Journal of Computer Applications ›› 2024, Vol. 44 ›› Issue (7): 2144-2150.DOI: 10.11772/j.issn.1001-9081.2023070982

• Advanced computing • Previous Articles     Next Articles

Experimental design and staged PSO-Kriging modeling based on weighted hesitant fuzzy set

Peigen GAO, Bin SUO()   

  1. School of Information Technology,Southwest University of Science and Technology,Mianyang Sichuan 621000,China
  • Received:2023-07-20 Revised:2023-09-15 Accepted:2023-09-20 Online:2023-10-26 Published:2024-07-10
  • Contact: Bin SUO
  • About author:GAO Peigen, born in 1996, M. S. candidate. His research interests include experimental design and modeling, reliability optimized design.
    First author contact:SUO Bin, born in 1979, Ph. D., associate research fellow. His research interests include reliability design and analysis, uncertain information processing.
  • Supported by:
    National Natural Science Foundation of China(U1830133)

基于加权犹豫模糊集的实验设计与分阶段PSO-Kriging建模

高培根, 锁斌()   

  1. 西南科技大学 信息工程学院,四川 绵阳 621000
  • 通讯作者: 锁斌
  • 作者简介:高培根(1996—),男,四川眉山人,硕士研究生,主要研究方向:实验设计与建模、可靠性优化设计;
    第一联系人:锁斌(1979—),男(回族),陕西西乡人,副研究员,博士,主要研究方向:可靠性设计分析、不确定信息处理。
  • 基金资助:
    国家自然科学基金资助项目(U1830133)

Abstract:

Excessive experimental costs lead to fewer experimental sample points obtained for complex systems with nonlinear multipolar outputs, and lower accuracy of proxy models. An experimental design and modeling method based on priori information was proposed to address this issue. Priori information was utilized to divide experimental design regions, weighted hesitant fuzzy set was constructed for each region based on volatility indicator to increase the rationality of evaluating results. The number of experimental sample points was determined by combining volatility and range of each region, and the sample points were obtained by Hammersley sequence sampling. Then staged search Particle Swarm Optimization (PSO) algorithm was combined with Kriging method to improve the computational accuracy of proxy model. Finally, the damage model of simulating planar truss structure was used to verify the effectiveness of the proposed method. Experimental results show that the model goodness-of-fit of model established by the proposed method is improved by 0.84% and 4.94% on average and root mean squared error is reduced by 31.02% and 57.18% on average compared with the models built by Hammersley sequence sampling and Latin hypercube design.

Key words: priori information, weighted hesitant fuzzy set, Hammersley sequence, Particle Swarm Optimization (PSO) algorithm, Kriging model

摘要:

过高的实验成本导致输出为非线性多极值的复杂系统获得的实验样本点少,建立的代理模型精度较低。针对此现状提出一种基于先验信息的实验设计与建模方法。该方法利用先验信息划分实验设计域,并根据波动性指标构建各区域的加权犹豫模糊集,增加评价结果的合理性;结合各区域的波动性与范围大小决定实验样本点个数,由汉默斯里序列采样获取样本点;再将分阶段搜索粒子群算法与Kriging方法结合,提高代理模型的计算精度。以模拟平面桁架结构的损伤模型验证所提方法的有效性。实验结果表明,与汉默斯里序列采样、拉丁超立方设计建立的模型相比,所提方法建立的模型拟合优度平均提升0.84%和4.94%,均方根误差平均降低31.02%和57.18%。

关键词: 先验信息, 加权犹豫模糊集, 汉默斯里序列, 粒子群优化算法, 克里金模型

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