Optimization of the sampling design for multiobjective soil mapping using the multiple path SSA (MP-SSA) method
文献类型： 外文期刊
作者： Gao, Bingbo 1 ; Chen, Ziyue 2 ; Gao, YunBing 3 ; Hu, Maogui 4 ; Li, Xiaolan 3 ; Pan, Yuchun 3 ;
作者机构： 1.China Agr Univ, Coll Land Sci & Technol, 2 Yuanmingyuan West Rd, Beijing 100193, Peoples R China
2.Beijing Normal Univ, Coll Global Change & Earth Syst Sci, 19 Xinjiekouwai St, Beijing 100875, Peoples R China
3.Beijing Acad Agr & Forestry Sci, Res Ctr Informat Technol, 11 Shuguang Huayuan Middle Rd, Beijing 100097, Peoples R China
4.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, LREIS, A11 Datun Rd, Beijing 100101, Peoples R China
关键词： Sampling design; Multiobjective optimization; Soil mapping; MP-SSA
期刊名称：CATENA （ 影响因子：6.367； 五年影响因子：6.497 ）
ISSN： 0341-8162
年卷期： 2022 年 217 卷
页码：
收录情况： SCI
摘要： Spatial sampling is important for soil surveys and mapping, and the optimization of the sampling design is a hot topic. Most often in soil sampling, multiple purposes are usually involved and corresponding objectives need to be optimized as much as possible. In such cases, balanced optimization is needed to produce the best compromised solutions that reach the maximum common interest, but not to generate a well-spread Pareto front. To solve this problem, the multiple path spatial simulated annealing (MP-SSA) was developed by extending the classic SSA. It can synchronously optimize multiobjective functions of different types and magnitudes by setting one annealing path for each objective, and designing a voting and annealing mechanism. To illustrate the difference and performance for MP-SSA, it was compared with the archived multiobjective simulated annealing (AMOSA) and Non-dominated Sorting Genetic Algorithm II (NSGA-II), both aiming at generating well-spread Pareto front, in two case studies with hypothetical data and actual soil heavy metal data. The results show that the MP-SSA is more efficient in generating the best compromised solutions, and is an efficient and promising tool for balanced multiobjective optimization for spatial sampling design when all objectives need to be optimized as much as possible.
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