| 引用本文: | 甘如美江1,傅 杰2,3,汪 正4,江雨燕2,3,王付宇2,3.改进灰狼算法与机器学习混合模型的时间序列预测(J/M/D/N,J:杂志，M：书，D：论文，N：报纸).期刊名称,2026，43（3）：125-134 |
| CHEN X. Adap tive slidingmode contr ol for discrete2ti me multi2inputmulti2 out put systems[ J ]. Aut omatica, 2006, 42(6): 4272-435 |
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| 改进灰狼算法与机器学习混合模型的时间序列预测 |
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甘如美江1,傅 杰2,3,汪 正4,江雨燕2,3,王付宇2,3
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1. 安徽工业大学 电气与信息工程学院,安徽 马鞍山 243002
2. 安徽工业大学 管理科学与工程学院,安徽 马鞍山 243002
3. 复杂系统多学科管理与控制安徽省普通高校重点实验室,安徽 马鞍山 243002
4. 安徽工业大学 艺术与设计学院,安徽 马鞍山 243002
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| 摘要: |
| 目的 自人类文明诞生以来,与病毒的斗争便从未停止,因此传染病数据预测具有重要参考价值,提出一种
新算法以解决灰狼算法易陷入局部最优解和全局搜索能力不足的问题。 方法 通过 Halton 序列搜索算法初始化狼
群位置,避免灰狼算法陷入局部最优解和重复运算;引入 Levy 飞行和随机游动策略对灰狼算法的寻优过程进行优
化,以增加算法的全局搜索能力;利用改进的灰狼算法优化 6 种机器学习模型的参数并进行预测;使用带约束的优
化算法序贯最小二乘规划算法( SLSQP,Sequential Least Squares Programming) 和灰狼优化算法(GWO,Grey Wolf
Optimization Algorithm)得出各模型的最佳集成权重,采用 3 种评估函数对各模型以及混合模型的预测效果进行得
分评估。 结果 改进的灰狼算法性能良好,在迭代测试中优于其他对比算法;利用改进灰狼算法优化的机器学习预
测模型中,以优化梯度提升树模型预测效果最佳,相较于未进行优化模型的绝对平均值误差、均方根误差和拟合优
度,预测精度提升了 52%、52%和 1%。 结论 基于灰狼优化算法加权后的机器学习模型加权混合得分出色,预测精
度进一步得到提升,绝对平均值误差、均方根误差和拟合优度分别为 0. 07、0. 24 和 0. 99,对传染病感染人数的预测
具有重要参考价值。 |
| 关键词: 改进的灰狼算法 加权集成模型 Halton 序列 Levy 飞行 |
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| Time Series Prediction of a Hybrid Model Combining an Improved Grey Wolf Algorithm and Machine Learning |
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GAN Rumeijiang1 FU Jie2 3 WANG Zheng4 JIANG Yuyan2 3 WANG Fuyu2 3
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1. School of Electrical and Information Engineering Anhui University of Technology Ma?? anshan 243002 Anhui China
2. School of Management Science and Engineering Anhui University of Technology Ma?? anshan 243002 Anhui China
3. Key Laboratory of Multidisciplinary Management and Control of Complex Systems in Anhui Universities Ma?? anshan
243002 Anhui China
4. School of Art and Design Anhui University of Technology Ma?? anshan 243002 Anhui China
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| Abstract: |
| Objective Since the dawn of human civilization the battle against viruses has never ceased making the
prediction of infectious disease data of significant reference value. This paper proposes a novel algorithm to address the
limitations of the standard grey wolf algorithm which is prone to local optima and exhibits insufficient global search capability. Methods The wolf pack positions were initialized via the Halton sequence search algorithm to prevent the grey
wolf algorithm from becoming trapped in local optima and redundant computations. The Lévy flight and random walk
strategies were introduced to optimize the search process of the grey wolf algorithm thereby enhancing the algorithm?? s
global exploration capability. The improved grey wolf algorithm was then utilized to optimize the parameters of six machine
learning models for prediction. Constrained optimization techniques—specifically the sequential least squares
programming SLSQP algorithm and grey wolf optimization GWO algorithm—were employed to determine the optimal
ensemble weights for each model. Finally three evaluation functions were applied to score the predictive performance of
both individual models and the hybrid model. Results The improved GWO algorithm demonstrated superior performance
outperforming other benchmark algorithms in iterative tests. Among the machine learning models optimized by the
improved GWO the optimized gradient boosting tree model achieved the best prediction performance. Compared with its
unoptimized counterpart its accuracy improved by 52% 52% and 1% in terms of mean absolute error MAE root
mean square error RMSE and coefficient of determination R
2
respectively. Conclusion The weighted ensemble
model based on GWO-optimized machine learning models achieves outstanding scores yielding a further enhancement
in predictive accuracy. The model?? s MAE RMSE and R
2 were 0. 07 0. 24 and 0. 99 respectively. This approach
provides a valuable reference for forecasting the number of infectious disease cases. |
| Key words: improved grey wolf algorithm weighted integrated model Halton sequence Lévy flight |
