Combination of Pareto ant colony algorithm with remote sensing for optimal allocation of water resources
文献类型：EI期刊论文
| 作者 | Sun Jiu-Lin |
| 发表日期 | 2012 |
| 关键词 | Water resources Genetic algorithms Mathematical models Multiobjective optimization Neural networks Pareto principle Remote sensing Water quality Water supply |
| 英文摘要 | To solve the optimal allocation problem of water resources with Pareto ant colony algorithm (PACA) and remote sensing (RS), we develop an optimization model in pixel scales. This model produces the largest social, economic, and environmental benefits under constraints on water supply, water demand and water quality. By limiting the local pheromone scope, dynamically updating the global pheromone and filtering the Pareto solution set, we improve the PACA to make ants move towards the optimal border with higher pheromone density, and enhance the global search capability and raise the convergence rate. To validate the feasibility and effectiveness of the PACA, a county in central China is selected as the simulation object, from which the data of the land-use pattern is obtained by using the RS technology. By solving the multi-objective model, we obtain the optimal allocation scheme for water resources with the aid of PACA on a raster map. Performance and convergence of the PACA are compared with those of the genetic algorithm (GA) and BP neural network algorithm (BP-ANN); results show that PACA can effectively solve the large-scale, multi-objective optimization model of water resources with stronger global search capability and higher convergence rate and precision. |
| 出处 | Kongzhi Lilun Yu Yingyong/Control Theory and Applications
 |
| 卷 | 29期:9页:1157-1162 |
| 收录类别 | EI |
| 语种 | 英语 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/31154]  |
| 专题 | 地理科学与资源研究所_历年回溯文献 |
| 推荐引用方式 GB/T 7714 | Sun Jiu-Lin. Combination of Pareto ant colony algorithm with remote sensing for optimal allocation of water resources. 2012. |
入库方式： OAI收割
来源：地理科学与资源研究所
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