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

ABSTRACT Optimization algorithms are normally influenced by metaheuristic 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 feedforward neural network. Firefly algorithm is a nature inspired meta-heuristic algorithm, and it is incorporated into backpropagation 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 backpropagation. It is observed that proposed method consumes less time to converge and providing improved convergence rate with minimum feed-forward neural network design .