群智能启发式算法求解折扣{0-1}背包问题(D{0-1}KP)时,为提升求解效率和求解质量,需采用某种修复与优化策略将非正常编码个体转换为符合解约束条件的编码个体。在引入项集价值密度概念基础上,以粒子群算法(PSO)为例,提出一组基于项集的...群智能启发式算法求解折扣{0-1}背包问题(D{0-1}KP)时,为提升求解效率和求解质量,需采用某种修复与优化策略将非正常编码个体转换为符合解约束条件的编码个体。在引入项集价值密度概念基础上,以粒子群算法(PSO)为例,提出一组基于项集的贪婪修复与优化方法(group greedy repair and optimization algorithm,GGROA),并进一步构造PSO-GGRDKP算法(PSO based GGROA for solving D{0-1}KP)以探究GGROA方法的可行性和性能。PSO-NGROADKP(PSO based NGROA for solving D{0-1}KP)和PSO-GRDKP(PSO based GROA for solving D{0-1}KP)是基于项贪心修复与优化方法的粒子群算法。在D{0-1}KP标准数据集的实验结果表明:与PSO-NGROADKP和PSO-GRDKP相比,PSO-GGRDKP算法的解误差率略高,但算法时间性能分别提升了13.8%、12.9%。展开更多
A real-time,long-term surface meteorological blended forcing dataset(SMBFD)has been developed based on station observations,satellite retrievals,and reanalysis products in China.The observations are collected at natio...A real-time,long-term surface meteorological blended forcing dataset(SMBFD)has been developed based on station observations,satellite retrievals,and reanalysis products in China.The observations are collected at national and regional automatic weather stations,satellite data are obtained from the Fengyun(FY)series satellites retrievals,and the reanalysis products are obtained from the ECMWF.The 90-m resolution digital terrain elevation data in China are obtained from the Shuttle Radar Topographic Mission(SRTM)for temperature and humidity elevation adjustment.The dataset includes 2-m air temperature and humidity,10-m zonal and meridional winds,downward shortwave radiation,surface pressure,and precipitation.The spatial resolution is 1 km,and the temporal resolution is 1 h.During the data processing procedure,various data fusion techniques including the space–time multiscale variational analysis,the discrete ordinates radiative transfer(DISORT)model,the hybrid radiation estimation model,and a terrain correction algorithm are employed.Dependent and independent evaluations of the dataset are performed against observations.The SMBFD dataset is also compared with similar datasets produced in other major meteorological operational centers in the world.The results are as follows.(1)All variables show reasonable geographic distribution features and realistic spatial and temporal variations.(2)Dependent and independent evaluations both indicate that the gridded SMBFD dataset is close to the observations,while the dependent evaluation yields better results than the independent evaluation.(3)Compared with similar datasets produced in other meteorological operational centers,the real-time and retrospective surface meteorological fusion data obviously have higher quality.The dataset introduced in the present study is in general stable and accurate,and can be applied in various practice such as meteorology,agriculture,ecology,environmental protection,etc.Meanwhile,this dataset has been used as the atmospheric forcing data to drive the operational High-resolution Land Data Assimilation System of China Meteorological Administration.The dataset with the network Common Data Form(NETCDF)can be decoded by various programming languages,and it is freely available to non-commercial users.展开更多
文摘群智能启发式算法求解折扣{0-1}背包问题(D{0-1}KP)时,为提升求解效率和求解质量,需采用某种修复与优化策略将非正常编码个体转换为符合解约束条件的编码个体。在引入项集价值密度概念基础上,以粒子群算法(PSO)为例,提出一组基于项集的贪婪修复与优化方法(group greedy repair and optimization algorithm,GGROA),并进一步构造PSO-GGRDKP算法(PSO based GGROA for solving D{0-1}KP)以探究GGROA方法的可行性和性能。PSO-NGROADKP(PSO based NGROA for solving D{0-1}KP)和PSO-GRDKP(PSO based GROA for solving D{0-1}KP)是基于项贪心修复与优化方法的粒子群算法。在D{0-1}KP标准数据集的实验结果表明:与PSO-NGROADKP和PSO-GRDKP相比,PSO-GGRDKP算法的解误差率略高,但算法时间性能分别提升了13.8%、12.9%。
基金Supported by the National Key Research and Development Program of China(2018YFC1506601)National Natural Science Foundation of China(91437220)China Meteorological Administration Special Public Welfare Research Fund(GYHY201306045 and GYHY201506002).
文摘A real-time,long-term surface meteorological blended forcing dataset(SMBFD)has been developed based on station observations,satellite retrievals,and reanalysis products in China.The observations are collected at national and regional automatic weather stations,satellite data are obtained from the Fengyun(FY)series satellites retrievals,and the reanalysis products are obtained from the ECMWF.The 90-m resolution digital terrain elevation data in China are obtained from the Shuttle Radar Topographic Mission(SRTM)for temperature and humidity elevation adjustment.The dataset includes 2-m air temperature and humidity,10-m zonal and meridional winds,downward shortwave radiation,surface pressure,and precipitation.The spatial resolution is 1 km,and the temporal resolution is 1 h.During the data processing procedure,various data fusion techniques including the space–time multiscale variational analysis,the discrete ordinates radiative transfer(DISORT)model,the hybrid radiation estimation model,and a terrain correction algorithm are employed.Dependent and independent evaluations of the dataset are performed against observations.The SMBFD dataset is also compared with similar datasets produced in other major meteorological operational centers in the world.The results are as follows.(1)All variables show reasonable geographic distribution features and realistic spatial and temporal variations.(2)Dependent and independent evaluations both indicate that the gridded SMBFD dataset is close to the observations,while the dependent evaluation yields better results than the independent evaluation.(3)Compared with similar datasets produced in other meteorological operational centers,the real-time and retrospective surface meteorological fusion data obviously have higher quality.The dataset introduced in the present study is in general stable and accurate,and can be applied in various practice such as meteorology,agriculture,ecology,environmental protection,etc.Meanwhile,this dataset has been used as the atmospheric forcing data to drive the operational High-resolution Land Data Assimilation System of China Meteorological Administration.The dataset with the network Common Data Form(NETCDF)can be decoded by various programming languages,and it is freely available to non-commercial users.
