| 水资源空间优化配置的群智能算法改进与仿真 |
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| 引用本文： | 侯景伟,吴建军.水资源空间优化配置的群智能算法改进与仿真[J].地球信息科学,2015,17(4):431-437. |
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| 作者姓名： | 侯景伟 吴建军 |
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| 作者单位： | 1. 宁夏大学资源环境学院, 银川 7500212. 宁夏沙漠信息智能感知重点实验室,银川 7500213. 开封大学旅游学院, 开封 475004 |
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| 基金项目： | 宁夏自然科学基金重点项目(NZ14002) |
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| 摘 要： | 本文尝试用群智能算法中的Pareto蚁群算法(PACA)求解复杂的水资源空间优化配置问题。首先,建立了以社会、经济和生态综合效益最大的目标函数,以水质、需水和供水为约束条件的水资源空间优化配置模型,并采用局部信息素强度限制,全局信息素动态更新等策略,对PACA进行改进,使蚂蚁向信息素浓度大的优化边界移动,以提高PACA的全局搜索能力和收敛速度。本文以河南省镇平县为仿真对象,借助RS和GIS,利用改进的PACA求解水资源空间优化配置模型,得到地表水、地下水、外调水的最优配置方案和最佳经济、社会、生态效益方案。通过对PACA性能指标的分析,以及对PACA改进前后解的寻优对比,表明了PACA经过改进后能有效地求解多目标、大规模的水资源空间优化配置模型,提高了寻优性能、收敛速度和全局搜索能力。
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| 关 键 词： | 优化配置 水资源 遥感 地理信息系统 Pareto蚁群算法 |
| 收稿时间： | 2013-10-08 |
Improvement and Simulation of Swarm Intelligence Algorithm for Spatial Optimal Allocation for Water Resources |
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| HOU Jingwei;WU Jianjun.Improvement and Simulation of Swarm Intelligence Algorithm for Spatial Optimal Allocation for Water Resources[J].Geo-information Science,2015,17(4):431-437. |
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| Authors: | HOU Jingwei;WU Jianjun |
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| Institution: | 1. School of Resouece and Environment, Ningxia University, Yinchuan 750021, China2. Ningxia Key Laboratory of Intelligent Sensing for Desert Information, Ningxia University, Yinchuan 750021, China3. Tourism College, Kaifeng University, Kaifeng 475004, China |
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| Abstract: | 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. |
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| Keywords: | optimal allocation water resources RS GIS Pareto Ant Colony Algorithm (PACA) |
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