粒子群优化算法在确定河流水质参数中的应用

Application of particle swarm optimization algorithms to determination of water quality parameters of river streams

将粒子群优化(PSO)算法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,以及确定河流水质参数的函数优化问题。分别就粒子数目和待估水质参数的初始取值范围对算法运算过程的影响进行了数值实验。结果表明:①PSO算法能够有效地应用于求解分析河流水质试验数据,确定水质参数的函数优化问题;②粒子数目的多少对迭代次数、运算时间和算法是否收敛有一定的影响,在粒子数目较大的情况下,可以保证运算过程收敛;③待估参数初始猜测值的选取范围对迭代次数也有一定的影响,选取范围越大,需要的迭代次数越多。最后,指出了需要进一步研究的问题。

The particle swarm optimization （PSO） algorithms were applied to analysis of 1D tracing test data of river streams with tracers instantaneously injected, and further to optimization of functions to estimate the water quality parameters of river streams. The influences of the number of particles and the range of initial values adopted for water quality parameters to be evaluated on the convergence of the PSO algorithms were studied by numerical experiments. The result shows that the PSO algorithms are effective for function optimization to estimate water quality parameters of river streams with tracing test data. Some conelusions are also drawn： the convergence of PSO algorithms and the computation time are influenced by the number of particles and the large number of particles can ensure the convergence of the calculation; the range of initial values of the parameters to be evaluated is of certain influences on the number of iteration, and the larger the range, the more the iteration number and the computation time, Finally, some problems for further research were put forward.