Abstract
An innovative metaheuristic based algorithm Chicken Swarm Optimization (CSO) is inspired by characteristics of chicken flock. CSO is particularly suitable for the investigation in candidate solutions for large spaces. This paper hybridize the CSO algorithm with the Back Propagation (BP) algorithm to solve the local minimum problem and to enhance convergence to global minimum in BP algorithm. The proposed Chicken Swarm Back Propagation (Chicken S-BP) is compared with the Artificial Bee Colony Back-Propagation (ABCBP), Genetic Algorithm Neural Network (GANN) and traditional BPNN algorithms. In particular Iris, Australian Credit Card, and 7-Bit Party classification datasets are used in training and testing the performance of the Chicken S-BP hybrid network. Results of simulation illustrates that Chicken S-BP algorithm efficiently prevents local minima and provides optimal solution.
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Acknowledgments
The Authors would like to thank Office of Research, Innovation, Commercialization and Consultancy (ORICC), Universiti Tun Hussein Onn Malaysia (UTHM) and Ministry of Education (MOE) Malaysia for financially supporting this Research under Fundamental Research Grant Scheme (FRGS) vote no. 1236. This research is also supported by Gates IT Solution Sdn. Bhd under its publication scheme.
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Khan, A. et al. (2017). Chicken S-BP: An Efficient Chicken Swarm Based Back-Propagation Algorithm. In: Herawan, T., Ghazali, R., Nawi, N.M., Deris, M.M. (eds) Recent Advances on Soft Computing and Data Mining. SCDM 2016. Advances in Intelligent Systems and Computing, vol 549. Springer, Cham. https://doi.org/10.1007/978-3-319-51281-5_13
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DOI: https://doi.org/10.1007/978-3-319-51281-5_13
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