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Training Artificial Neural Networks by a Hybrid PSO-CS Algorithm

Department of Industrial Engineering and Systems Management, Feng Chia University, Taichung 40724, Taiwan
Department of Electrical and Electronic Engineering, University of Transport Technology, Hanoi 100000, Vietnam
Author to whom correspondence should be addressed.
Academic Editor: Toly Chen
Algorithms 2015, 8(2), 292-308;
Received: 14 March 2015 / Accepted: 29 May 2015 / Published: 11 June 2015
PDF [783 KB, uploaded 11 June 2015]


Presenting a satisfactory and efficient training algorithm for artificial neural networks (ANN) has been a challenging task in the supervised learning area. Particle swarm optimization (PSO) is one of the most widely used algorithms due to its simplicity of implementation and fast convergence speed. On the other hand, Cuckoo Search (CS) algorithm has been proven to have a good ability for finding the global optimum; however, it has a slow convergence rate. In this study, a hybrid algorithm based on PSO and CS is proposed to make use of the advantages of both PSO and CS algorithms. The proposed hybrid algorithm is employed as a new training method for feedforward neural networks (FNNs). To investigate the performance of the proposed algorithm, two benchmark problems are used and the results are compared with those obtained from FNNs trained by original PSO and CS algorithms. The experimental results show that the proposed hybrid algorithm outperforms both PSO and CS in training FNNs. View Full-Text
Keywords: Cuckoo Search algorithm; artificial neural network; prediction; flow forecasting; reservoir Cuckoo Search algorithm; artificial neural network; prediction; flow forecasting; reservoir

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Chen, J.-F.; Do, Q.H.; Hsieh, H.-N. Training Artificial Neural Networks by a Hybrid PSO-CS Algorithm. Algorithms 2015, 8, 292-308.

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