Analysis of a Nature Inspired Firefly Algorithm based Back-propagation Neural Network Training

TL;DR

This paper introduces a hybrid training method combining a firefly algorithm with back-propagation to enhance neural network training speed and convergence, outperforming genetic algorithm-based methods on standard datasets.

Contribution

It presents a novel hybrid approach integrating a firefly algorithm with back-propagation for faster neural network training convergence.

Findings

Faster convergence with fewer iterations.

Reduced training time compared to genetic algorithms.

Improved neural network performance with minimal design complexity.

Abstract

Optimization algorithms are normally influenced by meta-heuristic approach. In recent years several hybrid methods for optimization are developed to find out a better solution. The proposed work using meta-heuristic Nature Inspired algorithm is applied with back-propagation method to train a feed-forward neural network. Firefly algorithm is a nature inspired meta-heuristic algorithm, and it is incorporated into back-propagation algorithm to achieve fast and improved convergence rate in training feed-forward neural network. The proposed technique is tested over some standard data set. It is found that proposed method produces an improved convergence within very few iteration. This performance is also analyzed and compared to genetic algorithm based back-propagation. It is observed that proposed method consumes less time to converge and providing improved convergence rate with minimum feed-forward neural network design.