Abstract: Stochastic chance-constrained programming belongs to a class of stochastic programming problems，in the paper，random simulation is used to produce training samples for BP neural network to approximate the stochastic function，fitness value is calculated and feasible solution is checked by the trained neural network in PSO，and a hybrid intelligent algorithm for stochastic chance-constrained programming is presented.Finally，the simulation results show the correctness and effectiveness of the algorithm.

摘要： 随机机会约束规划是一类有着广泛应用背景的随机规划问题，采用随机仿真产生样本训练BP网络以逼近随机函数，然后在微粒群算法中利用神经网络计算适应值和实现检验解的可行性，从而提出了一种求解随机机会约束规划的混合智能算法。最后通过两个实例的仿真结果说明了算法的正确性和有效性。