基于双精英进化樽海鞘群算法优化ELM的焦炭价格预测

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

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。

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.