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基于智能优化算法的河流水质模型参数的优化

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刘韵, 李祚泳, 汪嘉杨. 基于智能优化算法的河流水质模型参数的优化[J]. 环境工程学报, 2014, 8(2): 488-492.
引用本文: 刘韵, 李祚泳, 汪嘉杨. 基于智能优化算法的河流水质模型参数的优化[J]. 环境工程学报, 2014, 8(2): 488-492.
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Liu Yun, Li Zuoyong, Wang Jiayang. Optimization of water quality model parameters for river based on intelligent optimization algorithm[J]. Chinese Journal of Environmental Engineering, 2014, 8(2): 488-492.
Citation: Liu Yun, Li Zuoyong, Wang Jiayang. Optimization of water quality model parameters for river based on intelligent optimization algorithm[J]. Chinese Journal of Environmental Engineering, 2014, 8(2): 488-492.
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基于智能优化算法的河流水质模型参数的优化

  • 1.

    成都信息工程学院资源环境学院, 成都 610225

  • 基金项目:

    国家自然科学基金资助项目（51209024）

  • 中图分类号: X824

Optimization of water quality model parameters for river based on intelligent optimization algorithm

  • 1.

    College of Resourges and Environment, Chengdu University of Information Technology, Chengdu 610225, China

  • Fund Project:

  • 摘要: 为探索河流水质模型参数新的求解方法，根据有限的实测数据，分别应用免疫进化优化算法和免疫进化优选的捕食搜索算法，对河流水质模型计算公式中的多参数进行优化。将优化得到的计算公式用于国内外若干河流的河段中DO浓度值的拟合，并与实测结果进行了比较。结果表明，将免疫进化优化算法或免疫进化优选的捕食搜索算法优化得到的水质模型参数精度不仅较高，而且相对稳定，从而为河流水质模型参数的优化提供了一种新方法。
    Abstract: The study was to explore the new method of water quality model parameters for river. According to the limited experimental data, the parameters in the formula of water quality model for river were optimized using immune evolutionary algorithm and predatory search strategy of immune evolutionary optimization method respectively. The optimized formula was applied to the effects of the fitting of DO concentration in the reaches of certain rivers of domestic and foreign, and compared with measured results. The results showed that the water quality model parameters optimized using immune evolutionary algorithm or predatory search strategy of immune evolutionary algorithm have higher accuracy and better stability. This study provides a new method for the parameter optimization of water quality model for river.
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  • 收稿日期:  2013-03-14
  • 刊出日期:  2014-01-27

基于智能优化算法的河流水质模型参数的优化

  • 1. 成都信息工程学院资源环境学院, 成都 610225
  • 收稿日期:  2013-03-14
基金项目:

国家自然科学基金资助项目（51209024）

摘要: 为探索河流水质模型参数新的求解方法，根据有限的实测数据，分别应用免疫进化优化算法和免疫进化优选的捕食搜索算法，对河流水质模型计算公式中的多参数进行优化。将优化得到的计算公式用于国内外若干河流的河段中DO浓度值的拟合，并与实测结果进行了比较。结果表明，将免疫进化优化算法或免疫进化优选的捕食搜索算法优化得到的水质模型参数精度不仅较高，而且相对稳定，从而为河流水质模型参数的优化提供了一种新方法。

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