Research on Material Allocation Path Based on Hopfield Neural Network and Simulated Annealing Hybrid Algorithm

Abstract

When a natural disaster occurs, in order to ensure the basic material needs of the affected people, the delivery of emergency materials after the disaster occupies a very important position. Aiming at the defects that the Hopfield neural network algorithm is easy to fall into local optimum and the simulated annealing algorithm convergence speed is too slow, a hybrid neural network algorithm (SA-HNN) is proposed. Combining the advantages of Hopfield neural network and simulated annealing algorithm, the simulated annealing algorithm is the mechanism of receiving a poor solution with a certain probability is applied to the Hopfield neural network algorithm, which overcomes the defect that the neural network is easy to fall into the local optimal. Based on this hybrid algorithm, the vehicle distribution path optimization problem is solved. Compared with the traditional neural network algorithm, the algorithm is improved Calculation efficiency and accuracy.