Journal of Information Processing Systems

Korea Information Processing Society (한국정보처리학회)
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A Hybrid Bacterial Foraging Optimization Algorithm and a Radial Basic Function Network for Image Classification

  • Amghar, Yasmina Teldja (Dept. of Computer Science, Faculty of Mathematics and Computer Science, University of Sciences and Technology of Orann - Mohamed Boudiaf) ;
  • Fizazi, Hadria (Dept. of Computer Science, Faculty of Mathematics and Computer Science, University of Sciences and Technology of Orann - Mohamed Boudiaf)
  • Received : 2016.05.27
  • Accepted : 2017.02.09
  • Published : 2017.04.30
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Abstract

Foraging is a biological process, where a bacterium moves to search for nutriments, and avoids harmful substances. This paper proposes a hybrid approach integrating the bacterial foraging optimization algorithm (BFOA) in a radial basis function neural network, applied to image classification, in order to improve the classification rate and the objective function value. At the beginning, the proposed approach is presented and described. Then its performance is studied with an accent on the variation of the number of bacteria in the population, the number of reproduction steps, the number of elimination-dispersal steps and the number of chemotactic steps of bacteria. By using various values of BFOA parameters, and after different tests, it is found that the proposed hybrid approach is very robust and efficient for several-image classification.

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References

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