改进BFO优化BPNN的自来水混凝加药预测

本文给出一种量子粒子群（QPSO）算法、改进菌群觅食（IBFO）算法优化反向传播神经网络（BPNN）的混凝投药预测模型，利用量子粒子群的个体极值与群体极值更新细菌觅食算法趋化过程中细菌位置；通过细菌协同改进趋化算子提高优化精度，结合差分算法改进繁殖算子解决部分维度退化问题，加入轮盘赌方法作为选择机制改进迁移算子来克服优化过程中优秀解消失的缺陷；进而优化BP神经网络的权值、阈值以此预测混凝剂投药量.对云南某自来水厂的数据进行离线训练和模型测试，结果表明，所提算法预测结果的均方误差（MSE）达0.0116mg/L，平均绝对误差百分比（MAPE）达1.36%，在预测精度和稳定性上优于BFO-BPNN、PSO-BPNN等模型.

Prediction model for tap water coagulation dosing based on BPNNoptimizedwith improved BFO

In this paper, a prediction control model was proposed, which was designed with BPNN optimized by the hybrid algorithm with quantum particle swarm optimization (QPSO) and improved bacterial foraging (IBFO). In this strategy, the individual and population extremum of quantum particle swarmoptimizationwere used to update the bacterial positions in the chemotaxis process for BFO. The chemotaxis operator wasupgraded through bacteria synergy to improve the optimization accuracy. The reproduction operator was improved with difference method to solve the problem of partial dimension degradation. The roulette measure was applied as the selection mechanism to perfect the migration operator, which could overcome the disadvantage of the disappearance for the excellent solutions in the optimization process. Finally, the weights and thresholds of BP neural network were optimized to work out the coagulant dosage. Off-line training andtesting fordata model of one waterworks in Yunnan showed that the mean square error (MSE) of the prediction results of the proposed algorithm was 0.0116mg/L, and the mean absolute percentageerror (MAPE) was 1.36%, which weresuperior toBFO-BPNN and PSO-BPNN models in prediction accuracy and stability.