Recursive particle swarm optimization applications in radial basis function networks modeling system

A novel strategy on particle swarm optimization is proposed to solve dynamic optimization problems, in which the data are obtained not once for all but one by one. The evolutionary states of the particle swarm are guided recursively by the proposed algorithm, according to the information achieved by the continuous data and the prior estimated knowledge on the solution space. The experimental results for three test functions show that radial basis function networks modeling system based on the proposed recursive algorithm requires fewer radial basis functions and gives more accurate results than other traditional improved PSO in solving dynamic problems.