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Artificial Neural Network Learning Enhancement using Bacterial Foraging Optimization Algorithm

by Raminder Kaur, Bikrampal Kaur
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 102 - Number 10
Year of Publication: 2014
Authors: Raminder Kaur, Bikrampal Kaur
10.5120/17852-8812

Raminder Kaur, Bikrampal Kaur . Artificial Neural Network Learning Enhancement using Bacterial Foraging Optimization Algorithm. International Journal of Computer Applications. 102, 10 ( September 2014), 27-33. DOI=10.5120/17852-8812

@article{ 10.5120/17852-8812,
author = { Raminder Kaur, Bikrampal Kaur },
title = { Artificial Neural Network Learning Enhancement using Bacterial Foraging Optimization Algorithm },
journal = { International Journal of Computer Applications },
issue_date = { September 2014 },
volume = { 102 },
number = { 10 },
month = { September },
year = { 2014 },
issn = { 0975-8887 },
pages = { 27-33 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume102/number10/17852-8812/ },
doi = { 10.5120/17852-8812 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:32:46.143969+05:30
%A Raminder Kaur
%A Bikrampal Kaur
%T Artificial Neural Network Learning Enhancement using Bacterial Foraging Optimization Algorithm
%J International Journal of Computer Applications
%@ 0975-8887
%V 102
%N 10
%P 27-33
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The artificial neural network (ANN) is a mathematical model capable of representing any non-linear relationship between input and output data. ANN is an abstract representation of the biological nervous system which has the ability to solve many complex problems. It has been successfully applied to a wide variety of classification and function approximation problems. The information processing capability of artificial neural networks (ANNs) is related to its architecture and weights. To have a high efficiency in ANN, selection of an appropriate architecture and learning algorithm is very important. In this study, the adaption of neural network connection weights using Bacterial Foraging Optimization Algorithm (BFO) is proposed as a mechanism to improve the performance of Artificial Neural Network in classification of Software Defect Dataset. The problem concerns the classification of software as defective or non defective on the basis of software metrics data. The results show that BFO-ANNs have better accuracy than traditional ANNs. The experimental results showed that BFOA-ANN has an improvement of 2. 55 % in software defect prediction accuracy than the original feed forward artificial neural network and 2. 80 % in case of cascade forward neural network.

References
  1. Zhang Chunkai, Huihe Shao, Yu Li, "Particle swarm optimisation for evolving artificial neural network", IEEE International Conference on Systems, Man and Cybernetics, Volume 4, 2000.
  2. R. Vijay, "Intelligent Bacterial Foraging Optimization Technique to Economic Load Dispatch Problem", International Journal of Soft Computing and Engineering, Volume 2, Issue 2, May 2012.
  3. Hanning Chen, Yunlong Zhu, Kunyuan Hu, "Adaptive Bacterial Foraging Optimization", Hindawi Publishing Corporation , Abstract and Applied Sciences, Volume 2011, Article ID 108269, 27 Pages
  4. Swagatam Dass, Arijit Biswas, Sambarta Dasgupta, Ajith Abraham, "Bacterial Foraging Optimisation Algorithm: Theoretical Foundations, Analysis and Applications", Foundation of Computer Intelligence, Vol. 3 SCI 203, pp. 23-55
  5. Vipul Sharma, SS. Pattnaik, Tanuj Garg, "A Review of Bacterial Foraging Optimization And its Application", National Conference on Future Aspects of Artificial Intelligence in Industrial Automation (NCFAAIIA 2012), Proceedings Published by International Journal of Computer Applications
  6. Riya Mary Thomas, "Survay of Bacterial Foraging Optimization Algorithm", International Journal of Science and Modern Engineering, Volume 1 Issue 4, March 2013
  7. Zhang Dan, "Improving the accuracy in software effort estimation using artificial neural network model based on particle swarm optimization", IEEE International conference on Service Operations and Logistics and Informatics, 2013.
  8. Kewin Li, Jisong Kou, Lina Gong, "Predicting Software Quality by Optimised BP Network Based on PSO", Journal of computer Science, Vol. 6, No. 1, January 2011.
  9. Abdul Hamed, Haza Nuzly, "Particle Swarm Optimization for neural network learning enhancment", Masters Thesis,University Technology Malaysia , 2006.
  10. Kayarvizhy N, Kanmani S, Rhymend Uthariaraj V, "Improving Fault prediction using ANN-PSO in object oriented systems", International Journal of computer Application (0975-8887) Volume 73, No. 3, July 2013
  11. Al-Qasem Al-Hadi, Ismail Ahmed, "Bacterial foraging optimization Algorithm for neural network learning enhancement", Master's Thesis, University Technology Malaysia. , 2011
  12. Ismail Ahmed Al-Qasem Al-Hadi, Siti Zaiton Mohamed Hashim, "Bacterial Foraging Optimization Algorithm for Neural Network Learning Enhancement", International Journal of Innovative Computing,Vol. 1, No. 1, 2012.
  13. Alireza Askarzadeh, "Artificial Neural network training using a new efficient optimization algorithm", Applied Soft Computing, Volume 13, Issue 2, February 2013, Pages 1206-1213.
  14. Solmaz Farshidpur, Farshid Keynia, "Using artificial bee colony Algorithm for MLP Training on software Defect Prediction", Oriental Journal of Computer science & Technology, Vol. 5 No. (2) Pgs. 231-239 December 2012
  15. Yan-Peng Liu, Ming-Guang Wu, Ji-Xin Qian, "Evolving Neural Networks Using the Hybrid of Ant Colony Optimization and BP Algorithms" ISNN 2006, LNCS 3971, pp. 714-722, 2006.
  16. Amir Hatampur, Rasul Razmi,Mohammad Hossein Sedaghat, "Improving Performance of a neural network model by Artificial Ant colony Optimization for Predicting Permeability of Petroleum Reservoir Rocks" Middle-East Journal of Scientific Research 13(9): 1217-1223, 2013.
  17. John Paul T. Yusiong, "Optimizing Artificial Neural Networks using Cat Swarm Optimization Algorithm" I. J. Intelligent Systems and Applications, 2013, 01, 69-80.
  18. Jasmina Arifovic, Ramazan Gencay, "Using Genetic Algorithm to select architecture of a feedforward artificial neural network", Physica A 289 (2001) 574-594
  19. Sachin Sharma, Dr. Savita Shiwani, "Data Mining Based Accuracy Enhancement Of ANN Using Swarm Intelligence", International Journal of Communication and Computer Technologies, Volume 2, No. 9 Issue: 05 June 2014.
  20. Shafaatunnur Hasan, Tan Swee Quo, Siti Mariyam Shamsuddin, Roselina Sallehuddin, "Artificial Neural Network Learning Enhancement Using Artificial Fish Swarm Algorithm" Proceedings of the 3rd International Conference on computing and Informatics, ICOCI 2011, 8-9 June, 2011 Bandung, Indonesia.
  21. Ahmad AL Kawam, Nashat Mansour, "Metaheuritic Optimization Algorithms for Training Artificial Neural Networks", International Journal of Computer and Information Technology Volume 01-Issue 02, November 2012.
  22. Jiawei Han, Micheline Kamber, Jian Pei, "Data Mining Concepts and Techniques"
  23. http://promisedata. googlecode. com/svn/trunk/defect/ant-1. 7/ant-1. 7. csv
Index Terms

Computer Science
Information Sciences

Keywords

Artificial Neural Network Bacterial Foraging Optimization algorithm swarm intelligence

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