An Improved Parallelized Multi-Objective Optimization Method for Complex Geographical Spatial Sampling: AMOSA-II
文献类型： 外文期刊
作者： Li, Xiaolan 1 ; Gao, Bingbo 2 ; Bai, Zhongke 1 ; Pan, Yuchun 2 ; Gao, Yunbing 2 ;
作者机构： 1.China Univ Geosci, Coll Land Sci & Technol, Beijing 100083, Peoples R China
2.Beijing Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China
3.China Agr Univ, Coll Land Sci & Technol, Beijing 100083, Peoples R China
4.Natl Engn Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China
5.Minist Agr, Key Lab Agri Informat, Beijing 100097, Peoples R China
关键词： multi-objective optimization; AMOSA-II; multiple Markov chains; parallelization
期刊名称：ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION （ 影响因子：2.899； 五年影响因子：2.971 ）
ISSN：
年卷期： 2020 年 9 卷 4 期
页码：
收录情况： SCI
摘要： Complex geographical spatial sampling usually encounters various multi-objective optimization problems, for which effective multi-objective optimization algorithms are much needed to help advance the field. To improve the computational efficiency of the multi-objective optimization process, the archived multi-objective simulated annealing (AMOSA)-II method is proposed as an improved parallelized multi-objective optimization method for complex geographical spatial sampling. Based on the AMOSA method, multiple Markov chains are used to extend the traditional single Markov chain; multi-core parallelization technology is employed based on multi-Markov chains. The tabu-archive constraint is designed to avoid repeated searches for optimal solutions. Two cases were investigated: one with six typical traditional test problems, and the other for soil spatial sampling optimization applications. Six performance indices of the two cases were analyzed computational time, convergence, purity, spacing, min-spacing and displacement. The results revealed that AMOSA-II performed better which was more effective in obtaining preferable optimal solutions compared with AMOSA and NSGA-II. AMOSA-II can be treated as a feasible means to apply in other complex geographical spatial sampling optimizations.
- 相关文献
作者其他论文 更多>>
-
A new approach to extract the upright maize straw from Sentinel-2 satellite imagery using new straw indices
作者：Zhou, Jingping;Gu, Xiaohe;Wu, Wenbiao;Pan, Yuchun;Sun, Qian;Zhang, Sen;Qu, Xuzhou;Zhou, Jingping;Liu, Cuiling;Sun, Qian;Zhang, Sen;Qu, Xuzhou
关键词：Upright maize straw; New straw index; Sentinel-2; Remote sensing; Decision tree
-
Quantitative Determination of Cd Using Energy Dispersion XRF Based on Gaussian Mixture Clustering-Multilevel Model Recalibration
作者：Gao, Yunbing;Zhao, Yanan;Pan, Yuchun;Sun, Wenbin;Zhao, Xiande;Liu, Xiaoyang;Li, Xue;Mao, Xuefei
关键词：
-
Deep learning in cropland field identification: A review
作者：Xu, Fan;Yao, Xiaochuang;Zhang, Kangxin;Feng, Quanlong;Yan, Shuai;Gao, Bingbo;Yang, Jianyu;Zhang, Chao;Zhu, Dehai;Yao, Xiaochuang;Feng, Quanlong;Gao, Bingbo;Yang, Jianyu;Zhang, Chao;Zhu, Dehai;Yang, Hao;Li, Ying;Li, Shaoshuai;Lv, Yahui;Ye, Sijing
关键词：Cropland field identification; Deep learning; Remote sensing; Bibliometric analysis; Sample dataset
-
Hyperspectral estimation of maize (Zea mays L.) yield loss under lodging stress
作者：Sun, Qian;Chen, Liping;Sun, Qian;Gu, Xiaohe;Qu, Xuzhou;Zhang, Sen;Zhou, Jingping;Pan, Yuchun;Chen, Liping;Qu, Xuzhou;Zhang, Sen
关键词：Maize; Lodging stress; Canopy hyperspectral; Yield loss; Feature selection
-
Spatial-temporal evolution of agricultural land utilization benefits and tradeoffs/synergies in the Beijing-Tianjin-Hebei region
作者：Yin, Ziyan;Tang, Linnan;Zhou, Wei;Yin, Ziyan;Liu, Yu;Tang, Linnan;Pan, Yuchun;Yin, Ziyan;Liu, Yu;Tang, Linnan;Pan, Yuchun
关键词：Agricultural land utilization benefits; Spatial-temporal evolution; Tradeoffs/synergies; County scale; the Beijing-Tianjin-Hebei region
-
Monitoring maize lodging severity based on multi-temporal Sentinel-1 images using Time-weighted Dynamic time Warping
作者：Qu, Xuzhou;Zhou, Jingping;Gu, Xiaohe;Sun, Qian;Pan, Yuchun;Wang, Yancang
关键词：Lodging severity; Maize; Time series; Time-weighted Dynamic Time Warping; (TwDTW); Jeffreys-Matusita (J-M)
-
AGTML: A novel approach to land cover classification by integrating automatic generation of training samples and machine learning algorithms on Google Earth Engine
作者：Cui, Yanglin;Zhou, Yanbing;Zhao, Chunjiang;Pan, Yuchun;Sun, Qian;Gu, Xiaohe;Cui, Yanglin;Yang, Gaoxiang;Sun, Qian
关键词：Land cover mapping; Landsat 8; Machine learning; Optimal focal radii; Training sample generation
