一种求解随机期望值模型的有效算法

An efficient algorithm for solving stochastic expected value models

随机期望值模型是一类有着广泛应用背景的随机规划问题.为了寻找更为有效的求解随机期望值模型的算法,通过采用随机仿真来逼近随机函数,在微粒群算法中利用随机仿真进行适应值估计和实现为了检验解的可行性,从而给出了求解随机期望值模型的新的算法.最后,通过实例仿真说明了算法的正确性和有效性.

The stochastic expected value model is a class of stochastic programming problems with wide applicability. In order to find a more effective algorithm for solving these problems, we used random simulations to approach the stochastic function in particle swarm optimization, obtaining an estimation of the degree of fitness and verifying the feasibility of the solution. Finally, results of simulations show the correctness and effectiveness of this algorithm.