Abstract: When deal with infrared image segmentation of electrical equipment, traditional image segmentation method has the shortage of the low accuracy and poor efficiency of diagnosis. Therefore, a improved grasshopper optimization algorithm (IGOA) with self-learning and neighborhood searching ability is proposed. Combined with cross entropy, the improved algorithm was applied on infrared image segmentation of electrical equipment. In order to improve the optimization precision and the optimizing efficiency of standard grasshopper optimization algorithm (GOA), the good point-set, pseudo-opposite learning, paired self-learning and neighborhood searching strategy were used to improve the global optimizing ability of GOA. JP3Cross entropy was regarded as the evaluation measurement, a segmentation model of infrared images IGOA-Cross was constructed. Four common infrared image of electrical equipment were used for experimentalJP analysis. The results show that compared with contrast model, the proposed model has lower misclassification rate, higher peak signal-to-noise ratio and structural similarity degree. The improved model can deal with infrared image segmentation with inhomogeneous background and large noise, and has a better segmentation efficiency and precision.