Chaotic PSO and its application in modeling of circulating fluidized bed boiler combustion system

Particle Swarm optimization (PSO) is a stochastic computational evolutionary technique which is based on intelligence of swarms but its convergence and search accuracy still need to be improved. Chaotic search, average initiate particle location, variable search zone, anti-prematurity strategy, inflection algorithm and changeable momentum weights were combined with PSO algorithm in order to solve these problems. This modified chaotic PSO was used to identify values of the transfer function on a circulation fluidized bed boiler combustion system. Simulation result shows that combining analytical, intelligent and experimental approach is an effective and practicable modeling approach.