Prediction of Railway Passenger Flow Based on Fireworks Particle Swarm Optimization Algorithm Optimized BP Neural Network
To further enhance the accuracy of orbit passenger flow prediction,a fireworks algorithm(FWA)-particle swarm optimi-zation(PSO)-back propagation(BP)orbit passenger flow prediction model was proposed.Random factors were introduced through PSO algorithm into the evolutionary equation.However,the local search function of PSO algorithm might be weakened by this random search mode,easily leading to premature convergence and insufficient optimization power.In order to improve this problem,the explosion spark and mutation spark in the fireworks algorithm were introduced to dynamically adjust the search range and number of particles,en-hance the diversity of the particle swarm,and make the particle swarm algorithm have a self-regulation mechanism of local search abili-ty and global search ability,thereby improving the premature convergence problem of the particle swarm algorithm.The initial weights and thresholds of the backpropagation(BP)neural network were better optimized.Using Chongqing rail passenger flow data as an exam-ple,the results show that the FWA-PSO-BP model has a mean absolute percentage error(MAPE)of 2.54％,which is superior to all other compared models.
万方数据
维普
