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A Novel Artificial Bee Colony Learning System for Data Classification

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Advances in Computing Systems and Applications (CSA 2018)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 50))

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Abstract

Training artificial neural networks (ANNs) is a common hard optimization problem. The process of neural nets training is generally defined on synaptic weights and thresholds of artificial neurons with the aim to find optimal or near-optimal values. Artificial bee colony (ABC) optimization has been successfully applied to several optimization problems, including the optimization of weights and biases of ANNs. This paper addresses the problem of feed-forward ANNs training by using a novel ABC variant named cooperative learning artificial bee colony algorithm (CLABC), which we have developed in our previous work. The performance of the CLABC-trained feed-forward ANN is validated on different classification problems, namely the XOR problem, the 3-bit parity, 4-bit encoder-decoder and Iris benchmark problems. The results are compared to other advanced optimization methods.

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Author information

Authors and Affiliations

  1. Applied Automation Laboratory, M’hamed Bougara University of Boumerdès, Av. de l’indépendance, Boumerdès, Algeria

    Fatima Harfouchi & Hacene Habbi

Authors
  1. Fatima Harfouchi
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  2. Hacene Habbi
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Corresponding author

Correspondence to Fatima Harfouchi .

Editor information

Editors and Affiliations

  1. Department of Computer Science, Ecole Militaire Polytechnique, Algiers, Algeria

    Oualid Demigha

  2. Department of Computer Science, Ecole Militaire Polytechnique, Algiers, Algeria

    Badis Djamaa

  3. Department of Computer Science, Ecole Militaire Polytechnique, Algiers, Algeria

    Abdenour Amamra

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Harfouchi, F., Habbi, H. (2019). A Novel Artificial Bee Colony Learning System for Data Classification. In: Demigha, O., Djamaa, B., Amamra, A. (eds) Advances in Computing Systems and Applications. CSA 2018. Lecture Notes in Networks and Systems, vol 50. Springer, Cham. https://doi.org/10.1007/978-3-319-98352-3_34

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