Training Multi-Layer Perceptron Using Harris Hawks Optimization

Eker E., Kayri M., Ekinci S., Izci D.

2nd International Congress on Human-Computer Interaction, Optimization and Robotic Applications, HORA 2020, Ankara, Türkiye, 26 - 27 Haziran 2020 identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Doi Numarası: 10.1109/hora49412.2020.9152874
  • Basıldığı Şehir: Ankara
  • Basıldığı Ülke: Türkiye
  • Anahtar Kelimeler: Harris hawks optimization (HHO), Multi-layer perceptron (MLP), sine cosine algorithm (SCA)
  • Van Yüzüncü Yıl Üniversitesi Adresli: Evet

Özet

In this paper, Harris hawks optimization (HHO) algorithm has been proposed as an up-to-date meta-heuristic algorithm for training multi-layer perceptron (MLP). The performance of the HHO-based MLP trainer was tested by employing five standard data sets (XOR, Balloon, Iris, Breast Cancer and Heart). The results were compared with those obtained with the sine cosine algorithm (SCA). Comparative statistical results showed that using HHO algorithm as a trainer is more effective and has a higher rate of classification ability.