Optimization of modular granular neural networks using a firefly algorithm for human recognition

In this paper a new optimization method for modular neural network (MNN) design using granular computing and a firefly algorithm is proposed. This method is tested with human recognition based on benchmark ear and face databases to verify the effectiveness and the advantages of the proposed method. Nowadays, there are a great number of optimization techniques, but it is very important to find an appropriate one that allows for better results depending on the area of application. For this reason, a comparison of techniques is presented in this paper, where the results achieved for ear recognition and face recognition by the proposed method are compared against a hierarchical genetic algorithm in order to know which of these techniques provides better results when a modular granular neural network is optimized and applied to pattern recognition mainly for human recognition. The parameters of modular neural networks that are being optimized are: the number of modules (or sub granules), percentage of data for the training phase, learning algorithm, goal error, number of hidden layers and their number of neurons. Simulation results show that the proposed approach combining the firefly algorithm with granular computing provides very good results in optimal design of MNNs.