基于改进人工蜂群算法优化小波神经网络的短时交通流预测

doi:10.3976/j.issn.1002-4026.2018.02.013

Abstract

Abstract:

To improve the forecasting accuracy of short-term traffic flow of urban road, and to overcome the disadvantages of slow convergence and easy to fall into local optimum in the prediction process of wavelet neural network, an improved artificial bee colony algorithm, or ABC for short, was proposed to optimize the wavelet neural network prediction model. The adaptive mutation operation in differential evolution algorithm and the selection operator, crossover operator and mutation operator in genetic algorithm were introduced to optimize the traditional ABC, and to improve such disadvantages as slow convergence and weak local search ability in its later period. In this paper, the algorithm was used to optimize the parameters of the wavelet neural network and predict the shortterm traffic flow. The simulation results show that compared with the existing model, the improved ABC algorithm has less error, higher accuracy, fewer training times, and has higher practical application value.

Key words: wavelet neural network, genetic algorithm, artificial bee colony algorithm, differential evolution algorithm, short-term traffic flow

CLC Number:

U491.1⁺4

HUANG En-tan, GU Yuan-li. An optimized wavelet neural network based on improved artificial bee colony algorithm for short-term traffic flow prediction[J].SHANDONG SCIENCE, 2018, 31(2): 79-87.

References

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An optimized wavelet neural network based on improved artificial bee colony algorithm for short-term traffic flow prediction

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