Abstract: As a new swarm intelligent algorithm, firefly learning has a good performance of global searching, and it can be used to solve the problems of back propagation (BP) network’s local minima and slow convergence speed. However, there is no guidance for firefly parameter selection when the algorithm is utilized in neural network learning. If parameters are selected improperly, it may lead to that the firefly neural network converging slowly and fail in collaborative optimization. Therefore, a bi-layer firefly algorithm is proposed to improve parameter selection. UCI iris data and bearing fault data are used to validate the proposed approach. Experiment results demonstrate that the converging rate of firefly neural network decreases remarkably by using the proposed method, and considering the training efficiency and fault recognition rate, firefly performs better than BP, particle swarm optimization (PSO) and genetic algorithm (GA).

Key words: fault diagnosis, firefly algorithm, neural network, parameter selection