Storm identification and tracking based on weather radar data are essential to nowcasting and severe weather warning.A new two-dimensional storm identification method simultaneously seeking in two directions is propos...Storm identification and tracking based on weather radar data are essential to nowcasting and severe weather warning.A new two-dimensional storm identification method simultaneously seeking in two directions is proposed,and identification results are used to discuss storm tracking algorithms.Three modern optimization algorithms (simulated annealing algorithm,genetic algorithm and ant colony algorithm) are tested to match storms in successive time intervals.Preliminary results indicate that the simulated annealing algorithm and ant colony algorithm are effective and have intuitionally adjustable parameters,whereas the genetic algorithm is unsatisfactorily constrained by the mode of genetic operations.Experiments provide not only the feasibility and characteristics of storm tracking with modern optimization algorithms,but also references for studies and applications in relevant fields.展开更多
A genetic algorithm was used to optimize the parameters of the two-dimensional Storm Surge/Tide Operational Model （STORM） to improve sea level predictions.The genetic algorithm was applied to nine typhoons that affe...A genetic algorithm was used to optimize the parameters of the two-dimensional Storm Surge/Tide Operational Model （STORM） to improve sea level predictions.The genetic algorithm was applied to nine typhoons that affected the Korean Peninsula during 2005-2007.The following model parameters were used：the bottom drag coefficient,the background horizontal diffusivity,Smagorinski＇s horizontal viscosity,and the sea level pressure scaling.Generally,the simulation results using the optimized,mean,and median parameter values improved sea level predictions.The four estimated parameters improved the sea level prediction by 76% and 54% in the bias and root mean square error for Typhoon Kalmaegi （0807） in 2008,respectively.One-month simulations of February and August 2008 were also improved using the estimated parameters.This study demonstrates that parameter optimization on STORM can improve sea level prediction.展开更多
In the field of data mining and machine learning,clustering is a typical issue which has been widely studied by many researchers,and lots of effective algorithms have been proposed,including K-means,fuzzy c-means(FCM)...In the field of data mining and machine learning,clustering is a typical issue which has been widely studied by many researchers,and lots of effective algorithms have been proposed,including K-means,fuzzy c-means(FCM)and DBSCAN.However,the traditional clustering methods are easily trapped into local optimum.Thus,many evolutionary-based clustering methods have been investigated.Considering the effectiveness of brain storm optimization(BSO)in increasing the diversity while the diversity optimization is performed,in this paper,we propose a new clustering model based on BSO to use the global ability of BSO.In our experiment,we apply the novel binary model to solve the problem.During the period of processing data,BSO was mainly utilized for iteration.Also,in the process of K-means,we set the more appropriate parameters selected to match it greatly.Four datasets were used in our experiment.In our model,BSO was first introduced in solving the clustering problem.With the algorithm running on each dataset repeatedly,our experimental results have obtained good convergence and diversity.In addition,by comparing the results with other clustering models,the BSO clustering model also guarantees high accuracy.Therefore,from many aspects,the simulation results show that the model of this paper has good performance.展开更多
