改进的FCM聚类在交通时段自动划分中的应用

Application of improved Fuzzy C-Means clustering in automatic programming traffic intervals

针对传统交通时段划分方法的局限性,提出了一种混合蛙跳算法(SFLA)与模糊C均值算法(FCM)有机结合的交通时段划分方法SFLA-FCM。SFLA是一种全新的后启发式群体进化算法,具有高效的计算性能和优良的全局搜索能力。SFLA-FCM使用SFLA的优化过程代替FCM的基于梯度下降的迭代过程,有效地避免了FCM对初值敏感及容易陷入局部极小的缺陷。实验结果表明,与单一FCM法相比,SFLA-FCM聚类更准确,效果更佳,对解决城市交通时段的自动划分问题是可行、有效的。

Due to limitations of traditional traffic interval programming methods,a novel traffic interval programming method （SFLA-FCM） is proposed based on Shuffled Frog Leaping Algorithm（SFLA） and Fuzzy C Means（FCM）.SFLA is a new rectaheuristic population evolutionary algorithm and it has fast calculation speed and excellent global search capability.SFLA-FCM uses SFLA to replace the iteration process of FCM based on the gradient descent and avoids the disadvantages of local optimality and initialization dependence.The experimental results show that the proposed method is more accurate and efficient than FCM and it is feasible and effective for automatic programming traffic intervals.