计算机科学 ›› 2021, Vol. 48 ›› Issue (2): 224-230.doi: 10.11896/jsjkx.200600016
刘奇1, 陈红梅2, 罗川3
LIU Qi1, CHEN Hong-mei2, LUO Chuan3
摘要： 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39.827 8,1.10%,55.819 1。实验结果证明,提出的方法具有较高的可靠性。
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| [1] LI J,FAN J,LI M Z,et al.Discussion on the advantages of mo-dern inventory management in blood inventory optimization management[J].Chinese Journal of Blood Transfusion,2019,32(10):1038-1041. [2] KURUP R,ANDERSON A,BOSTON C,et al.A study onblood product usage and wastage at the public hospital,Guyana[J].BMC Research Notes,2016,9(1):307-312. [3] ZHAO H X,YU Q,CHEN F.Application of allocation mechanism in red blood cell inventory management in blood center[J].Journal of Clinical Hematology,2019,32(12):962-964. [4] YE M L,LI C L.Thinking on blood inventory management inblood station[J].China Medicine and Pharmacy,2019,9(12):255-257. [5] CUI Y L,ZHAO F J,JIA J,et al.Research analysis of the system of blood supply in Hebei province[J].Chinese Journal of Blood Transfusion,2018,31(4):418-423. [6] KHALDI R,ELAFIA A,CHIHEB R,et al.Artificial neuralnetwork based approach for blood demand forecasting:Fez transfusion blood center case study[C]//Proceedings of the 2nd International Conference on Big Data,Cloud and Applications.2017:1-6. [7] FANOODI B,MALMIR B,JAHANTIGH F F.Reducing de-mand uncertainty in the platelet supply chain through artificial neural networks and ARIMA models[J].Computers in Biology and Medicine,2019,113:103415. [8] FORTSCH S M,KHAPALOVA E A.Reduc-ing uncertainty in demand for blood[J].Operations Research for Health Care,2016,9:16-28. [9] ZHENG Y P,FAN L.A LSTM-based Forecast Method forClinical Blood Demand[J].Computer and Modernization,2018,273(5):45-48,124. [10] NANDI A K,ROBERTS D J,NANDI A K.Prediction paradigm involving time series applied to total blood issues data from England[J].Transfusion,2020,60(3):535-543. [11] BUKHARI A H,RAJA M A Z,SULAIMAN M,et al.Fractio-nal neuro-sequential ARFIMA-LSTM for financial market forecasting[J].IEEE Access,2020,8:71326-71338. [12] LI X,PENG L,YAO X,et al.Long short-term memory neural network for air pollutant concentration predictions:Method develop-ment and evaluation[J].Environmental polluteon,2017,231:997-1004. [13] WANG Y,LIU Y,WANG M,et al.Lstm model optimization on stock price forecasting[C]// 2018 17th International Symposium on Distributed Computing and Applications for Business Engineering and Science (dcabes).IEEE,2018:173-177. [14] HOLLAND J H.Genetic algorithms[J].Scientific American,1992,267(1):66-73. [15] MIRJALILI S,LEWIS A.The whale optimization algorithm[J].Advances in Engineering Software,2016,95:51-67. [16] KENNEDY J,EBERHART R.Particle swarm optimization[C]//Proceedings of ICNN'95-International Conference on Neural Networks.IEEE,1995:1942-1948. [17] SAREMI S,MIRJALILI S,LEWIS A.Gras-shopper optimisa-tion algorithm:theory and application[J].Advances in Enginee-ring Software,2017,105:30-47. [18] YANG X S,GANDOMI A H.Bat algorithm:a novel approach for global engineering optimization[J].Engineering computations,2012,29(5):464-483. [19] HOCHREITER S,SCHMIDHUBER J.Long short-term memory[J].Neural Computation,1997,9(8):1735-1780. [20] BARMAN M,CHOUDHURY N B D,SUTRADHAR S.A regional hybrid GOA-SVM model based on similar day approach for short-term load forecasting in Assam,India[J].Energy,2018,145:710-720. [21] TIZHOOSH H R.Opposition-based learning:a new scheme for machine intelligence[C]// International Conference on Computational Intelligence for Modelling Control and Automation and International Conference on Intelligent Agents,Web Technologies and Internet Commerce (CIMCAIAW-TIC'06).IEEE,2005,1:695-701. [22] ZHOU J,FANG W,WU X,et al.An opposition-based learning competitive particle swarm optimizer[C]//2016 IEEE Congress on Evolutionary Computation (CEC).IEEE,2016:515-521. [23] LONG W,WU T,CAI S,et al.A novel grey wolf optimizer algorithm with refraction learning[J].IEEE Access,2019,7:57805-57819. [24] ARORA S,ANAND P.Chaotic grasshopper optimization algorithm for global optimization[J].Neural Computing and Applications,2019,31(8):4385-4405. [25] LI J,CHENG Y,CHEN K.Chaotic particle swarm optimization algorithm based on adaptive inertia weight[C]//The 26th Chinese Control and Decision Conference(2014 CCDC).IEEE,2014:1310-1315. [26] SHAO P,WU Z,ZHOU X,et al.FIR digital filter design using improved particle swarm optimization based on refraction principle[J].Soft Computing,2017,21(10):2631-2642. |
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