As a new intelligent optimization method,brain storm optimization(BSO)algorithm has been widely concerned for its advantages in solving classical optimization problems.Recently,an evolutionary classification optimizat...As a new intelligent optimization method,brain storm optimization(BSO)algorithm has been widely concerned for its advantages in solving classical optimization problems.Recently,an evolutionary classification optimization model based on BSO algorithm has been proposed,which proves its effectiveness in solving the classification problem.However,BSO algorithm also has defects.For example,large-scale datasets make the structure of the model complex,which affects its classification performance.In addition,in the process of optimization,the information of the dominant solution cannot be well preserved in BSO,which leads to its limitations in classification performance.Moreover,its generation strategy is inefficient in solving a variety of complex practical problems.Therefore,we briefly introduce the optimization model structure by feature selection.Besides,this paper retains the brainstorming process of BSO algorithm,and embeds the new generation strategy into BSO algorithm.Through the three generation methods of global optimal,local optimal and nearest neighbor,we can better retain the information of the dominant solution and improve the search efficiency.To verify the performance of the proposed generation strategy in solving the classification problem,twelve datasets are used in experiment.Experimental results show that the new generation strategy can improve the performance of BSO algorithm in solving classification problems.展开更多
Storm作为开源的分布式实时计算框架在处理流式数据方面具有明显的优势,但其默认调度算法没有将节点资源与任务实际相结合,仍存在节点资源利用率不高等问题,在负载均衡方面存在较大提升空间。为此,提出了一种基于布谷鸟搜索算法的Storm...Storm作为开源的分布式实时计算框架在处理流式数据方面具有明显的优势,但其默认调度算法没有将节点资源与任务实际相结合,仍存在节点资源利用率不高等问题,在负载均衡方面存在较大提升空间。为此,提出了一种基于布谷鸟搜索算法的Storm集群动态负载均衡策略(dynamic load balancing strategy for storm cluster based on cuckoo search algorithm,DLBSCSA)。该策略为达到集群节点负载的动态均衡,将任务调度模拟为布谷鸟寻窝产卵的过程,综合分析集群的CPU、网络带宽、内存等资源的实时利用情况,通过布谷鸟搜索算法的寻优过程自适应地确定节点性能权重,并根据权重动态分配任务。实验结果表明,该算法可以实现资源的合理分配,达到集群动态的负载均衡,从而减小集群响应时间,与默认算法相比具有更高的集群吞吐量和更小的系统延迟。展开更多
基金National Natural Science Foundation of China (60674074)Natural Science Foundation of Jiangsu province (BK2009415)+5 种基金Research Fund for the Doctoral Program of Higher Education of China (20093228110002)College Graduate Student Research and Innovation Program of Jiangsu province (CX09B_227Z)Meteorology Industry Special Project of CMA (GYHY(QX)2007-6-2)National 863 Project (2007AA061901)Project of State Key Laboratory of Severe Weather of Chinese Academy of Meteorological Sciences (2008LASW-B11)Project 2009Y0006
文摘Storm identification and tracking based on weather radar data are essential to nowcasting and severe weather warning.A new two-dimensional storm identification method simultaneously seeking in two directions is proposed,and identification results are used to discuss storm tracking algorithms.Three modern optimization algorithms (simulated annealing algorithm,genetic algorithm and ant colony algorithm) are tested to match storms in successive time intervals.Preliminary results indicate that the simulated annealing algorithm and ant colony algorithm are effective and have intuitionally adjustable parameters,whereas the genetic algorithm is unsatisfactorily constrained by the mode of genetic operations.Experiments provide not only the feasibility and characteristics of storm tracking with modern optimization algorithms,but also references for studies and applications in relevant fields.
基金supported by the National Institute of Meteorological Research of the Korea Meteorological Administration
文摘A genetic algorithm was used to optimize the parameters of the two-dimensional Storm Surge/Tide Operational Model （STORM） to improve sea level predictions.The genetic algorithm was applied to nine typhoons that affected the Korean Peninsula during 2005-2007.The following model parameters were used：the bottom drag coefficient,the background horizontal diffusivity,Smagorinski＇s horizontal viscosity,and the sea level pressure scaling.Generally,the simulation results using the optimized,mean,and median parameter values improved sea level predictions.The four estimated parameters improved the sea level prediction by 76% and 54% in the bias and root mean square error for Typhoon Kalmaegi （0807） in 2008,respectively.One-month simulations of February and August 2008 were also improved using the estimated parameters.This study demonstrates that parameter optimization on STORM can improve sea level prediction.
基金supported by Natural Science Foundation of Jiangsu Province(Grant No.BK20141005)by Natural Science Foundation of the Jiangsu Higher Education Institutions of China(Grant No.14KJB520025).
