Abstract
Spotted hyena optimizer (SHO) is a novel heuristic optimization algorithm based on the behavior of spotted hyena and their collaborative behavior in nature. In this paper, we design a spotted hyena optimizer for feedforward neural networks (FNNs). Training feedforward neural networks is regard as a challenging task, because it is easy to fall into local optima. Our objective is to apply heuristic optimization algorithm design to tackle these problems better than the mathematical and deterministic methods, in order to confirm that SHO algorithm training FNN is more effective. a classification datasets about Heart is applies to benchmark the performance of the proposed method. The more basic SHO is compared to other acclaimed state-of-the-art optimization algorithm, the results show that the proposed algorithm can provide better results.
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Acknowledgment
This work is supported by National Science Foundation of China under Grants No. 61563008, and by Project of Guangxi Natural Science Foundation under Grant No. 2016GXNSFAA380264.
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Li, J., Luo, Q., Liao, L., Zhou, Y. (2018). Using Spotted Hyena Optimizer for Training Feedforward Neural Networks. In: Huang, DS., Gromiha, M., Han, K., Hussain, A. (eds) Intelligent Computing Methodologies. ICIC 2018. Lecture Notes in Computer Science(), vol 10956. Springer, Cham. https://doi.org/10.1007/978-3-319-95957-3_88
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DOI: https://doi.org/10.1007/978-3-319-95957-3_88
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