污水生化处理常常受入水流量和质量浓度变化的影响而处于动态反应过程，如何确定控制变量动态最优设定值是其控制优化的一个难题．文中结合有全局优化能力的蛙跳算法和有快速寻优特性的粒子群优化算法，提出了一种新的蛙跳粒子群算法，并通过对4 个典型函数的仿真实验来验证该算法寻优的效率与求解的精确性．然后将该算法应用于前置反硝化工艺的污水生化处理过程，在晴天、连绵雨天、暴雨天的仿真实验结果表明，该优化方案能够在保证前置反硝化工艺实现关键污染物有效去除的前提下，降低污水生化处理过程的运行代价．

As the biochemical process of wastewater is usually a dynamic process due to large perturbations in the influent flow rate and the pollutant load,it is difficult to determine the dynamic optimal set values of the control variables in the biochemical process of wastewater.In this paper,by combining the shuffled frog leaping algorithm (SFLA) of global optimization and the particle swarm optimization (PSO) algorithm of fast convergence,a hybrid optimal algorithm,namely,SFLA- PSO,is proposed.Then,the efficiency and accuracy of the proposed algorithm are verified by means of the simulation experiments of four typical functions.Finally,the SFLA- PSO algorithm is applied to the biochemical process of wastewater with pre- denitrification.Simulation results under the conditions of dry,rainy and stormy weathers demonstrate that the proposed algorithm can ensure the effective pollutant removal of the biochemical process of wastewater with pre- denitrification,and that it reduces the operation cost.