena in the parameter space and that the algorithm may be of help for those interested in increasing the speed of more traditional dynamic bifurcation analysis.