Multi-Swarm Particle Swarm Optimizer with Mutation and Its Research in Biomedical Information Classification Optimizer

The classification of biomedical information plays an important role in the prediction and prevention of various physiological and psychological diseases. SVM is widely used in biomedical information classification due to its strong practicability in solving data classification problems such as small sample, nonlinearity and high dimension. To improve the classification accuracy of SVM in biomedical information, a particle swarm optimization algorithm based on multi-population mutation (MsM-PSO) is proposed in this paper. MsM-PSO uses multiple subpopulations to search the optimal solution in parallel. When nearly half of the subpopulations are clustered, The Gaussian mutation is performed on the optimal particle in each subpopulation, while the feedback mutations are performed on the two remaining poorer particles in each subpopulation. Then the improved PSO algorithm is used to optimize the parameters of the SVM model. A new classification method (MsM-PSO-SVM) is proposed. To verify the classification performance of the MsM-PSO-SVM, this article classifies biomedical data. The test result shows that the proposed MsM-PSO-SVM has achieved satisfactory classification result in biomedical prediction.