改进引力搜索最小二乘支持向量机交通流预测

为了提高基于最小二乘支持向量机的交通流预测模型的精度，提出一种新的改进引力搜索算法（TCK-AGSA）对其进行参数寻优。首先，基于Tent映射改进Kbest函数，使算法具有跳出局部最优的机制；然后，引入全局最优引导策略，使粒子加速朝向最优解移动；接着，将进化度因子和聚合度因子引入速度更新权重系数，使算法具有较强的自适应能力。针对12个基准函数的仿真结果表明，TCK-AGSA的性能优于GSA及其改进算法。最后，建立基于TCK-AGSA寻优的最小二乘支持向量机模型，并选取2016年贵州省高速公路真实交通流数据进行预测实验，结果表明该模型具有更好的预测精度、鲁棒性和泛化能力。

Traffic flow forecasting using least squares support vector machine optimized by modified gravitational search algorithm

In order to improve the accuracy of traffic flow forecasting model based on least squares support vector machine, 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 has 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.