Density peak clustering algorithm based on improved fruit fly optimization algorithm

The cutoff distance of clustering by fast search and find of density peaks (DPC) requires manual intervention, and the selection of the parameters has great influence on the results of the algorithm. To overcome this problem, a density peak clustering method based on improved fruit fly optimization algorithm is proposed. The population of fruit fly is initialized by the Tent chaotic mapping, and using the characteristics of randomness, ergodicity and regularity of Tent chaotic sequence, the diversity of the initial population and the global exploration ability of the algorithm are enhanced. And the basic fruit fly optimization algorithm is improved by introducing dynamic step factor and Cauchy mutation strategy to enhance its local exploration ability and help the algorithm jump out of the local optimization. The convergence of the improved FOA algorithm is analyzed theoretically by using the convergence criterion of random algorithm. The experimental results of six test functions show that the improved FOA algorithm has faster convergence speed and higher solution accuracy. The improved FOA and DPC algorithm are fused into a new DPC algorithm, using the effective optimization ability of the improved FOA to find the best cutoff distance and realize the final clustering. Experimental results show that the clustering performance of new algorithm under UCI data set and artificial data set are improved, the new algorithm outperforms DPC, FOA-DPC, FADPC, ACS-FSDP with the better performance indexes, and the effect of manually selecting truncation distance parameter is effectively suppressed.