求解随机机会约束规划的混合智能算法及应用

为更有效地求解随机机会约束规划问题，提出一种基于克隆选择算法(CSA)、随机模拟技术及神经网络的混合智能算法。采用随机模拟技术产生随机变量样本矩阵训练反向传播(BP)网络以逼近不确定函数，之后在CSA中利用神经网络检验个体的可行性、计算适应度，从而得到优化问题的最优解。为保证算法搜索的快速性和有效性，CSA采用双克隆和双变异策略。仿真结果表明,与已有算法相比,混合智能算法在500代时已取得比较满意的结果，且其精度在单目标优化问题中提高了2.2%，在多目标优化问题中提高了65%;将该算法应用于求解水库优化调度的难题上,结果也表明所建立的模型及算法的可行性和有效性。

Hybrid intelligent algorithm for solving stochastic chance-constrained programming and its application

In order to find an algorithm which can solve the Stochastic Chance-Constrained Programming(SCCP) problem more effectively,a hybrid intelligence algorithm based on Clonal Selection Algorithm(CSA),random simulation technology and neural network was proposed.Random simulation was used to produce random variables sample matrix for training Back Propagation(BP) neural network to approximate the stochastic function.Fitness value was calculated and feasible solution was checked by the trained neural network in CSA until it could get the solution to the optimization problems.In order to make the searching rapid and effective,double cloning operators and double mutation operators were adopted in CSA.The simulation results show that satisfactory result has been achieved before 500 generation;moreover,the precision in the single objective optimization problem is improved by 2.2% and the precision in multi-objective optimization problems is increased by 65% compared with other existing algorithms.In addition,the algorithm was applied to solve the problem of optimal reservoir scheduling.The simulation results also show the correctness and effectiveness of the model and the algorithm.