地理研究

地理研究 ›› 2001, Vol. 20 ›› Issue (1): 97-102.doi: 10.11821/yj2001010014

• 论文 • 上一篇    下一篇

水质模型参数的非数值随机优化

郑红星, 李丽娟   

  1. 中国科学院地理科学与资源研究所, 北京 100101
  • 收稿日期:2000-03-17 修回日期:2000-06-15 出版日期:2001-02-15 发布日期:2001-02-15
  • 作者简介:郑红星(1973-),男,福建永春人,博士研究生.研究方向为水资源和水环境。
  • 基金资助:

    国家重点基础研究发展规划项目(G1999043602);中国科学院地理科学与资源研究所创新项目资助(CXIOG-A00-07)

Stochastic optimization on parameters of water quality model

ZHENG Hong xing, LI Li juan   

  1. Institute of Geographic Sciences and Natural Resources Reseach, CAS, Beijing 100101, China
  • Received:2000-03-17 Revised:2000-06-15 Online:2001-02-15 Published:2001-02-15

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被引次数

44 | 6

摘要：

以模拟退火算法为核心着重讨论了水质模型参数的非数值随机优化方法。实例分析表明,利用非数值随机优化方法(包括模拟退火算法和遗传算法)对水质模型参数进行估计,可以获得较为理想的结果。不同参数估计方法的比较进一步阐述了非数值随机优化方法在参数估计中的优点

关键词: 水质模型参数, 非数值算法, 随机优化, 模拟退火算法

Abstract:

This paper focuses on stochastic parameter optimization for water quality model with simulated annealing algorithm (SA) which is discussed in detail. For comparison, genetic algorithm (GA) and steepest decent algorithm (SD) are also discussed. Simultaneously, the typical S P water quality model is adopted in a case study. Result of the case study shows that the stochastic optimization methods (SA and GA) are more effective than the other methods such as the steepest decent method. What are testified include not only in the aspect of theory but also in the case study, both SA and GA are able to reach the global optimal results. However, concerning SA and GA, GA is weaker in local optimization and spends more time in parameter optimization.

Key words: stochastic optimization, simulated annealing algorithm, parameters, water quality model

PACS: 

  • P342