A hybrid particle swarm optimization and its application in neural networks

In this paper, a novel particle swarm optimization model for radial basis function neural networks (RBFNN) using hybrid algorithms to solve classification problems is proposed. In the model, linearly decreased inertia weight of each particle (ALPSO) can be automatically calculated according to fitness value. The proposed ALPSO algorithm was compared with various well-known PSO algorithms on benchmark test functions with and without rotation. Besides, a modified fisher ratio class separability measure (MFRCSM) was used to select the initial hidden centers of radial basis function neural networks, and then orthogonal least square algorithm (OLSA) combined with the proposed ALPSO was employed to further optimize the structure of the RBFNN including the weights and controlling parameters. The proposed optimization model integrating MFRCSM, OLSA and ALPSO (MOA-RBFNN) is validated by testing various benchmark classification problems. The experimental results show that the proposed optimization method outperforms the conventional methods and approaches proposed in recent literature.

► The hybrid PSO algorithm outperforms well-known PSO algorithms on classification.
► The hybrid PSO algorithm can construct radial basis function neural networks.
► A PSO with automatic and linearly decreased inertia weight is proposed.