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.
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