In order to improve the segmentation effect of rough clustering algorithm when applied to image segmentation,a surrogate multi-objective particle swam driven rough clustering algorithm is proposed for image segmentation.First,an adaptive threshold determination mechanism is designed to determine the upper and lower approximations of rough clustering,and to reduce the manual in-tervention.Second,a penalty factor is designed by utilizing the proportion of samples in the bounda-ry region in rough clustering,and then an effective rough clustering objective function is constructed with the combination of the compactness and the separation of clusters.The clustering quality is e-valuated from different perspectives by the connectivity objective function of clusters.Finally,the surrogate-assisted elite multi-objective particle swarm optimization strategy is designed to screen the elite particles to update the population,and to obtain the final clustering center,thus avoiding the problem that the rough clustering algorithm is sensitive to the initial center and prone to fall into the local optimum,and improving the optimization efficiency.Experiment results show that the de-signed optimization strategy can get better results on the standard test problem;it has the best seg-mentation effect compared with other image segmentation algorithms.
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