水污染控制系统问题的随机机会约束规划模型及其混合智能算法

Stochastic chance-constrained programming model & hybird genetic algorithms for water pollution control system

在水流量为随机变量且河流中工业污水含量标准给定的条件下,以极小化污水处理费用为目标,建立了水污染控制系统问题的随机机会约束规划模型。鉴于传统方法求解随机规划较为困难,给出了一个将随机模拟、神经元网络及遗传算法相结合的混合智能算法来求解该模型,并用算例进行了验证,结果表明该算法有较强的适应性。

A stochastic chance-constrained programming model for water pollution control system with minimizing sewage treatment cost is established under the condition that water current capacity is a stochastic variable and industrial sewage content standard in rivers is given. Then a hybird intelligent algorithm integrating stochastic simulation, neural network and genetic algorithm is designed to solve this model because traditional methods solving stochastic programming are difficult. Finally, a numerical experiment is given in order to illustrate the effectiveness of the algorithm. The result indicates the algorithm has better compatibility.