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

by Sudarshan Nandy, Partha Pratim Sarkar, Achintya Das
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 43 - Number 22
Year of Publication: 2012
Authors: Sudarshan Nandy, Partha Pratim Sarkar, Achintya Das
10.5120/6401-8339

Sudarshan Nandy, Partha Pratim Sarkar, Achintya Das . Analysis of a Nature Inspired Firefly Algorithm based Back-propagation Neural Network Training. International Journal of Computer Applications. 43, 22 ( April 2012), 8-16. DOI=10.5120/6401-8339

@article{ 10.5120/6401-8339,
author = { Sudarshan Nandy, Partha Pratim Sarkar, Achintya Das },
title = { Analysis of a Nature Inspired Firefly Algorithm based Back-propagation Neural Network Training },
journal = { International Journal of Computer Applications },
issue_date = { April 2012 },
volume = { 43 },
number = { 22 },
month = { April },
year = { 2012 },
issn = { 0975-8887 },
pages = { 8-16 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume43/number22/6401-8339/ },
doi = { 10.5120/6401-8339 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:33:58.357664+05:30
%A Sudarshan Nandy
%A Partha Pratim Sarkar
%A Achintya Das
%T Analysis of a Nature Inspired Firefly Algorithm based Back-propagation Neural Network Training
%J International Journal of Computer Applications
%@ 0975-8887
%V 43
%N 22
%P 8-16
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
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

References
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Index Terms

Computer Science
Information Sciences

Keywords

Neural Network Back-propagation Firefly Back-propagation Algorithms Meta-heuristic Back-propagation

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