Computer Science ›› 2023, Vol. 50 ›› Issue (5): 292-301.doi: 10.11896/jsjkx.220300259

• Artificial Intelligence • Previous Articles     Next Articles

Coke Price Prediction Based on ELM Optimized by Double-elite Evolution Salp Swarm Algorithm

ZHU Xuhui1,2, SHE Xiaomin1,2, NI Zhiwei1,2, XIA Pingfan1,2, ZHANG Chen3   

  1. 1 School of Management,Hefei University of Technology,Hefei 230009,China
    2 Key Laboratory of Process Optimization and Intelligent Decision-making,Ministry of Education,Hefei 230009,China
    3 School of Artificial Intelligence and Big Data,Hefei University,Hefei 230092,China
  • Received:2022-03-28 Revised:2022-09-18 Online:2023-05-15 Published:2023-05-06
  • About author:ZHU Xuhui,born in 1991,Ph.D,lectu-rer,master supervisor,is a member of China Computer Federation.His main research interests include intelligent computing,ensemble learning,deep learning and smart manufacturing.
    NI Zhiwei,born in 1963,Ph.D,professor,Ph.D supervisor.His main research interests include artificial intelligence,machine learning and edge computing.
  • Supported by:
    National Natural Science Foundation of China(91546108,71521001),Natural Science Foundation of Anhui Pro-vince,China(1908085QG298,1908085MG232),Fundamental Research Funds for the Central Universities(JZ2019HGTA0053,JZ2019HGBZ0128),Anhui Provincial Science and Technology Major Projects(201903a05020020) and Open Research Fund Program of Key Laboratory of Process Optimization and Intelligent Decision-making(Hefei University of Technology),Ministry of Education.

Abstract: Coke is one of important industrial raw materials,and accurate prediction of its future price trend has great significance for making production scheduling plans of coking plants.Extreme learning machine(ELM) has strong generalization ability and fast computing speed,and it is suitable as the model of coke price prediction.However,the prediction performance of ELM is greatly affected by its key parameters,and its parameters need to be optimized.Based on this,a coke price prediction method is proposed by optimizing the key parameters of ELM using double-elite evolution salp swarm algorithm.Firstly,the double-elite evolutionary salp swarm algorithm(MDSSA) is proposed by introducing logistic chaotic mapping,improved convergence factor,adaptive inertia weights and double-elite evolutionary mechanism,so as to enhance the search capability of salp swarm algorithm(MDSSA).Secondly,the connection weights and thresholds of ELM are optimized using MDSSA for finding the optimal parameters combination,so as to construct the MDSSA-ELM coke price prediction model.Finally,the convergence performance of MDSSA is validated using 8 benchmark functions,and the prediction ability of MDSSA-ELM model is tested on the actual coke price dataset.Experimental results demonstrate that MDSSA-ELM has stronger predictive capability than other methods,and MDSSA has superior searching ability than other algorithms,which provides an effective prediction tool for coking plants for achieving intelligent production scheduling.

Key words: Salp swarm algorithm, Extreme learning machine, Double-elite evolution, Coke price prediction

CLC Number: 

  • TP181
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