Particle Swarm Optimization (PSO) is easy to fall into local minimum, and has poor global search ability. Many improved algorithms cannot optimize PSO performance fully by using a single search strategy in a way. In order to solve the problem, a novel PSO with Firefly Behavior and Levy Flight (FBLFPSO) was proposed. The local search ability of PSO was improved to avoid falling into local optimum by using improved self-regulating step firefly search strategy. Then, the principle of Levy flight was taken to enhance population diversity and improve the global search ability of PSO, which contributed to escape from local optimal solution. The simulation results show that, compared with the existing correlation algorithms, the global search ability and the search accuracy of FBLFPSO are greatly improved.

Since the clustering by data competition algorithm has poor performance on complex datasets, a density-sensitive clustering by data competition algorithm was proposed. Firstly, the local distance was defined based on density-sensitive distance measure to describe the local consistency of data distribution. Secondly, the global distance was calculated based on local distance to describe the global consistency of data distribution and dig the information of data space distribution, which can make up for the defect of Euclidean distance on describing the global consistency of data distribution. Finally, the global distance was used in clustering by data competition algorithm. Using synthetic and real life datasets, the comparison experiments were conducted on the proposed algorithm and the original clustering by data competition based on Euclidean distance. The simulation results show that the proposed algorithm can obtain better performance in clustering accuracy rate and overcome the defect that clustering by data competition algorithm is difficult to handle complex datasets.

Considering the low accuracy of the existing image segmentation method based on affinity propagation clustering, a FCAP algorithm which combined fuzzy connectedness and affinity propagation clustering was proposed. A Whole Fuzzy Connectedness (WFC) algorithm was also proposed with concerning the shortcoming of traditional fuzzy connectedness algorithms that can not get fuzzy connectedness of every pair of pixels. In FCAP, the image was segmented by using super pixel technique. These super pixels could be considered as data points and their fuzzy connectedness could be computed by WFC. Affinities between super pixels could be calculated based on their fuzzy connectedness and spatial distances. Finally, affinity propagation clustering algorithm was used to complete the segmentation. The experimental results show that FCAP is much better than the methods which use affinity propagation clustering directly after getting super pixels, and can achieve competitive performance when comparing with other unsupervised segmentation methods.