Using hybrid algorithm of simplex-annealing to estimate water quality parameters of river stream
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摘要: 将由单纯形法(SM)与模拟退火(SA)两种算法构成的混合算法(SMSA),应用于求解分析瞬时投放示踪剂情况下的一维河流水团示踪试验数据,估计河流水质参数的函数优化问题。分别就不同的降温指数、内循环次数与新状态产生函数中的扰动系数等算法控制参数对混合算法和改进模拟退火法的收敛速度的影响,进行了数值实验。结果表明,SMSA混合优化算法对于求解估计河流水质参数的函数优化问题是非常有效的。在实验条件下,与单一具有记忆功能的改进模拟退火法的水质参数计算结果相比较,SMSA混合算法具有:①混合算法的收敛速度明显优于改进模拟退火法;②降温指数和内循环次数对SMSA混合算法的收敛速度影响非常微弱;③新状态产生函数中的随机扰动幅度大小对算法收敛速度具有较为明显的影响等特点。Abstract: With the application of the hybrid algorithm of simplex method (SM) and the simulated annealing (SA) (SMSA),the function optimization problem is effectively solved in analyzing the data of water mass tracer test of river stream on the condition of instant injection of tracer to estimate such parameters as the longitudinal dispersion coefficient,the average stream velocity and the other parameter.With such different key parameters of algorithm as the number of inner loop,the cooling rate and the range of random disturbance in the new state generator,the simulation experiments are conducted to explore the effect of these key parameters on the convergence of the hybrid algorithm.The results show that the SMSA hybrid algorithm may be effectively applied to solve the function optimization problem of estimating water quality parameters of river stream,the conver-gence of hybrid algorithm is much better than that of improved simulated annealing with memory function in inner loop,the number of inner loop and the cooling rate have little effect on the convergence of hybrid algorithm,but much on that of improved SA,and the range of random disturbance in the new state generator may have clear evident effect on the convergence as on that of improved SA as well.
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