Hybrid particle swarm optimization and group method of data handling for inductive modeling

This paper proposes a new design methodology which is based on hybrid of particle swarm optimization(PSO) and group method of data handling (GMDH). The PSO and GMDH are two well-known nonlinear methods of mathematical modeling. The proposed method constructs a GMDH network model of a population of promising PSO solutions. The new PSO-GMDH hybrid implementation is then applied to modeling and prediction of practical datasets and its results are compared with the results obtained by GMDH-related algorithms. Results presented show that the proposed algorithm appears to perform reasonably well and hence can be applied to real-life prediction and modeling problems.