A novel hybrid algorithm combining hunting search with harmony search algorithm for training neural networks


JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY, cilt.64, sa.5, ss.748-761, 2013 (SCI İndekslerine Giren Dergi) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 64 Konu: 5
  • Basım Tarihi: 2013
  • Doi Numarası: 10.1057/jors.2012.79
  • Sayfa Sayıları: ss.748-761


Neural networks (NNs) are one of the most widely used techniques for pattern classification. Owing to the most common back-propagation training algorithm of NN being extremely computationally intensive and it having some drawbacks, such as converging into local minima, many meta-heuristic algorithms have been applied to training of NNs. This paper presents a novel hybrid algorithm which is the integration of Harmony Search (HS) and Hunting Search (HuS) algorithms, called h_HS-HuS, in order to train Feed-Forward Neural Networks (FFNNs) for pattern classification. HS and HuS algorithms are recently proposed meta-heuristic algorithms inspired from the improvisation process of musicians and hunting of animals, respectively. Harmony search builds up the main structure of the hybrid algorithm, and HuS forms the pitch adjustment phase of the HS algorithm. The performance proposed algorithm is compared to conventional and meta-heuristic algorithms. Empirical tests are carried out by training NNs on nine widely used classification benchmark problems. The experimental results show that the proposed hybrid harmony-hunting algorithm is highly capable of training NNs. Journal of the Operational Research Society (2013) 64, 748-761. doi:10.1057/jors.2012.79 Published online 22 August 2012