计算机科学 ›› 2023, Vol. 50 ›› Issue (5): 292-301.doi: 10.11896/jsjkx.220300259
朱旭辉1,2, 佘孝敏1,2, 倪志伟1,2, 夏平凡1,2, 张琛3
ZHU Xuhui1,2, SHE Xiaomin1,2, NI Zhiwei1,2, XIA Pingfan1,2, ZHANG Chen3
摘要: 焦炭是焦化企业生产的重要工业原料之一,准确地预测其未来价格趋势对焦化企业制定排产计划具有重要意义。极限学习机(ELM)泛化能力强,计算速度快,适合作为焦炭价格预测的模型,但ELM的预测性能受模型关键参数影响较大,故需对其参数进行优化。基于此,文中提出了基于双精英进化樽海鞘群算法的ELM焦炭价格预测方法。首先,采用Logistic混沌映射、改进的收敛因子、自适应惯性权重和双精英进化机制来改进樽海鞘群算法,提出了双精英进化樽海鞘群算法(MDSSA),提高算法的搜索能力;其次,运用MDSSA优化ELM的连接权值与阈值,找到ELM的最优参数组合,构建MDSSA-ELM焦炭价格预测模型;最后,在8个基准测试函数上测试MDSSA的收敛性能,在实际焦炭价格数据集上对MDSSA-ELM模型的预测性能进行实验,实验结果表明,MDSSA-ELM相比其他方法预测能力更优,MDSSA相比其他群智能算法搜索能力更强,为焦化企业实现焦炭智慧排产提供了有效的预测工具。
中图分类号:
[1]PADILLA W R,GARCÍA J,MOLINA J M.Improving time series forecasting using information fusion in local agricultural markets[J].Neurocomputing,2021,452:355-373. [2]TAI V V,CHENGOC H,LEDAI N,et al.A new strategy for short-term stock investment using bayesian approach[J].Computational Economics,2021,59(2):887-911. [3]DHANAPAL R,AJANRAJ A,BALAVINAYAGAPRAGATHI-SH S,et al.Crop price prediction using supervised machine learning algorithms[J].Journal of Physics:Conference Series,2021,1916(1):012042. [4]YANG B Q,ZHANG X L.Forecast of price of rare earths neodymium oxide and dysprosium oxide based on ARIMA time series model[J].Journal of the Chinese Society of Rare Earths,2017,35(5):680-686. [5]DU Y A.Research on the route pricing optimization model ofthe car-free carrier platform based on the BP neural network algorithm[J].Complexity,2021,2021(4):8204214. [6]E J W,YE J M,HE L L,et al.A denoising carbon price foreca-sting method based on the integration of kernel independent component analysis and least squares support vector regression[J].Neurocomputing,2021,434:67-79. [7]HUANG G B,WANG D H,LAN Y.Extreme learning ma-chines:a survey[J].International Journal of Machine Learning and Cybernetics,2011,2(2):107-122. [8]XU H X,MA C L,FENG H.A thrust allocation method based on extreme learning machine[J].Journal of Huazhong Univer-sity of Science and Technology(Natural Science Edition),2021,49(12):34-39,70. [9]HUO Y L,LI Y L.A plant leaf classification method based on multi feature fusion and extreme learning machine[J].Computer Engineering and Science,2021,43(3):486-493. [10]WANG H X,CHEN Y Q,SHEN J,et al.Novel semi-supervised extreme learning machine and its application in anti-vibration hammer corrosion detection[J].Computer Science,2020,47(12):262-266. [11]LIU W,YAN S,CHEN T,et al.Feature recognition of irregular pellet images by regularized extreme learning machine in combination with fractal theory[J].Future Generation Computer Systems,2022,127:92-108. [12]LI L L,SUN J,TSENG M L,et al.Extreme learning machine optimized by whale optimization algorithm using insulated gate bipolar transistor module aging degree evaluation[J].Expert Systems with Applications,2019,127:58-67. [13]SUBUDHI U,DASH S.Detection and classification of powerquality disturbances using GWO ELM[J].Journal of Industrial Information Integration,2021,22:100204. [14]MUDULI D,DASH R,MAJHI B.Automated breast cancer detection in digital mammograms:A moth flame optimization based ELM approach[J].Biomedical Signal Processing and Control,2020,59:101912. [15]MIRJALILI S,GANDOMI A H,MIRJALILI S Z,et al.SalpSwarm Algorithm:A bio-inspired optimizer for engineering