Parameter fitting of variogram based on hybrid algorithm of particle swarm and artificial fish swarm

被引:21
作者
Zhang, Xialin [1 ,2 ,3 ,4 ]
Lian, Lingkun [1 ]
Zhu, Fukang [1 ]
机构
[1] China Univ Geosci, Sch Comp Sci, Wuhan 430074, Peoples R China
[2] China Univ Geosci, Hubei Key Lab Intelligent Geoinformat Proc, Wuhan 430074, Peoples R China
[3] Engn Technol Innovat Ctr Mineral Resources Explor, Minist Nat Resources, Wuhan 550081, Peoples R China
[4] Intelligent Geol Resources & Environm Technol Hub, Wuhan 430074, Peoples R China
来源
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE | 2021年 / 116卷
基金
中国国家自然科学基金;
关键词
Variation function; Particle swarm algorithm; Artificial fish swarm algorithm; Parameter fitting;
D O I
10.1016/j.future.2020.09.026
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
Variation function is an important tool for describing the spatial correlation characteristics of regionalized variables in geostatistical methods. Variation function modeling is an important part of kriging interpolation and will directly affect the accuracy of the final interpolation result. The purpose of this work is to address the shortcomings of traditional variogram fitting methods, introduce particle swarm algorithm and artificial fish swarm algorithm under swarm intelligence framework, and design a variogram parameter fitting based on the hybrid algorithm of particle swarm and artificial fish swarm method. With this method, the minimum difference between the variation function fitting model and the given experimental variation value is utilized as the optimization goal. An appropriate objective function is set to convert it into a minimum problem. The hybrid algorithm has a strong search ability and convergence, as well as the ability to obtain the satisfactory fitness values. By comparing the results of the VARFIT fitting and the results of the optimization algorithm, it can be concluded that the absolute deviation of the fitting results of the optimization algorithm is 3.39 lower than the results of the VARFIT fitting. Compared with the traditional variogram modeling approach, this method has a strong optimization ability and high precision, and can effectively realize the automatic fitting of variogram parameters. (C) 2020 Published by Elsevier B.V.
引用
收藏
页码:265 / 274
页数:10
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