【作者】 杜新忠； 谌宏伟； 柳林； 彭向训；

【Author】 Du Xinzhong;Chen Hongwei;Liu Lin;Peng Xiangxun;College of Hydraulic Engineering,Changsha University of Science and Technology;

【机构】 长沙理工大学水利学院；

【摘要】 水质模型的参数估计是水环境数值模拟的关键,目前水质模型参数估计的主要方法存在精度低、收敛速度慢及易陷入局部极小等缺陷。粒子群优化算法是近年发展起来的一种新的进化算法,具有实现容易、精度高、收敛快等优点。文章采用一种带惯性项的粒子群算法进行水质模型的参数估值,并在应用中加入了特定的程序模块,当其早熟收敛时,将粒子群打散,重新进行迭代计算,以达到全局最优值。应用此算法对Streeter-Phelps水质模型进行参数估算,实际算例表明该算法结果较优。更多还原

【Abstract】 Estimation of Water Quality Model Parameters is the key of water environment numerical simulation.Currently,the main methods of parameters estimation of Water quality model have defects of low precision,slow convergence and easily falling into local minimum.In this paper,particle swarm model with inertia was used for the estimation of water quality model parameters,and a specific module was joined in the application process.When the premature convergence happened,the particle swarm would be broken up and a new iterative method was conducted in order to achieve the global optimal value.Using this algorithm to estimate parameters of Streeter-Phelps water quality model of an example,a better result was gained.更多还原