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Harmony Search Algorithms for Optimizing Extreme Learning Machines

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Proceedings of 6th International Conference on Harmony Search, Soft Computing and Applications (ICHSA 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1275))

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Abstract

Extreme learning machine (ELM) is a non-iterative algorithm for training single-hidden layer feedforward neural network (SLFN). ELM has been shown to have good generalization performance and faster learning speed than conventional gradient-based learning algorithms. However, due to the random determination of the hidden neuron parameters (i.e., input weights and biases) ELM may require a large number of neurons in the hidden layer. In this paper, the original harmony search (HS) and its variants, namely, improved harmony search (IHS), global-best harmony search (GHS), and intelligent tuned harmony search (ITHS) are used to optimize the input weights and hidden biases of ELM. The output weights are analytically determined using the Moore–Penrose (MP) generalized inverse. The performance of the hybrid approaches is tested on several benchmark classification problems. The simulation results show that the integration of HS algorithms with ELM has obtained compact network architectures with good generalization performance.

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Notes

  1. 1.

    https://archive.ics.uci.edu/ml/.

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Acknowledgements

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. 2019R1A2B5B03069810).

Author information

Authors and Affiliations

  1. Research Institute for Mega Construction, Korea University, Seoul, South Korea

    Abobakr Khalil Al-Shamiri

  2. Department of Mechanical Engineering, University of Science and Culture, Tehran, Iran

    Ali Sadollah

  3. School of Civil, Environmental and Architectural Engineering, Korea University, Seoul, South Korea

    Joong Hoon Kim

Authors
  1. Abobakr Khalil Al-Shamiri
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  2. Ali Sadollah
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  3. Joong Hoon Kim
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Corresponding author

Correspondence to Joong Hoon Kim .

Editor information

Editors and Affiliations

  1. Istanbul University – Cerrahpaşa, Istanbul, Turkey

    Sinan Melih Nigdeli

  2. Korea University, Seoul, Korea (Republic of)

    Joong Hoon Kim

  3. Istanbul University - Cerrahpaşa, Istanbul, Turkey

    Gebrail Bekdaş

  4. Dr. BR Ambedkar National Institute of Technology, Jalandhar, India

    Anupam Yadav

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Al-Shamiri, A.K., Sadollah, A., Kim, J.H. (2021). Harmony Search Algorithms for Optimizing Extreme Learning Machines. In: Nigdeli, S.M., Kim, J.H., Bekdaş, G., Yadav, A. (eds) Proceedings of 6th International Conference on Harmony Search, Soft Computing and Applications. ICHSA 2020. Advances in Intelligent Systems and Computing, vol 1275. Springer, Singapore. https://doi.org/10.1007/978-981-15-8603-3_2

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