基于改进的蝗虫优化算法的红细胞供应预测方法

当前国内“血荒”问题比较严峻,血站与用血单位之间存在着血液供不应求的现象。针对这个问题,提出了一种基于改进的蝗虫优化算法的LSTM预测方法,用于对未来的红细胞供应情况进行预测,为血站工作人员在制定采血计划以及制备计划时提供有效的指导。该预测模型通过使用长短期记忆网络(Long-Short Term Memory Network,LSTM)来捕捉历史红细胞库存数据之间的潜在规律,以达到对未来的供应情况进行预测的效果。首先,针对蝗虫优化算法容易陷入局部最优、收敛速度较慢的问题,通过加入基于折射原理的反向学习机制与混沌映射,加快蝗虫优化算法的收敛速度,使其具备更强的搜索能力。其次,为提高LSTM的预测性能,将改进的蝗虫优化算法与LSTM相结合,并使用某地区的红细胞库存真实数据作为实验数据,用于验证改进的LSTM预测模型的性能。与标准LSTM相比,所提方法的MAE,MAPE,RMSE分别降低了39.8278,1.10%,55.8191。实验结果证明,提出的方法具有较高的可靠性。

Method for Prediction of Red Blood Cells Supply Based on Improved Grasshopper Optimization Algorithm

At present,the problem of blood supply shortage is quite serious.There exists the phenomenon that short supply happen between blood stations and institutions that use blood.Aiming at such a problem,the LSTM prediction method based on the improved grasshopper optimization algorithm(GOA)is proposed in order to predict red blood cells supply in the future and provide effective guidance for workers in making blood collection plan and preparation plan.By using LSTM to capture the under-lying patterns between the historical data,the effect of predicting the future can be achieved.There are two parts of work.Firstly,aiming at the problem that the conventional GOA is easy to fall into local optimum and has a slower convergence speed,the model of refracting opposite-based learning and chaotic mapping are introduced to GOA so as to improve the global exploration capability.Secondly,in order to improve the accuracy of LSTM,it is combined with the improved GOA and evaluate the perfor-mance of the improved LSTM model by using the real data of red blood cells supply in a certain area.Comparing to the conventional LSTM,the MAE,MAPE,RMSE are reduced by 39.8278,1.10%,55.8191,respectively.The experimental results show that the proposed method has higher reliability.