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

Authors:
Sultan Noman Qasem;Siti Mariyam Shamsuddin;Siti Zaiton Mohd Hashim;Maslina Darus;Eiman Al-Shammari
Affiliations:
Computer Science Department, College of Computer and Information Sciences, Al-Imam Muhammad ibn Saud Islamic University, Riyadh, Saudi Arabia and Computer Science Department, Faculty of Applied Sc ...;Soft Computing Research Group, Faculty of Computer Science and Information System, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia;Soft Computing Research Group, Faculty of Computer Science and Information System, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia;School of Mathematical Sciences, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, Malaysia;Information Science Department, College of Computing Sciences and Engineering, Kuwait University, Kuwait
Venue:
Information Sciences: an International Journal
Year:
2013

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Abstract

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.