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Kriging模型改进的多目标优化算法研究

余竹玛 李梅

余竹玛, 李梅. Kriging模型改进的多目标优化算法研究[J]. 机械科学与技术, 2019, 38(6): 977-984. doi: 10.13433/j.cnki.1003-8728.20190056
引用本文: 余竹玛, 李梅. Kriging模型改进的多目标优化算法研究[J]. 机械科学与技术, 2019, 38(6): 977-984. doi: 10.13433/j.cnki.1003-8728.20190056
Yu Zhuma, Li Mei. A Improved Multi-objective Optimization Algorithm using Kriging Model[J]. Mechanical Science and Technology for Aerospace Engineering, 2019, 38(6): 977-984. doi: 10.13433/j.cnki.1003-8728.20190056
Citation: Yu Zhuma, Li Mei. A Improved Multi-objective Optimization Algorithm using Kriging Model[J]. Mechanical Science and Technology for Aerospace Engineering, 2019, 38(6): 977-984. doi: 10.13433/j.cnki.1003-8728.20190056

Kriging模型改进的多目标优化算法研究

doi: 10.13433/j.cnki.1003-8728.20190056
基金项目: 

国家自然科学基金项目 71501110

详细信息
    作者简介:

    余竹玛(1990-), 助理实验师, 研究方向为智能制造与优化设计, zmyuctgu1990@126.com

  • 中图分类号: TH166

A Improved Multi-objective Optimization Algorithm using Kriging Model

  • 摘要: 将基于模糊C均值聚类改进的多目标优化算法(A fuzzy c-means clustering based evolutionary algorithm,FCEA)与高价单目标优化算法(Efficient global optimization,EGO)进行融合,基于Kriging模型提出了一种改进的多目标优化算法(FCEA-EGO)。在FCEA-EGO算法寻优过程中,利用模糊C均值聚类算法从整个种群中选择相似个体进行遗传操作,引导算法进行寻优;基于EGO算法的校正点选择机制,逐步修正校正点,提高Kriging模型精度。实验结果表明,FCEA-EGO算法相对于典型的高价多目标优化算法MOEA/D-EGO、ParEGO、SMS-EGO具有更优异的求解能力。最后,基于FCEA-EGO算法对某轻型飞机的齿轮减速器进行了优化设计,验证了其求解实际工程优化问题的能力。
  • 图  1  FCEA-EGO算法流程图

    图  2  四种算法求解ZDT1、ZDT3、DTLZ2问题

    图  3  某轻型飞机的齿轮减速器

    图  4  两种算法求解齿轮减速器优化问题的HV指标的箱形图

    图  5  FCEA-EGO、MOEA/D-EGO求解齿轮减速器优化问题

    表  1  IGD的平均值(标准差)

    测试函数 FCEA-EGO MOEA/D-EGO SMS-EGO ParEGO
    ZDT1 1.524E-02(1.606E-02) 3.669E-02(1.208E-02) 8.089E-01(9.934E-02) 1.272E+01(1.632E+00)
    ZDT2 3.957E-02(1.452E-02) 3.791E-02(4.083E-03) 6.095E-01(9.100E-06) 1.441E+01(3.003E+00)
    ZDT3 7.435E-02(2.947E-02) 3.067E-01(1.225E-01) 7.654E-01(1.158E-01) 1.223E+01(2.119E+00)
    ZDT4 3.523E-01(1.047E-01) 1.882E+01(1.289E+01) 6.030E+01(1.148E+01) 5.263E+01(1.172E+01)
    ZDT6 1.852E-01(9.820E-02) 5.812E-01(1.122E-01) 1.521E+00(1.885E+00) 1.055E+01(3.007E-01)
    DTLZ2 3.137E-02(1.167E-03) 1.419E-01(1.035E-02) 1.428E-01(3.703E-02) 2.157E-01(1.532E-02)
    下载: 导出CSV

    表  2  HV的平均值(标准差)

    测试函数 FCEA-EGO MOEA/D-EGO SMS-EGO ParEGO
    ZDT1 3.720E+00(3.807E-02) 3.605E+00(2.027E-02) 1.448E-01(1.099E-01) 0.000E+00(0.000E+00)
    ZDT2 3.413E+00(1.402E-01) 3.282E+00(6.652E-03) 1.100E-01(2.414E-05) 0.000E+00(0.000E+00)
    ZDT3 5.156E+00(1.517E+00) 3.349E+00(4.673E-01) 1.471E-01(1.126E-01) 0.000E+00(0.000E+00)
    ZDT4 3.863E+02(3.462E+02) 1.824E-02(5.770E-02) 0.000E+00(0.000E+00) 0.000E+00(0.000E+00)
    ZDT6 1.319E+01(1.875E+01) 1.280E+00(2.202E-01) 8.000E-02(8.896E-02) 0.000E+00(0.000E+00)
    DTLZ2 9.729E+00(7.861E-02) 7.299E+00(1.191E-02) 6.495E-01(5.788E-02) 3.511E-01(3.610E-02)
    下载: 导出CSV
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出版历程
  • 收稿日期:  2018-12-11
  • 刊出日期:  2019-06-05

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