Title: Coke price prediction approach based on dense GRU and opposition-based learning salp swarm algorithm

Authors: Xuhui Zhu; Pingfan Xia; Qizhi He; Zhiwei Ni; Liping Ni

Addresses: School of Management, Hefei University of Technology, Hefei, Anhui, China ' School of Management, Hefei University of Technology, Hefei, Anhui, China ' School of Statistics and Mathematics, Zhejiang Gongshang University, Hangzhou, Zhejiang, China ' School of Management, Hefei University of Technology, Hefei, Anhui, China ' School of Management, Hefei University of Technology, Hefei, Anhui, China

Abstract: Coke price prediction is critical for smart coking plants to make sensible production plan. The prediction of coke price fluctuations is a time-series problem, and gated recurrent unit (GRU) performs well on dealing with it. Meanwhile, densely connected GRU can improve the information flow of time-series data, but its key parameters are sensitive. Therefore, a novel coke price prediction method, named DGOLSCPP, is proposed using dense GRU (DGRU) and opposition-based learning salp swarm algorithm (OLSSA). Firstly, a model with two layers stacked DGRU is constructed for capturing deeper features. Secondly, OLSSA is proposed by introducing opposition-based learning, following and stochastic walk operation for enhancing searching ability. Finally, OLSSA is employed to adjust the key parameters of DGRU for winning the accurate predictive results. Experimental results on two real-world coke price datasets from a certain smart coking plant suggest DGOLSCPP outperforms other competitive methods.

Keywords: salp swarm algorithm; gated recurrent unit; GRU; dense network; coke price prediction; opposition-based learning salp swarm algorithm; OLSSA.

DOI: 10.1504/IJBIC.2023.130549

International Journal of Bio-Inspired Computation, 2023 Vol.21 No.2, pp.106 - 121

Received: 02 Sep 2021
Accepted: 17 Mar 2022
Published online: 27 Apr 2023 *

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