Novel Methodology to Optimize the Architecture of Multi-Layer Feed Forward Neural Network Using Grey Wolf Optimizer (GWO-MLP)
Sandeep Patil1, Nidul Sinha2, Biswajit Purkayastha3
1Sandeep Patil, Research Scholar, Department of Computer Science and Engineering, National Institute of Technology Silchar, Cachar, Assam, India.
2Nidul Sinha, Professor, Department of Electrical Engineering, National Institute of Technology Silchar, Cachar, Assam, India.
3Biswajit Purkayastha, Department of Electrical Engineering, National Institute of Technology Silchar, Cachar, Assam, India.
Manuscript received on 10 April 2019 | Revised Manuscript received on 17 April 2019 | Manuscript Published on 26 April 2019 | PP: 731-739 | Volume-8 Issue-6S April 2019 | Retrieval Number: F61610486S19/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The performance of a multi-layer neural network (MLP) depends how it is optimized. The optimization of MLP including its structure is tedious one as there is no explicit rules for deciding number of layers and number of neurons in each layer. Further, if the error function is multi-modal the conventional way of using gradient descent rule may give only local optimal solutions which may result in poorer performance of the network. In this paper a novel way is adopted to optimize the MLP in which a recently developed meta-heuristic optimization technique, Gray wolf optimizer (GWO) is used to optimize the weights of the MLP network. Meta-heuristic algorithms are known to be very efficient in finding globally optimal solutions of highly non-linear optimization problems. In this work the optimization of MLP is done by variation of hidden neurons layer wise and best performance is obtained using GWO algorithm. The ultimate optimal structure of MLP network so obtained is 13-6-1 where 13 is the number of neurons in the input layer, 6 is the number of neurons in the hidden layer and 1 is the number of neuron in the output layer. Single hidden layer is found to give better results as compared to more hidden layers. The performance of the optimized GWO-MLP network is investigated on three different datasets namely UCI Cleveland Benchmark Dataset, UCI Statlog Benchmark Dataset and Ruby Hall Clinic Local Dataset. On comparison the performance of the proposed approach is found to be superior to all other already reported works in terms of accuracy and MSE.
Keywords: MSE, UCI, GWO.
Scope of the Article: Computer Science and Its Applications