Journal of Geo-information Science >
Improvement and Simulation of Swarm Intelligence Algorithm for Spatial Optimal Allocation for Water Resources
Received date: 2013-10-08
Request revised date: 2013-11-09
Online published: 2015-04-10
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In order to solve spatial optimal allocation problem of water resource with multi-objective functions and multi-constrained conditions, Pareto ant colony algorithm (PACA) is used in this study. The model for spatial optimal allocation of water resources is established. Its objective function is the largest benefits from economy, society and environment. And its constraints include water supply, water demand and water quality. PACA is improved according to such strategies as limiting local pheromone scope and dynamically updating global pheromone. Then, GIS software is developed with the help of VB. NET 2008, ArcGIS Engine and Access. Zhenping County, Henan Province, China is selected as a study area. Data about water resources in the study area are handled using RS and GIS technology. The model is solved with PACA in the GIS environment. Spatial optimal allocation schemes of water resources, including surface water, groundwater and transfer water, are obtained. And spatial optimal benefit schemes of water resources, including economic, social and ecological benefits are also obtained. The optimal results obtained from PACA are compared with other intelligent optimization algorithms. Robustness performance, optimal performance and time performance of the improved PACA are 5.38, 0.398 and 21.6, respectively. The three performances of the ACA, however, are 8.16, 2.108 and 36.8, respectively. The results indicate that the integration of RS, GIS and PACA can effectively improve the performance of large-scale, multi-objective optimization model of water resources. This method can enhance the global search capability, the convergence speed and the result’s precision.
Key words: optimal allocation; water resources; RS; GIS; Pareto Ant Colony Algorithm (PACA)
HOU Jingwei , WU Jianjun . Improvement and Simulation of Swarm Intelligence Algorithm for Spatial Optimal Allocation for Water Resources[J]. Journal of Geo-information Science, 2015 , 17(4) : 431 -437 . DOI: 10.3724/SP.J.1047.2015.00431
Fig.1 Natural conditions in Zhenping County图1 镇平县自然概况 |
Fig.2 Spatial distribution of water demand in Zhenping County图2 镇平县需水量空间分布(m3/ pixel.a) |
Fig.3 Interface of optimal allocation for water resources图3 水资源优化配置的界面 |
Fig.4 Optimal spatial allocation schemes图4 最佳空间优化配置方案(m3/ pixel.a) |
Fig.5 Optimal benefit schemes图5 最佳效益方案 |
Tab.1 Performance validation of the improved PACA表1 改进PACA的性能验证 |
算法类型 | 鲁棒性能指标 | 最佳性能指标 | 时间性能指标 |
---|---|---|---|
改进PACA | 5.38 | 0.397 | 21.6 |
基本蚁群算法 | 8.16 | 2.108 | 36.8 |
Fig.6 Optimal contrast of the solutions before and after improvement of PACA图6 PACA改进前、后解的寻优对比 |
The authors have declared that no competing interests exist.
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