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。
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