• 典型应用 •

### 蚁群算法在需水预测模型参数优化中的应用

1. 1. 河南大学 环境与规划学院,河南 开封 475004
2. 宁夏大学 资源环境学院,银川 750021
3. 中国科学院 地理科学与自然资源研究所,北京 100101
• 收稿日期:2012-04-01 修回日期:2012-05-20 发布日期:2012-10-23 出版日期:2012-10-01
• 通讯作者: 侯景伟
• 作者简介:侯景伟(1973-),男,河南镇平人,博士,主要研究方向：GIS的开发与应用、最优化控制;孔云峰 (1967-),男,河南新安人,教授,博士生导师,主要研究方向：GIS分析与设计、空间优化、空间综合社会科学;孙九林 (1937-),男,江苏盐城人,工程院院士,博士生导师,主要研究方向：遥感与地理信息系统应用、虚拟地理环境、信息化农业、区域开发规划。
• 基金资助:
国家自然科学基金资助项目;高等学校博士学科点专项科研基金资助项目;省部共建河南大学科研项目

### Application of ant colony algorithm for parameter optimization of water demand prediction model

HOU Jing-wei1,2,KONG Yun-feng1,SUN Jiu-lin3

1. 1. College of Environment and Planning, Henan University, Kaifeng Henan 475004, China
2. School of Resources and Environment, Ningxia University, Yinchuan Ningxia 750021, China
3. Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101,China
• Received:2012-04-01 Revised:2012-05-20 Online:2012-10-23 Published:2012-10-01
• Contact: HOU Jing-wei

Abstract: To improve forecast accuracy of water demand when using Projection Pursuit (PP) model which are high-dimensional, non-normality and nonlinear, an Ant Colony Algorithm (ACA) was used for the parameter optimization of the model. ACA was improved to self-adaptive control pheromone on the grids divided by definitional domains of the model parameters. A case for water demand prediction was emulated according to the improved ACA and PP model. Then prediction accuracy from the improved ACA was compared with the results from Artificial Immune Algorithm (AIA) and BP Artificial Neural Network (BPANN) model, respectively. It is shown that: 1) the absolute relative errors of fitting accuracy are less than 2% from ACA and less than 10% from AIA and BPANN; 2) the absolute relative errors of prediction accuracy are less than 6%, 11% and 12% from ACA, AIA and BPANN, respectively; 3) ACA can converge to global optimal solution with higher convergence rate. Therefore, the improved ACA for optimizing the parameters of PP water demand prediction model is significantly better than the AIA and BPANN. This method can be applied to other similar high-dimensional and nonlinear problems.