Stochastic optimization on parameters of water quality model
水质模型参数的非数值随机优化

This paper focuses on stochastic parameter optimization for water quality model with simulated annealing algorithm (SA) which is discussed in detail. For comparison, genetic algorithm (GA) and steepest decent algorithm (SD) are also discussed. Simultaneously, the typical S P water quality model is adopted in a case study. Result of the case study shows that the stochastic optimization methods (SA and GA) are more effective than the other methods such as the steepest decent method. What are testified include not only in the aspect of theory but also in the case study, both SA and GA are able to reach the global optimal results. However, concerning SA and GA, GA is weaker in local optimization and spends more time in parameter optimization.