蚁群算法在需水预测模型参数优化中的应用

摘要

To improve forecast accuracy of water demand when using Projection Pursuit (PP) model which are high-dimensional, non-normality and nonlinear, an Ant Colony Algorithm (ACA) was used for the parameter optimization of the model. ACA was improved to self-adaptive control pheromone on the grids divided by definitional domains of the model parameters- A case for water demand prediction was emulated according to the improved ACA and PP model. Then prediction accuracy from the improved ACA was compared with the results from Artificial Immune Algorithm (AIA) and BP Artificial Neural Network (BPANN) model, respectively. It is shown that: 1) the absolute relative errors of fitting accuracy are less than 2% from ACA and less than 10% from AIA and BPANN;2) the absolute relative errors of prediction accuracy are less than 6%, 11% and 12% from ACA, AIA and BPANN, respectively;3) ACA can converge to global optimal solution with higher convergence rate. Therefore, the improved ACA for optimizing the parameters of PP water demand prediction model is significantly better than the AIA and BPANN. This method can be applied to other similar high-dimensional and nonlinear problems.%为了解决投影寻踪(PP)需水预测模型的高维、非正态、非线性参数优化问题,提高需水预测的精度,尝试用基于网格划分的自适应连续域蚁群算法(ACA)在不同拟合和预测时长内对模型参数进行优化组合,并运用该模型进行年需水量预测.基于改进蚁群算法的投影寻踪需水预测模型参数优化进行了实例仿真.对基于改进蚁群算法的预测精度与基于人工免疫算法(AIA)和BP神经网络的模型(BPANN)参数优化结果分别进行了比较,实验结果表明:1)这三种算法的拟合精度相对误差绝对值分别小于2％、10％和10％；2)预测精度相对误差绝对值分别小于6％、11％和12％；3)改进蚁群算法能收敛到全局最优解,收敛速度较快.因此,改进蚁群算法的投影寻踪需水预测结果明显优于人工免疫算法和BP神经网络.该方法可推广到其他类似的高维非线性问题上.