design problems[J].Advances in Engineering Software,2017,114:163-191. [16]LIU J S,YUAN M M,ZUO F.Global search-oriented adaptive leader salp swarm algorithm[J].Control and Decision,2021,36(9):2152-2160. [17]YU J S,WU L.Two types of leaders salp swarm algorithm[J].Computer Science,2021,48(4):254-260. [18]KAMEL S,EBEED M,JURADO F,et al.An improved version of salp swarm algorithm for solving optimal power flow problem[J].Soft Computing,2021,25(5):4027-4052. [19]HEGAZY A E,MAKHLOUF M A,El-Tawel G S.Feature selection using chaotic salp swarm algorithm for data classification[J].Arabian Journal for Science and Engineering,2019,44(4):3801-3816. [20]BALAKRISHNAN K,DHANALAKSHMI R,KHAIRE U M.Improved salp swarm algorithm based on the levy flight for feature selection[J].The Journal of Supercomputing,2021,77(11):12399-12419. [21]CHAABANE S B,BELAZI A,KHARBECH S,et al.Improved salp swarm optimization algorithm:application in feature weighting for blind modulation identification[J].Electronics,2021,10(16):2002. [22]LI Y C,HAN M X,GUO Q L.Modified whale optimization algorithm based on tent chaotic mapping and its application in structural optimization[J].KSCE Journal of Civil Engineering,2020,24(12):3703-3713. [23]WANG M N,WANG Q P,WANG X F.Improved grey wolf optimization algorithm based on iterative mapping and simplex method[J].Journal of Computer Applications,2018,38(A2):16-20,54. [24]XIA P F,NI Z W,ZHU X H.Attribute selection method based on fireworks evolution artificial fish swarm algorithm and multi-fractal dimension with its application in air quality prediction[J].Journal of Systems Science and Mathematical Sciences,2020,40(7):1157-1177. [25]PENG P,NI W,ZHU X H,et al.Attribute reduction methodbased on improved binary glowworm swarm optimization algorithm and neighborhood rough set[J].Pattern Recognition and Artificial Intelligence,2020,33(2):95-105. [26]DONG H B,PANG J W,HAN Q L.Gray extreme learning machine prediction method[J].Computer Science,2015,42(5):78-81,105. [27]BARATA J C A,HUSSEIN M S.The Moore-Penrose pseudoinverse:A tutorial review of the theory[J].Brazilian Journal of Physics,2012,42(1):146-165. [28]WANG J.Research on the linkage relationship between coal-coke-iron prices[J].Coal Economic Research,2008,2:13-16. [29]LAURITZEN S L.The EM algorithm for graphical association models with missing data[J].Computational Statistics & Data Analysis,1995,19(2):191-201. [30]AZLI H,TITRI S,LARBES C.MPPT-Based improved salpswarm algorithm for improving performance and efficiency of photovoltaic system under partial shading condition[C]//ICAIRES 2020:Artificial Intelligence and Renewables Towards an Energy Transition.2020:478-486. [31]MIRJALILI S,LEWIS A.The whale optimization algorithm[J].Advances in Engineering Software,2016,95:51-67. [32]MIRJALILI S,MIRJALILI S M,LEWIS A.Grey wolf optimizer[J].Advances in Engineering Software,2014,69:46-61. [33]SHEHAB M,ABUALIGAH L,AL HAMAD H,et al.Moth-flame optimization algorithm:variants and applications[J].Neural Computing and Applications,2020,32(14):9859-9884. [34]XIA P F,NI Z W,ZHU X H,et al.Selective ensemble approach based on reverse binary glowworm swarm optimization and diversity measure[J].Journal of Systems Science and Mathematical Sciences,2021,41(3):730-746. |
|