基于扇形趋利果蝇优化算法改进的FS-K聚类算法

针对果蝇算法对高维函数收敛精度低的缺点,提出了一种改进的基于扇形搜索的果蝇算法(Fan search-Fruit Fly Optimization Algorithm,FS-FOA),该算法在原果蝇FOA算法的基础上改进了果蝇群体的搜索路径,并赋予果蝇个体趋利性,使更多的果蝇个体朝着味道浓度更大的方向前进,使果蝇群体的搜索方向有更多的选择性,增加果蝇算法在处理高维函数问题上的收敛速度和收敛精度;并将改进的FS-FOA算法与K-means聚类相结合,提出一种FS-K聚类算法,与原K-means聚类和原果蝇(FOA)算法进行对比实验,引入5个经典的测试函数对原FOA算法和FS-FOA算法寻优结果进行测试,结果表明采用FS-FOA算法具有更高的收敛精度;引入5个UCI公共数据集对改进FS-K聚类算法和原K-means算法、SOM聚类算法、FCM聚类算法进行测试,结果表明FS-K聚类算法具有更好的聚类效果。

FS-K Clustering Algorithm Based on Fan-shaped Profit-seeking Fruit Fly Optimization Algorithm

According to the shortcoming of low convergence precision of the high dimensional function of Drosophila algorithm, an improved algorithm based on fan-shaped search fruit fly optimization algorithm (FS-FOA) is put forward. This algorithm improves the search path of drosophila group on the basis of the original fruit fly FOA algorithm, gives individual of fruit flies profit-seeking, makes more individuals of the fruit flies move in the direction of bigger taste concentration, makes fruit fly group search direction more selective and increases convergence speed and convergence precision for the fruit fly algorithm to deal with high dimensional function. A FS-K clustering algorithm is forwarded based on the combination of FS-FOA algorithm and k-means clustering. Through the comparison experiments of the original k-means clustering and the original FOA algorithm, five classical test functions are introduced to test the optimizing results of the original FOA algorithm and FS-FOA algorithm, and the results show that FS-FOA algorithm has higher convergence precision. Five UCI public data sets are introduced to test the improved FS-K clustering algorithm, the original k-means algorithm, SOM clustering algorithm and FCM clustering algorithm, and the results show that FS-K clustering algorithm has better clustering effect