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首页> 外文期刊>Journal of Medical Imaging and Health Informatics >Multi-Swarm Particle Swarm Optimizer with Mutation and Its Research in Biomedical Information Classification Optimizer
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Multi-Swarm Particle Swarm Optimizer with Mutation and Its Research in Biomedical Information Classification Optimizer

机译：多群粒子群优化器，具有突变及其在生物医学信息分类优化器中的研究

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摘要

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.
机译：生物医学信息的分类在各种生理和心理疾病的预测和预防中起着重要作用。由于其强大的实用性，SVM广泛用于生物医学信息分类，在求解数据分类问题，例如小样本，非线性和高尺​​寸等数据分类问题。为了提高生物医学信息中SVM的分类准确性，本文提出了一种基于多群突变（MSM-PSO）的粒子群优化算法。 MSM-PSO使用多个子步骤并行搜索最佳解决方案。当聚集近一半的亚群时，对每个亚群中的最佳颗粒进行高斯突变，而在每个亚群中的两个剩余较差的颗粒上进行反馈突变。然后，改进的PSO算法用于优化SVM模型的参数。提出了一种新的分类方法（MSM-PSO-SVM）。要验证MSM-PSO-SVM的分类性能，本文对生物医学数据进行分类。测试结果表明，所提出的MSM-PSO-SVM已经实现了令人满意的生物医学预测的分类结果。

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