粒子群局部优化的反距离权重插值算法  

Inverse distance weight interpolation algorithm based on particle swarm local optimization

作　　者：向峰 李中志 熊熙[1,2] 李斌勇 XIANG Feng;LI Zhongzhi;XIONG Xi;LI Binyong(School of Cybersecurity,Chengdu University of Information Technology,Chengdu Sichuan 610255,China;Advanced Cryptography and System Security Key Laboratory of Sichuan Province(Chengdu University of Information Technology),Chengdu Sichuan 610225,China)

机构地区：[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

出　　处：《计算机应用》2023年第2期385-390,共6页journal of Computer Applications

基　　金：四川省科技计划项目(2021JDRC0046)。

摘　　要：参考点与参数的选取会对反距离权重(IDW)的精度产生影响。针对多参数协同优化反距离权重算法(PIDW)忽略局部特性的问题,提出一种利用粒子群对IDW进行局部优化的改进算法——PLIDW。首先,分别对研究区域中各个样本点的参数进行寻优,利用交叉验证方法进行评估,记录各自最优取值的一组参数;同时,为提高查询效率,使用K维树(KD-Tree)保存空间位置与最优参数;最后,根据空间邻近程度从K维树选取最近的一组参数优化IDW。基于模拟数据与真实的温度数据集上的实验结果表明,相较于PIDW,PLIDW在真实数据集上的准确度提高4.18%以上,改善了PIDW存在的因忽略局部特性导致部分场景准确度低的问题,适应能力更强。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.

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 

分 类 号：TP399[自动化与计算机技术—计算机应用技术]