Streeter-Phelps模型参数估计的遗传算法

提出Streeter-Phelps模型参数估计的新方法--遗传算法(Genetic Algorithm),它不同于常规参数估计方法,其优点在于,从多个初始点开始寻优,并采用交迭和变异运算避免过早地收敛到局部最优解,可获得全局最优解,且不受初始值影响.该方法不必求导计算,编程简单快捷.给出了实例计算及与其他方法相比较的结果.

A Genetic Algorithm for Parameter Optimization of Streeter Phelps Model

This paper presents a new method,called genetic algorithm(GA),for the parameter optimization of Streeter-phelps model.It is comparable in performance to existing mathematical programming methods.The GA uses a population of points at a time in contrast to the single-point approach by the traditional optimization ones,and will guarantee to find the global optimum.The optimal solution by GA is no longer dependent on initial search values.The GA uses randomized operators and works on coded variables.It does not require problem derivative.