Aiming at the problems of slow convergence speed and low precision of the traditional nonlinear demand forecast model for fresh agricultural product logistics,a forecast model that combines the improved particle swarm optimization(IPSO)algorithm with the back propagation(BP)neural network is proposed.The opposition learning mechanism,the adaptive inertia weight and the asymmetric learning factors are introduced to improve the quality of the initial solution of the particle swarm optimization(PSO)algorithm,and balance the capability of local development and global search of the algorithm;IPSO is used to optimize the weight and threshold of BP neural network to solve the problems of slow convergence speed and easy to fall into the local optimum.The effectiveness of the model is verified by an example of fresh agricultural product logistics demand forecast in Shanghai.The results show that the IPSO-BP neural network model is significantly better than the traditional PSO-BP neural network model and the BP neural network model in the prediction accuracy and the convergence speed.
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