| 摘要: |
| 【目的】将改进的粒子群优化算法应用于河流水质模型参数求解，为预估河流水质参数提供一种有效的方法。【方法】以粒子群算法为基础，用混沌序列的产生过程模拟粒子初始化提高算法的全局搜索能力，加入单纯形算法提高计算精度，建立改进的粒子群优化算法。用改进的粒子群优化算法对一维及二维河流水质模型参数进行求解，并进行实例验证。【结果】改进的粒子群优化算法可以有效地应用于一维及二维河流水质模型参数的求解；随着参数取值区间的不断扩大，算法的运算时间增加；改进的粒子群优化算法比粒子群优化算法具有更好的收敛性且计算精度更高。【结论】改进的粒子群优化算法能改善原算法易陷入局部最优解的不足，是分析河流水团示踪试验数据、预估一维及二维河流水质模型参数的一种有效方法。 |
| 关键词: 环境水利 河流水质模型 参数估计 粒子群优化算法 横向扩散系数 |
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| 基金项目:国家自然科学基金项目(51009009，51379014)；教育部地下水文与生态效应重点实验室开放基金项目(2013G1502044) |
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| Modified particle swarm optimization algorithm based estimation of river water quality parameters |
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YUAN Hua,LIU Yuan-hui,GUO Jian-qing,et al
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| Abstract: |
| 【Objective】This study adopted modified particle swarm optimization algorithm for solving river water quality models to obtain an effective model for estimation of river water quality parameters.【Method】Chaos optimization algorithm was incorporated into particle swarm optimization algorithm to improve global search capability.Then the modified particle swarm optimization algorithm was established with improved accuracy using simplex algorithm.The modified particle swarm optimization algorithm was used to solve one dimensional and two dimensional water quality models.【Result】The modified particle swarm optimization algorithm was successfully used in estimating water quality parameters for one dimensional and two dimensional models.The range of initial values had influences on computation time.The modified algorithm had better convergence and higher accuracy.【Conclusion】The modified algorithm was proved to be an effective way to estimate parameters for river water quality models. |
| Key words: environmental hydraulics water quality model of river parameter estimation particle swarm algorithm transversal diffusion coefficient |
