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Feed-Forward Neural Network Training by Hybrid Bat Algorithm

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Modelling and Development of Intelligent Systems (MDIS 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1341))

Abstract

Artificial neural networks are very powerful machine learning techniques and they are capable to solve complex problems. In the artificial neural network, one of the most difficult challenges is to find the optimal values of the weights during the learning process. To address this issue, we propose a new hybridized metaheuristic method, called BAABC for weight connection optimization. The experiments are performed on two binary classification datasets. The obtained results are compared to other similar approaches where other metaheuristics are used. The obtained results show that the proposed algorithm can find the optimal weight connection values and achieve higher performance and the proposed BAABC outperformed the other methods.

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Acknowledgement

The paper is supported by the Ministry of Education, Science and Technological Development of Republic of Serbia, Grant No. III-44006.

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Authors and Affiliations

  1. Singidunum University, Danijelova 32, 11000, Belgrade, Serbia

    Stefan Milosevic, Timea Bezdan, Miodrag Zivkovic, Nebojsa Bacanin, Ivana Strumberger & Milan Tuba

Authors
  1. Stefan Milosevic
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  2. Timea Bezdan
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  3. Miodrag Zivkovic
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  4. Nebojsa Bacanin
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  5. Ivana Strumberger
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  6. Milan Tuba
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Corresponding author

Correspondence to Nebojsa Bacanin .

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Editors and Affiliations

  1. Lucian Blaga University of Sibiu, Sibiu, Romania

    Dana Simian

  2. Lucian Blaga University of Sibiu, Sibiu, Romania

    Laura Florentina Stoica

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Milosevic, S., Bezdan, T., Zivkovic, M., Bacanin, N., Strumberger, I., Tuba, M. (2021). Feed-Forward Neural Network Training by Hybrid Bat Algorithm. In: Simian, D., Stoica, L.F. (eds) Modelling and Development of Intelligent Systems. MDIS 2020. Communications in Computer and Information Science, vol 1341. Springer, Cham. https://doi.org/10.1007/978-3-030-68527-0_4

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  • DOI: https://doi.org/10.1007/978-3-030-68527-0_4

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