兰州理工大学学报

兰州理工大学学报 ›› 2025, Vol. 51 ›› Issue (3): 89-98.

• 自动化技术与计算机技术 • 上一篇    下一篇

基于改进乌燕鸥算法同步优化SVM的特征选择

赵小强*1,2,3, 缐文霞1   

  1. 1.兰州理工大学 电气工程与信息工程学院, 甘肃 兰州 730050;
    2.兰州理工大学 甘肃省工业过程先进控制重点实验室, 甘肃 兰州 730050;
    3.兰州理工大学 国家级电气与控制工程实验教学中心, 甘肃 兰州 730050
  • 收稿日期:2022-08-02 出版日期:2025-06-28 发布日期:2025-06-30
  • 通讯作者: 赵小强(1969-),男,陕西岐山人,博士,教授,博导.Email:xqzhao@lut.edu.cn
  • 基金资助:
    国家自然科学基金(62263021),甘肃省科技计划资助(21YF5GA072,21JR7RA206)

Feature selection for simultaneous optimization of SVM based on improved sooty tern algorithm

ZHAO Xiao-qiang1,2,3, XIAN Wen-xia1   

  1. 1. College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China;
    2. Gansu Key Laboratory of Advance Control for Industrial Process, Lanzhou University of Technology, Lanzhou 730050, China;
    3. National Experimental Teach Electrical and Control Engineering, Lanzhou University of Technology, Lanzhou 730050, China
  • Received:2022-08-02 Online:2025-06-28 Published:2025-06-30

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摘要： 针对支持向量机(SVM)中特征选择和参数优化对分类精度有较大影响的问题,提出了一种基于改进乌燕鸥算法同步优化SVM的特征选择算法.首先利用Tent混沌映射对乌燕鸥种群初始化,增加种群多样性,在此基础上引入余弦自适应并结合模拟退火算法,避免乌燕鸥算法陷入局部最优的缺陷,增强算法全局搜索能力,提高收敛精度;其次将改进算法同特征选择和支持向量机相结合,同步优化二进制特征选择和SVM的参数;最后在10个标准数据集上进行特征选择仿真对比实验,实验结果表明相比原始算法及其他对比优化算法,所提算法能有效降低数据维度,提高分类准确率.

关键词: 乌燕鸥优化算法, 余弦自适应, 模拟退火算法, 支持向量机, 特征选择

Abstract: To address the significant impact of features selection and parameter optimization on classification accuracy in support vector machine (SVM), a feature selection algorithm based on the improved sootytern algorithm to optimize SVM synchronously is proposed. Firstly, the Tent chaotic map is used to initialize the sooty tern population to increase the diversity of the population. On this basis, cosine adaptation and simulated annealing algorithm are introduced to avoid the defect of the sooty tern algorithm falling into local optimality, enhance the global search ability of the algorithm, and improve the convergence accuracy. Secondly, the improved algorithm is combined with feature selection and SVM, and the parameters of binary feature selection and SVM are optimized simultaneously. Finally, feature selection simulation comparison experiments are carried out on 10 standard datasets. Compared to the original and other optimization algorithms, the proposed algorithm can effectively reduce the data dimension and improve the classification accuracy.

Key words: sooty tern optimization algorithm, cosine adaptation, simulated annealing algorithm, support vector machine, feature selection

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