An improved PSO based back propagation learning-MLP (IPSO-BP-MLP) for classification

Abstract

Although PSO has been successfully used in much application, the issues of trapping in local optimum and premature convergence can be avoided by using improved version of PSO (IPSO) by introducing new parameter called inertia weight. The IPSO is based on the global search properties of the traditional PSO and focuses on the suitable balance of the investigation and exploitation of the particles in the swarm for effective solution. During IPSO iterations, with increase in possible generations, the search space is decreased. Motivated from successful use of IPSO in many applications, in this paper, it is an attempt to design a MLP classifier with a hybrid back propagation learning based on IPSO. The proposed method has been tested using benchmark dataset from UCI machine learning repository and performances are compared with MLP, GA based MLP and PSO based MLP.