Optimizing Connection Weights in Neural Networks Using Hybrid Metaheuristics Algorithms

Rabab Bousmaha,Reda Mohamed Hamou,Abdelmalek Amine

doi:10.4018/ijirr.289569

Abstract

The learning process of artificial neural networks is an important and complex task in the supervised learning field. The main difficulty of training a neural network is the process of fine-tuning the best set of control parameters in terms of weight and bias. This paper presents a new training method based on hybrid particle swarm optimization with Multi-Verse Optimization (PMVO) to train the feedforward neural networks. The hybrid algorithm is utilized to search better in solution space which proves its efficiency in reducing the problems of trapping in local minima. The performance of the proposed approach was compared with five evolutionary techniques and the standard momentum backpropagation and adaptive learning rate. The comparison was benchmarked and evaluated using six bio-medical datasets. The results of the comparative study show that PMVO outperformed other training methods in most datasets and can be an alternative to other training methods.

Highlights

Artificial neural network (ANN) is one of the most important data mining techniques
This paper presents a new training method based on hybrid particle swarm optimization with multi-verse optimization (PMVO) to train the feedforward neural networks
This paper presents a new training approach based on particle swarm optimization (PSO) with Multi-Verse Optimization MVO, called PMVO, to train the feedforward neural network (FFNN)

Summary

INTRODUCTION

Artificial neural network (ANN) is one of the most important data mining techniques. It has been successfully applied to many domains. Backpropagation (BP) and its variants are gradient-based methods and considered as one of the most popular techniques used to train the MLP neural network. Biases and the tendency to be trapped in local minima(Zhang, Zhang, Lok, & Lyu,2007).To address these problems, stochastic search methods, such as metaheuristics have been proposed as alternative methods for training feedforward neural network. We propose a new training algorithm based on hybrid Particle Swarm Optimization (PSO) with Multi-Verse Optimization (MVO) to train MLP neural networks. Training MLP is an optimization problem that varies for each dataset (Faris et al, 2016) Based on those reasons, this paper presents a new training approach based on particle swarm optimization (PSO) with Multi-Verse Optimization MVO, called PMVO, to train the feedforward neural network (FFNN). Friedman statistical test shows that the proposed training algorithm outperforms other training algorithms

RELATED WORK

RESULTS

CONCLUSION