论文部分内容阅读
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。
This paper attempts to solve complex water resources optimization problem with Pareto Ant Colony Algorithm (PACA) in swarm intelligence algorithm. First of all, a model of water resources optimization based on the objective function of comprehensive social, economic and ecological benefits, water resources, water supply and water supply constraints is established, and the strategy of local pheromone intensity limitation and global pheromone updating are established , The PACA is improved so that the ants move to the optimal pheromone concentration boundary to improve the global search ability and convergence speed of PACA. In this paper, Zhenping County in Henan Province as a simulation object, with RS and GIS, the use of improved PACA to solve the optimal allocation of water resources space model, the optimal allocation of surface water, groundwater, water transfer and the best economic, social, ecological Benefit plan. Through the analysis of performance index of PACA and the comparison of the solutions before and after the improvement of PACA, it shows that the improved PACA can effectively solve the multi-objective and large-scale optimization model of water resources allocation and improve the performance of optimization and convergence And global search capabilities.