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改进的萤火虫算法优化双支持向量机参数

顾佳鑫 贺兴时 杨新社

顾佳鑫, 贺兴时, 杨新社. 改进的萤火虫算法优化双支持向量机参数[J]. 微电子学与计算机, 2022, 39(11): 11-18. doi: 10.19304/J.ISSN1000-7180.2022.0230
引用本文: 顾佳鑫, 贺兴时, 杨新社. 改进的萤火虫算法优化双支持向量机参数[J]. 微电子学与计算机, 2022, 39(11): 11-18. doi: 10.19304/J.ISSN1000-7180.2022.0230
GU Jiaxin, HE Xingshi, YANG Xinshe. Improved firefly algorithm optimizes twin support vector machine parameters[J]. Microelectronics & Computer, 2022, 39(11): 11-18. doi: 10.19304/J.ISSN1000-7180.2022.0230
Citation: GU Jiaxin, HE Xingshi, YANG Xinshe. Improved firefly algorithm optimizes twin support vector machine parameters[J]. Microelectronics & Computer, 2022, 39(11): 11-18. doi: 10.19304/J.ISSN1000-7180.2022.0230

改进的萤火虫算法优化双支持向量机参数

doi: 10.19304/J.ISSN1000-7180.2022.0230
基金项目: 

国家自然科学基金 12101477

陕西省自然科学基础研究计划 2020JQ-831

详细信息
    作者简介:

    顾佳鑫  女,(1997-),硕士研究生.研究方向为机器学习与智能优化算法

    杨新社  男,(1965-),博士,英国国家物理实验室高级科学家.研究方向为数学建模、工程优化以及科学、数值计算方法等

    通讯作者:

    贺兴时(通讯作者)  男,(1960-),硕士,教授.研究方向为智能优化算法、数理统计、数据挖掘等.E-mail: xsh1002@126.com

  • 中图分类号: TP301.6

Improved firefly algorithm optimizes twin support vector machine parameters

  • 摘要:

    针对原始萤火虫算法(Firefly Algorithm,FA)易陷入局部最优、求解精度低,双支持向量机(Twin Support Vector Machine, TWSVM)参数选择困难的问题,提出基于改进萤火虫算法(DEFA)的双支持向量机模型(DEFA-TWSVM).首先,对原始萤火虫算法进行改进,得到DEFA算法:在萤火虫位置更新公式中结合动态惯性权重,自适应地调整步长控制因子来快速搜索全局和局部最优解,对每次移动后的萤火虫群融入差分进化算法(Differential Evolution,DE)策略,保证种群迭代多样性,通过基准测试函数的仿真结果表明改进后的算法全局寻优能力强,不易陷入局部最优.其次,利用DEFA算法优化TWSVM的参数.最后,在UCI数据集进行测试,得到DEFA-TWSVM和其他模型的分类准确率.通过比较发现:DEFA算法可以在训练过程中自动确定TWSVM参数,解决了TWSVM参数选择盲目的问题,平均分类准确率相较其他模型提高了2到5个百分点.

     

  • 图 1  动态惯性权重w变化曲线

    Figure 1.  Variation diagram of dynamic inertia weight w

    图 2  α变化曲线

    Figure 2.  Variation diagram of α

    图 3  收敛曲线图

    Figure 3.  Convergence curve

    图 4  各算法在UCI数据集的分类准确率

    Figure 4.  Classification accuracy of algorithm on UCI data set

    表  1  基准测试函数

    Table  1.   Benchmark function

    函数 函数表达式 解空间 最优值
    Ackley $f_1(x)=-20 \exp \left(-0.2 \sqrt{\frac{1}{n} \sum\limits_{i=1}^n x_i^2}\right)-\exp \left(\frac{1}{n} \sum\limits_{i=1}^n \cos \left(2 \pi x_i\right)\right)+20+e $ [-32,32] 0
    Rastrigin $ f_2(x)=\sum\limits_{i=1}^n\left[x_i^2-10 \cos \left(2 \pi x_i\right)+10\right]$ [-5.12,5.12] 0
    Griewank $ f_3(x)=\frac{1}{4\;000} \sum\limits_{i=1}^n x_i^2-\prod\limits_{i=1}^n \cos \left(\frac{x_i}{\sqrt{i}}\right)+1$ [-600,600] 0
    Schwefel′s2.2 $ f_4(x)=\sum\limits_{i=1}^n\left|x_i\right|+\prod\limits_{i=1}^n\left|x_i\right|$ [-10,10] 0
    Sphere $ f_5(x)=\sum\limits_{i=1}^n x_i^2$ [-100,100] 0
    Sum square $ f_6(x)=\sum\nolimits_{i=1}^n i x_i^2$ [-10,10] 0
    下载: 导出CSV

    表  2  算法参数设置

    Table  2.   Algorithm parameter settings

    算法名称 参数设置
    DEFA γ=1,β0=0.2,F=0.5,n=30,T=200
    FA α=0.4,γ=1,β0=0.2,n=30,T=200
    BA A=20,r=0.5,n=30,T=200
    FPA P=0.7,n=30,T=200
    CS Pa=0.25,α0=0.01,n=30,T=200
    下载: 导出CSV

    表  3  实验数据集描述

    Table  3.   Description of experimental data set

    数据集名称 样本数 特征数
    Sonar 208 60
    Heart-c 303 13
    Ionosphere 351 34
    Monks3 432 6
    Australian 690 14
    German 1 000 24
    Image-seg 2 310 19
    下载: 导出CSV

    表  4  各算法在不同数据集上的分类准确率对比/%

    Table  4.   Comparison of classification accuracy of each algorithm on different data sets/%

    数据集名称 准确率±标准差
    TWSVM Grid-TWSVM PSO-TWSVM FA-TWSVM DEFA-TWSVM
    Sonar 61.17±5.25 64.00±3.71 64.55±3.72 65.95±5.42 67.50±3.34
    Heart-c 84.09±7.71 84.46±5.81 88.57±4.92 89.71±4.63 90.86±6.54
    Ionosphere 94.31±4.23 95.09±3.11 96.54±4.61 96.01±7.71 96.88±2.52
    Monks3 75.83±3.21 77.52±5.83 77.19±4.10 78.36±4.23 81.42±5.61
    Australian 79.71±5.30 79.28±5.59 81.45±7.38 80.72±6.27 85.58±5.73
    German 68.69±3.09 69.75±4.04 69.89±4.42 71.05±4.31 74.60±3.90
    Image-seg 83.81±5.84 85.24±2.17 87.14±2.02 85.71±3.36 87.61±5.69
    平均准确率 78.23 79.33 80.76 81.07 83.49
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-04-08
  • 修回日期:  2022-04-28
  • 网络出版日期:  2022-11-29

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