The stochastic dependent-chance programming belongs to a class of stochastic programming problems, which has wide application backgrounds, in order to search an algorithm which can more effectively solve this problem, in the paper, stochastic simulation is used to produce training samples for BP neural networks, and a hybrid intelligent algorithm for stochastic dependent-chance programming combined PSO algorithm with BP neural networks for approximation of the chance function is presented. Finally, the simulation results of two examples are given to show the correctness and effectiveness of the algorithm.
['Xiao Ning', 'Zeng Jian-chao']
A Hybrid Intelligent Algorithm for Stochastic Dependent-Chance Programming