改进引力搜索最小二乘支持向量机交通流预测.

In order to improve the accuracy of traffic flow forecasting model based on least squares support vector machine (LSSVM), this paper proposed a novel modified gravitational search algorithm (TCK-AGSA) for parameters optimization. Firstly, this paper improved the Kbest function based on tent map, so that the algorithm had a mechanism to jump out of local optimum. Then, it introduced the guidance of global optimal to accelerate the movement of agents towards optimal solution. Furthermore, it introduced the evolutionary factor and converge factor into the weighted coefficient of agent's velocity to make the algorithm more adaptive. The simulation results for 12 benchmark functions show that the performance of TCK-AGSA is better than GSA and its variants. Finally, this paper proposed a LSSVM model optimized by TCK-AGSA, and selected the 2016 actual traffic flow data of Guizhou Expressway for experiment. The results show that the proposed model has better prediction accuracy, robustness and generalization capability. [ABSTRACT FROM AUTHOR]