水资源空间优化配置的群智能算法改进与仿真

发布时间：2018-03-01 03:32

本文关键词： 优化配置水资源遥感地理信息系统 Pareto蚁群算法　出处：《地球信息科学学报》2015年04期 　论文类型：期刊论文

【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。
[Abstract]:This paper attempts to solve the complex spatial optimal allocation of water resources by using the Pareto ant colony algorithm in the swarm intelligence algorithm. Firstly, the objective function with the greatest comprehensive benefit of society, economy and ecology is established, and the water quality is used. The spatial optimal allocation model of water resources with water demand and water supply constraints is proposed. The local pheromone intensity restriction and global pheromone dynamic updating are used to improve the PACA to make ants move to the optimal boundary with high pheromone concentration. In order to improve the global search ability and convergence speed of PACA, this paper takes Zhenping County, Henan Province as the simulation object, using RS and GIS to solve the spatial optimal allocation model of water resources by using improved PACA, and obtains surface water and groundwater. Through the analysis of the performance index of PACA and the comparison of the solution before and after the improvement of PACA, it is shown that PACA can effectively solve the multi-objective after the improvement. The large-scale spatial optimal allocation model of water resources improves the optimization performance, convergence speed and global search ability.
【作者单位】：宁夏大学资源环境学院;宁夏沙漠信息智能感知重点实验室;开封大学旅游学院;
【基金】：宁夏自然科学基金重点项目(NZ14002)
【分类号】：P208;TV213.4;TP18

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