文摘In the field of data mining and machine learning,clustering is a typical issue which has been widely studied by many researchers,and lots of effective algorithms have been proposed,including K-means,fuzzy c-means(FCM)and DBSCAN.However,the traditional clustering methods are easily trapped into local optimum.Thus,many evolutionary-based clustering methods have been investigated.Considering the effectiveness of brain storm optimization(BSO)in increasing the diversity while the diversity optimization is performed,in this paper,we propose a new clustering model based on BSO to use the global ability of BSO.In our experiment,we apply the novel binary model to solve the problem.During the period of processing data,BSO was mainly utilized for iteration.Also,in the process of K-means,we set the more appropriate parameters selected to match it greatly.Four datasets were used in our experiment.In our model,BSO was first introduced in solving the clustering problem.With the algorithm running on each dataset repeatedly,our experimental results have obtained good convergence and diversity.In addition,by comparing the results with other clustering models,the BSO clustering model also guarantees high accuracy.Therefore,from many aspects,the simulation results show that the model of this paper has good performance.
基金supported by the National Natural Science Foundation of China(61876089,61403206,61876185,61902281)the opening Project of Jiangsu Key Laboratory of Data Science and Smart Software(No.2019DS302)+2 种基金the Natural Science Foundation of Jiangsu Province(BK20141005)the Natural Science Foundation of the Jiangsu Higher Education Institutions of China(14KJB520025)the Engineering Research Center of Digital Forensics,Ministry of Education,and the Priority Academic Program Development of Jiangsu Higher Education Institutions.
文摘As a new intelligent optimization method,brain storm optimization(BSO)algorithm has been widely concerned for its advantages in solving classical optimization problems.Recently,an evolutionary classification optimization model based on BSO algorithm has been proposed,which proves its effectiveness in solving the classification problem.However,BSO algorithm also has defects.For example,large-scale datasets make the structure of the model complex,which affects its classification performance.In addition,in the process of optimization,the information of the dominant solution cannot be well preserved in BSO,which leads to its limitations in classification performance.Moreover,its generation strategy is inefficient in solving a variety of complex practical problems.Therefore,we briefly introduce the optimization model structure by feature selection.Besides,this paper retains the brainstorming process of BSO algorithm,and embeds the new generation strategy into BSO algorithm.Through the three generation methods of global optimal,local optimal and nearest neighbor,we can better retain the information of the dominant solution and improve the search efficiency.To verify the performance of the proposed generation strategy in solving the classification problem,twelve datasets are used in experiment.Experimental results show that the new generation strategy can improve the performance of BSO algorithm in solving classification problems.
文摘Storm作为开源的分布式实时计算框架在处理流式数据方面具有明显的优势,但其默认调度算法没有将节点资源与任务实际相结合,仍存在节点资源利用率不高等问题,在负载均衡方面存在较大提升空间。为此,提出了一种基于布谷鸟搜索算法的Storm集群动态负载均衡策略(dynamic load balancing strategy for storm cluster based on cuckoo search algorithm,DLBSCSA)。该策略为达到集群节点负载的动态均衡,将任务调度模拟为布谷鸟寻窝产卵的过程,综合分析集群的CPU、网络带宽、内存等资源的实时利用情况,通过布谷鸟搜索算法的寻优过程自适应地确定节点性能权重,并根据权重动态分配任务。实验结果表明,该算法可以实现资源的合理分配,达到集群动态的负载均衡,从而减小集群响应时间,与默认算法相比具有更高的集群吞吐量和更小的系统延迟。
文摘在大规模图结构数据中发现最稠密子图具有极其广泛的应用,如社区发现、垃圾邮件检测和论文引用关系抽取等。基于带标签的无向图,提出了查询标签集的概念,设计了一个可以快速发现最稠密子图的近似算法DSFLC(Densest Subgraph Finding based on Labelset Constraint):用户提交自定义的查询标签集,算法便可保证在用户可以接受的时间内返回满足查询标签集约束的最稠密子图。对于任何参数ε(ε>0),DSFLC算法只需扫描大规模数据集O(log1+εn)次,同时可保证算法的近似因子是2(1+ε)。对DSFLC算法进行分析后,发现该算法在预处理阶段易于并行化,因此选择Twitter Storm平台,并行化地实现了DSFLC算法。最后对从DBLP数据库中抽取的合作关系图进行测试,一方面研究Storm平台对算法的加速程度;另一方面分析挖掘出的子图的稠密度与参数ε之间的关系,最终验证了DSFLC算法的实用性和可扩展性。
