采用改进模拟植物生长算法的世界石油船队总运力预测
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摘要
为准确预测世界石油船队总运力情况,收集近15年来世界石油船队总运力的统计数据,分别从总运力趋势波动和运力净增量波动两个方面进行分析。建立时间序列模型来揭示世界石油船队总运力的变化规律,用改进的模拟植物生长算法(plant growth simulation algorithm,PGSA)进行求解。与遗传算法进行对比,改进算法的程序运行时间、均方根误差和平均绝对百分比误差均较低,算法预测的结果与历史数据的拟合度达92. 32%,预测结果具有较高的准确性。分析思路和方法可为航运企业科学决策提供技术支撑。
In order to accurately predict the total transport capacity of world crude oil fleets,the data of total transport capacity of world crude oil fleets over the past 15 years are collected,and the data are analyzed from two aspects: the fluctuation of the total transport capacity trend and the fluctuation of the net increment of transport capacity. The time series model is established to reveal the change rule of the total transport capacity of world crude oil fleets,and the improved plant growth simulation algorithm( PGSA)is used to solve the model. Compared with the genetic algorithm,the program running time,the root mean square error and the mean absolute percent error of the improved PGSA are lower. The fitting degree between the predicted data obtained by the improved PGSA and the historical data is 92. 32%. The prediction results are of high accuracy. The analysis idea and method can provide technical support for scientific decision-making of shipping enterprises.
In order to accurately predict the total transport capacity of world crude oil fleets,the data of total transport capacity of world crude oil fleets over the past 15 years are collected,and the data are analyzed from two aspects: the fluctuation of the total transport capacity trend and the fluctuation of the net increment of transport capacity. The time series model is established to reveal the change rule of the total transport capacity of world crude oil fleets,and the improved plant growth simulation algorithm( PGSA)is used to solve the model. Compared with the genetic algorithm,the program running time,the root mean square error and the mean absolute percent error of the improved PGSA are lower. The fitting degree between the predicted data obtained by the improved PGSA and the historical data is 92. 32%. The prediction results are of high accuracy. The analysis idea and method can provide technical support for scientific decision-making of shipping enterprises.
引文
[1]王顺娟.世界干散货航运市场运量及运力分析[J].综合运输,2009(5):65-68.
[2]刘海青.长江干线液货危险品船舶运力预测与调控建议[J].中国水运,2012(8):28-29. DOI:10. 13646/j. cnki. 42-1395/u. 2012.08. 012.
[3]陈言诚,林国龙,胡志华.基于系统动力学的干散货航运市场运力供给研究[J].武汉理工大学学报(交通科学与工程版),2012,36(4):799-803. DOI:10. 3963/j. issn2095-3844. 2012. 04. 034.
[4]李志,邵哲平,邹开其,等.利用模糊神经推理系统ANFIS预测世界散货船队运力[C]//中国系统工程学会.模糊集理论与模糊应用专辑:中国系统工程学会模糊数学与模糊系统委员会第十届年会论文选集.中国系统工程学会,2000.
[5]刘妍.国际油船运输市场发展研究[J].中国水运,2007,5(6):6-7.
[6]尹栋.国际油船运输市场的周期波动及预测[D].大连:大连海事大学,2013.
[7]邹葵. 2012年国际油船运输市场回顾与2013年展望[J].世界海运,2013,36(2):5-10.
[8]邹葵,孙琦.国际油船运输市场2015年回顾与2016年展望[J].世界海运,2016,39(3):8-14.
[9] LAULAJAINEN R. Operative strategy in tanker(dirty)shipping[J]. Maritime Policy and Management,2008,35(3):313-339. DOI:10.1080/03088830802080522.
[10]李彤,王春峰,王文波,等.求解整数规划的一种仿生类全局优化算法:模拟植物生长算法[J].系统工程理论与实践,2005,25(1):76-85.
[11]李彤,陈畴镛.求解非线性二层规划问题的模拟植物生长算法[J].中国管理科学,2012,20(4):160-166.
