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
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，结合最小克里金方差（MKV）和极大熵准则（ME）构建了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4类粒子微观邻域操作策略，能高效协调土壤空间抽样方案拟合精度与插值精度的多目标冲突。试验结果表明，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，能够获取较优的抽样方案，可为土壤质量精确调查与高效监测提供技术支持。...
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| Published in | 测绘学报 Vol. 42; no. 5; pp. 722 - 728 |
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| Main Author | |
| Format | Journal Article |
| Language | Chinese |
| Published |
武汉大学数字制图与国土信息应用工程国家测绘地理信息局重点实验室,湖北武汉 430079
2013
武汉大学地理信息系统教育部重点实验室,湖北武汉430079 武汉大学资源与环境科学学院,湖北武汉 430079 |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1001-1595 |
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| Summary: | 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，结合最小克里金方差（MKV）和极大熵准则（ME）构建了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4类粒子微观邻域操作策略，能高效协调土壤空间抽样方案拟合精度与插值精度的多目标冲突。试验结果表明，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，能够获取较优的抽样方案，可为土壤质量精确调查与高效监测提供技术支持。 |
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| Bibliography: | 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-PSQ, and integrates the constraints of sampling barriers, maximum sample size, survey budget and sampling interval as the neighbor operating rules of the particles. The MM-PS~ model could improve sampling accuracy and efficiency and determine sample size and spatial sampling pattern simultaneously. The method was applied 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 networ |
| ISSN: | 1001-1595 |