[1]杨 飞,王洋.基于改进自然选择粒子群算法的铁路客运量组合预测研究[J].高速铁路技术,2022,13(06):63-68,73.[doi:10.12098/j.issn.1674-8247.2022.06.012
]
YANG Fei,WANG Yang.Study on Combination Forecast of Railway Passenger Volume by Improved Particle Swarm Optimization Algorithm Based on Natural Selection[J].HIGH SPEED RAILWAY TECHNOLOGY,2022,13(06):63-68,73.[doi:10.12098/j.issn.1674-8247.2022.06.012
]
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基于改进自然选择粒子群算法的铁路客运量组合预测研究()
《高速铁路技术》[ISSN:1674-8247/CN:51-1730/U]
- 卷:
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13卷
- 期数:
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2022年06期
- 页码:
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63-68,73
- 栏目:
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研究创新
- 出版日期:
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2022-12-20
文章信息/Info
- Title:
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Study on Combination Forecast of Railway Passenger Volume by Improved Particle Swarm Optimization Algorithm Based on Natural Selection
- 文章编号:
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1674—8247 (2022) 06—0063—06
- 作者:
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杨 飞; 王洋
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中铁工程设计咨询集团有限公司, 北京100055
- Author(s):
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YANG Fei; WANG Yang
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China Railway Engineering Design Consulting Group Co. ,Ltd. ,Beijing 100055 ,China
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- 关键词:
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自然选择理论; 粒子群算法; 铁路客运量; 组合预测; 权重
- Keywords:
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natural selection theory; particle swarm optimization algorithm; railway passenger volume; combination forecast; weight
- 分类号:
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U293.13
- DOI:
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10.12098/j.issn.1674-8247.2022.06.012
- 文献标志码:
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A
- 摘要:
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为提高铁路客运量的预测精度,引入基于自然选择的粒子群算法以解决铁路客运量组合预测模型权重分配问题,借助对数函数和正弦函数非线性变化的特点对基于自然选择粒子群算法的权重和学习因子进行改进,并结合BP神经网络和ARIMA模型对北京市铁路客运量进行组合预测。研究结果表明:(1)改进基于自然选择的粒子群算法在权重分配过程中展现出更好的寻优能力和收敛速度;(2)相比于BP神经网络和ARIMA模型和等分权重法赋值的组合预测模型,改进基于自然选择的粒子群算法求解权重的组合预测精度更高,预测年度平均预测相对误差分别提高了1.934%、5.009%和1.118%。
- Abstract:
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In order to improve the forecast accuracy of railway passenger volume,the particle swarm optimization (PSO)algorithm based on natural selection is introduced to solve the weight allocation problem of combination forecast model for railway passenger volume. The author carried out the combination forecast of railway passenger volume in Beijing by improving the weight and learning factors of PSO algorithm based on natural selection according to the characteristics of nonlinear variation of logarithmic function and sinusoidal function,and in combination with the BP Neural Network and ARIMA model. The results show that:(1)The improved PSO algorithm based on natural selection shows better optimization ability and convergence speed in the process of weight allocation.(2)Compared with the "BP Neural Network + ARIMA model + value assignment by equal weight method" combination forecast model,the combination forecast by solving the weight by the improved PSO algorithm based on natural selection has a higher accuracy. The average relative forecast errors in three forecast years are increased by 1.934%,5.009% and 1.118%respectively.
参考文献/References:
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备注/Memo
- 备注/Memo:
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收稿日期:2021-10-12
作者简介:杨飞(1995-),男,助理工程师。
更新日期/Last Update:
2022-12-20