Abstract: Artificial neural network has been an important role in grey system prediction with the excellent properties having any arbitrary precision approximation for any nonlinear function. On the basis of existed research, considering problems of low efficiency, local optimum and retardation of parameter modification in grey neural network evolution process, in this paper a new grey neural network model is established based on genetic algorithm and particle swarm optimization. Firstly, a mathematical grey neural network is proposed in order to use optimization algorithm to solve it. Secondly, a hybrid algorithm is given to optimize the neural network model, which takes both advantages of genetic algorithm and particle swarm optimization. Finally, through calculation analysis of sample about tourist quantity forecasting Japan to China, the prediction accuracy of new grey neural network, grey neural network, genetic algorithm grey neural network and particle swarm optimization grey neural network is compared. The simulation results show that the new grey neural network based on genetic algorithm and particle swarm optimization has better forecast performance, which can have a wide application prospect in social and economic fields.

Key words: genetic algorithm, particle swarm optimization, hybrid algorithm, gray neural network, optimization