Memetic multiobjective particle swarm optimization-based radial basis function network for classification problems

• This paper presents a new multiobjective swarm hybrid learning algorithm, MPSON.
• We also propose MGAN to comparison with MPSON based on performance measures.
• The aim of our algorithms is to obtain simple and accurate RBF networks simultaneously.
• We validate the algorithms on datasets with different features, samples and classes.
• Results show MPSON is the best algorithm with the accuracy-complexity balance for RBF network design.

This paper presents a new multiobjective evolutionary algorithm applied to a radial basis function (RBF) network design based on multiobjective particle swarm optimization augmented with local search features. The algorithm is named the memetic multiobjective particle swarm optimization RBF network (MPSON) because it integrates the accuracy and structure of an RBF network. The proposed algorithm is implemented on two-class and multiclass pattern classification problems with one complex real problem. The experimental results indicate that the proposed algorithm is viable, and provides an effective means to design multiobjective RBF networks with good generalization capability and compact network structure. The accuracy and complexity of the network obtained by the proposed algorithm are compared with the memetic non-dominated sorting genetic algorithm based RBF network (MGAN) through statistical tests. This study shows that MPSON generates RBF networks coming with an appropriate balance between accuracy and simplicity, outperforming the other algorithms considered.