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.