[12]王婷婷,杨琴.基于模拟植物生长算法的超市收银口优化调度[J].计算机应用,2014,34(5):1516-1520. DOI:10. 11772/j. issn. 1001-9081. 2014. 05. 1516.
[13] RAO R S,NARASIMHAM S V L,RAMALINGARAJU M. Optimal capacitor placement in a radial distribution system using plant growth simulation algorithm[J]. Electrical Power&Energy Systems,2011,33(5):1133-1139. DOI:10. 1016/j. ijepes. 2010. 11. 021.
[14] TANG Chengpei,LIU Ruiqi,NI Jiangqun. A novel wireless sensor network localization approach:localization based on plant growth simulation algorithm[J]. Elektronika Ir Elektrotechnika,2013,19(8):97-100. DOI:10. 5755/j01. eee. 19. 8. 5326.
[15]周飞,吕一清,石琳娜.改进粒子群算法优化灰色神经网络预测模型及其应用[J].统计与决策,2017(11):66-70. DOI:10. 13546/j.cnki. tjyjc. 2017. 11. 017.
[2]刘海青.长江干线液货危险品船舶运力预测与调控建议[J].中国水运,2012(8):28-29. DOI:10. 13646/j. cnki. 42-1395/u. 2012.08. 012.
[3]陈言诚,林国龙,胡志华.基于系统动力学的干散货航运市场运力供给研究[J].武汉理工大学学报(交通科学与工程版),2012,36(4):799-803. DOI:10. 3963/j. issn2095-3844. 2012. 04. 034.
[4]李志,邵哲平,邹开其,等.利用模糊神经推理系统ANFIS预测世界散货船队运力[C]//中国系统工程学会.模糊集理论与模糊应用专辑:中国系统工程学会模糊数学与模糊系统委员会第十届年会论文选集.中国系统工程学会,2000.
[5]刘妍.国际油船运输市场发展研究[J].中国水运,2007,5(6):6-7.
[6]尹栋.国际油船运输市场的周期波动及预测[D].大连:大连海事大学,2013.
[7]邹葵. 2012年国际油船运输市场回顾与2013年展望[J].世界海运,2013,36(2):5-10.
[8]邹葵,孙琦.国际油船运输市场2015年回顾与2016年展望[J].世界海运,2016,39(3):8-14.
[9] LAULAJAINEN R. Operative strategy in tanker(dirty)shipping[J]. Maritime Policy and Management,2008,35(3):313-339. DOI:10.1080/03088830802080522.
[10]李彤,王春峰,王文波,等.求解整数规划的一种仿生类全局优化算法:模拟植物生长算法[J].系统工程理论与实践,2005,25(1):76-85.
[11]李彤,陈畴镛.求解非线性二层规划问题的模拟植物生长算法[J].中国管理科学,2012,20(4):160-166.
[12]王婷婷,杨琴.基于模拟植物生长算法的超市收银口优化调度[J].计算机应用,2014,34(5):1516-1520. DOI:10. 11772/j. issn. 1001-9081. 2014. 05. 1516.
[13] RAO R S,NARASIMHAM S V L,RAMALINGARAJU M. Optimal capacitor placement in a radial distribution system using plant growth simulation algorithm[J]. Electrical Power&Energy Systems,2011,33(5):1133-1139. DOI:10. 1016/j. ijepes. 2010. 11. 021.
[14] TANG Chengpei,LIU Ruiqi,NI Jiangqun. A novel wireless sensor network localization approach:localization based on plant growth simulation algorithm[J]. Elektronika Ir Elektrotechnika,2013,19(8):97-100. DOI:10. 5755/j01. eee. 19. 8. 5326.
[15]周飞,吕一清,石琳娜.改进粒子群算法优化灰色神经网络预测模型及其应用[J].统计与决策,2017(11):66-70. DOI:10. 13546/j.cnki. tjyjc. 2017. 11. 017.