Differential evolution bare bones particle swarm optimization and its application to image segmentation

Basic bare bones particle swarm optimization (BBPSO) can not get good optimization performance because it easy to get stuck into local optima. Basing on basic BBPSO, using the idear of mutation in differential evolution, a new algorithm named differential evolution bare bones particle swarm optimization (DEBBPSO) is proposed. Combining with image fuzzy entropy, applies DEBBPSO to image segmentation. Uses DEBBPSO to explore fuzzy parameters of maximum fuzzy entropy, and gets the optimum fuzzy parameter combination, then obtains the segmentation threshold. According to experiment results of the new algorithm compare with other two algorithms, the proposed algorithm performs good segmentation performance and very low time cost. It can be use to real time and precision measure coal dust image.