Focusing on the weakness of the balance capability between global exploration and local search in the classical GWO algorithm

a novel grey wolf algorithm based on opposition learning (OLGWO)

which can evolve the hyper-parameters of forecasting model

is proposed to improve the accuracy and enhance the robustness of traffic flow forecasting models. This algorithm is designed to take advantage of opposition learning strategy with the iterative process

and exploits the concept of rank correlation that can describe the Spearman correlation coefficients between the target wolf and the common wolves

and then selectively updates the each wolf of the whole population according to their values. Firstly

the performance comparison of four algorithms (OLGWO

TGWO

GWO

PSO)

based on 12 benchmark functions

is conducted in terms of the two metrics

namely the optimization means and standard deviations. The results verify the outstanding performance of the proposed algorithm. Furthermore

based on the California highway traffic flow data

the four models optimized by the concerned algorithms are compared under different loss rates. The results show that the prediction accuracy of OLGWO-BP is higher than that of the others by 1.95%

3.98% and 11.07%

respectively

and the stability is better.