基于最优类中心扰动的萤火虫聚类算法

萤火虫算法FA是受自然界中萤火虫的发光特性以及通过发光交流信息的现象启发提出的一种新的群智能优化算法。在该算法中,萤火虫被吸引向亮度高的萤火虫移动,最亮的萤火虫随机移动,导致该算法聚类时存在收敛速度较慢、后期容易在最优值附近振荡、稳定性较差的问题。据此,对萤火虫的移动方式和随机扰动方式做了改进,提出了一种改进的萤火虫聚类算法——基于最优类中心扰动的萤火虫聚类算法。实验比较了步长因子的取值。UCI数据集的仿真结果表明,该聚类算法的聚类效果较好,能消除FA在最优值附近反复振荡的问题,算法的稳定性和收敛性明显提高。

An improved firefly clustering algorithm based on optimal class center disturbance

Firefly algorithm is one of the new swarm intelligence optimization algorithms inspired by the flashing behavior of fireflies and the phenomenon of bioluminescent communication.In the algorithm,a firefly is attracted to a brighter firefly and the brightest will move randomly,which causes slower convergence speed of the algorithm,oscillation on the optimum value in the later of iteration and poorer stability.So an improved firefly clustering algorithm is put forward based on optimal class center disturbance.The movement and randomization of the firefly are modified,and the values of step factors are compared in experiment.The simulation results on UCI benchmark data sets show that the algorithm is feasible;it can be efficiently used for clustering problems and eliminate the repeated oscillation while the stability and convergence are improved obviously.