| 多目标微观邻域粒子群算法及其在土壤空间优化抽样中的应用 |
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| 引用本文： | 刘殿锋,刘耀林,赵翔.多目标微观邻域粒子群算法及其在土壤空间优化抽样中的应用[J].测绘学报,2013,42(5):722-728. |
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| 作者姓名： | 刘殿锋 刘耀林 赵翔 |
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| 作者单位： | 武汉大学资源与环境科学学院 |
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| 基金项目： | 国家863计划(2011AA120304);中国博士后科学基金(2012M511253);中央高校基本科研业务费专项资金青年教师资助项目(121044) |
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| 摘 要： | 提出一种基于多目标微观邻域粒子群的土壤空间优化抽样方法。方法面向土壤空间调查的多目标特征，构建了基于最小克里金方差（MKV）和极大熵准则（ME）的粒子群多目标适应度函数，设计了最小样本量限制、样点可达性、采样成本限制和最小空间关联性四类粒子微观邻域操作策略，能高效协调土壤空间抽样精度、代表性、成本、样本量与样点布局等多目标冲突。实验结果表明，相比单目标粒子群算法和模拟退火算法，该方法的目标冲突协同能力强、收敛效率高，所设计抽样方案最优，为土壤质量精确调查与高效监测提供了技术支持。
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| 关 键 词： | 土壤抽样 多目标粒子群 微观邻域 地统计学 |
| 收稿时间： | 2012-05-14 |
| 修稿时间： | 2013-01-06 |
Soil Spatial Sampling design based on a Multi-objective Micro-neighborhood Particle Swarm Optimization Algorithm |
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| LIU Dianfeng;LIU Yaolin;ZHAO Xiang.Soil Spatial Sampling design based on a Multi-objective Micro-neighborhood Particle Swarm Optimization Algorithm[J].Acta Geodaetica et Cartographica Sinica,2013,42(5):722-728. |
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| Authors: | LIU Dianfeng;LIU Yaolin;ZHAO Xiang |
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| Institution: | LIU Dianfeng;LIU Yaolin;ZHAO Xiang;School of Resources and Environment Science,Wuhan University;Key Laboratory of Geographic Information System,Ministry of Education,Wuhan University;Key Laboratory of Digital Mapping and Land Information Application Engineering,National Administration of Surveying,Mapping and Geoinformation,Wuhan University; |
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| Abstract: | The design of a soil spatial sampling network is a complex optimization problem, which must reconcile the conflicts between survey budget, sampling efficiency, sample size and spatial pattern of soil variables. This study presents a soil spatial sampling model on the basis of a multi-objective micro-neighborhood particle swarm optimization algorithm (MM-PSO). The model combines minimum mean kriging variance (MKV) and maximum entropy (ME) as the fitness function of the MM-PSO, and integrates the constraints of sampling barriers, maximum sample size, survey budget and sampling interval as the neighbor operating rules of the particles, in order to improve sampling accuracy and efficiency and to determine sample size and spatial sampling pattern simultaneously. We applied the method to optimizing the sampling networks for soil organic matter in Hengshan County in north-west China. The results indicate that the MM-PSO features a good convergence ability and stability, and can obtain better sampling networks with higher fitness values of the objectives than the single objective and spatial simulated annealing algorithm. |
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