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Bacterial Foraging Optimization Algorithm for Evolving Artificial Neural Networks

Raminder Kaur, Bikrampal Kaur Published in Artificial Intelligence

International Journal of Applied Information Systems
Year of Publication: 2015
© 2013 by IJAIS Journal
10.5120/ijais15-451311
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  1. Raminder Kaur and Bikrampal Kaur. Article: Bacterial Foraging Optimization Algorithm for Evolving Artificial Neural Networks. International Journal of Applied Information Systems 8(5):16-19, March 2015. BibTeX

    @article{key:article,
    	author = "Raminder Kaur and Bikrampal Kaur",
    	title = "Article: Bacterial Foraging Optimization Algorithm for Evolving Artificial Neural Networks",
    	journal = "International Journal of Applied Information Systems",
    	year = 2015,
    	volume = 8,
    	number = 5,
    	pages = "16-19",
    	month = "March",
    	note = "Published by Foundation of Computer Science, New York, USA"
    }
    

Abstract

Artificial Neural Network (ANN) is a powerful artificial tool suitable for solving combinatorial problems such as prediction and classification. The performance of ANN is highly dependent upon its architecture and connection weights. To have a high efficiency in ANN, selection of an appropriate architecture and learning algorithm is very important. ANN learning is a complex task and efficient learning algorithm has a significant role to enhance its performance. The process of weight training is a complex continuous optimization problem. This paper deals with the application of swarm intelligence based algorithm, Bacterial Foraging Optimization (BFO) for training feed-forward and cascade-forward ANNs. BFO algorithm which is based on the foraging strategy of bacteria is adopted to train the connection weights and to evolve the ANN learning and accuracy. The experiments performed on dataset taken from promise repository verify the potential of BFO algorithm and showed that classification accuracy of BFO-ANN is more than the traditional ANN.

Reference

  1. Ismail Ahmed Al-Qasem Al-Hadi, Siti Zaiton Mohamed Hashim, “Bacterial Foraging Optimization for Neural Network Learning Enhancement”, International Journal of Innovation Computing, Vol. 1, No. 1, 2012.
  2. Jun Li,Jianwu Dang,Feng Bu, Jiansheng Wang, “Analysis and Improvement of the Bacterial Foraging Optimization Algorithm”, Journal of Computing Science and Engineering, Vol. 8, No. 1, March 2014, pp. 1-10.
  3. K.M Passino, “Biomimicry of bacterial foraging distributed optimization and control”, Control systems Magazine, IEEE, Vol. 22, pp. 52-67, 2002.
  4. Raminder Kaur, Bikrampal Kaur, “Artificial Neural Network Learning Enhancement using Bacterial Foraging Optimization Algorithm”, International Journal of Computer Applications, Volume 102, No. 10, September 2014.
  5. Yudong Zhang, Lenan WU, Shuihua WANG, “Bacterial foraging Optimization Based Neural Network for short-term Load forecasting,” Journal of computational Information Systems 6(7):2009-2105, 2010.

Keywords

Artificial Neural Networks, Bacterial Foraging Optimization Algorithm