Journal of Computer Applications ›› 2023, Vol. 43 ›› Issue (2): 385-390.DOI: 10.11772/j.issn.1001-9081.2022010056

• Artificial intelligence • Previous Articles    

Inverse distance weight interpolation algorithm based on particle swarm local optimization

Feng XIANG1,2, Zhongzhi LI1,2(), Xi XIONG1,2, Binyong LI1,2   

  1. 1.School of Cybersecurity,Chengdu University of Information Technology,Chengdu Sichuan 610255,China
    2.Advanced Cryptography and System Security Key Laboratory of Sichuan Province (Chengdu University of Information Technology),Chengdu Sichuan 610225,China
  • Received:2022-01-17 Revised:2022-04-06 Accepted:2022-04-11 Online:2022-04-21 Published:2023-02-10
  • Contact: Zhongzhi LI
  • About author:XIANG Feng, born in 1998, M. S. candidate. His research interests include machine learning, data analysis.
    XIONG Xi, born in 1983, Ph. D., associate professor. His research interests include artificial intelligence.
    LI Binyong, born in 1982, Ph. D., associate professor. His research interests include industrial intelligentization, data mining.
  • Supported by:
    Sichuan Science and Technology Program(2021JDRC0046)


向峰1,2, 李中志1,2(), 熊熙1,2, 李斌勇1,2   

  1. 1.成都信息工程大学 网络空间安全学院, 成都 610255
    2.先进密码技术与系统安全四川省重点实验室(成都信息工程大学), 成都 610225
  • 通讯作者: 李中志
  • 作者简介:向峰(1998—),男,湖南怀化人,硕士研究生,主要研究方向:机器学习、数据分析
  • 基金资助:


The accuracy of Inverse Distance Weighting (IDW) will be affected by the selection of reference points and parameters. Aiming at the problem of ignoring local characteristics in multi-Parameter co-optimization Inverse Distance Weighting algorithm (PIDW), an improved algorithm based on particle swarm local optimized IDW was proposed, namely Particle swarm Local optimization Inverse Distance Weight (PLIDW). Firstly, the parameters of each sample point in the study area were optimized respectively, and the cross-validation method was used for evaluation, and the optimal set of parameters for each sample point was recorded. At the same time, in order to improve the query efficiency, a K-Dimensional Tree (KD-Tree) was used to save the spatial positions and optimal parameters. Finally, according to the spatial proximity, the nearest set of parameters was selected from KD-Tree to optimize IDW. Experimental results based on simulated data and real temperature dataset show that compared with PIDW, PLIDW has the accuracy on the real dataset improved by more than 4.18%. This shows that the low accuracy in some scenarios caused by ignoring local features in PIDW is improved by the proposed algorithm, and the adaptability is increased at the same time.

Key words: spatial interpolation, Inverse Distance Weight (IDW), anisotropy, multi-parameter optimization, Particle Swarm Optimization (PSO) algorithm



关键词: 空间插值, 反距离权重, 各向异性, 多参数优化, 粒子群算法

CLC Number: