Design and Implementation a Sonar Data Set Classifier using Multi-Layer Perceptron Neural Network Trained by Elephant Herding Optimization

Due to the high-dimension of the sonar dataset, classification of them is a very complex task. Multi-Layer Perceptron Neural Network (MLP NN) is one of the most applicable tools in solving complicated problems as well as classifying between target and non-target in sonar applications. In this paper, a new kind of swarm-based metaheuristic search method, called Elephant Herding Optimization (EHO), is proposed for training the NN. The EHO method is inspired by the herding behavior of elephant group. In nature, the elephants belonging to different clans live together under the leadership of a matriarch, and the male elephants will leave their family group when they grow up. These two behaviors can be modelled into two following operators: clan updating operator and separating operator. The simulation results show that the new classifier performs better than the other benchmark algorithms and also original BBO in terms of avoidance trapping in local optima, classification accuracy, and convergence speed. This paper also implements the designed classifier on the FPGA substrate for testing the real-time application of the proposed method.