In order to solve the problem of efficiently assigning tasks in an ad-hoc mobile cloud( AMC),a task assignment algorithm based on the heuristic algorithm is proposed. The proposed task assignment algorithm based on pa...In order to solve the problem of efficiently assigning tasks in an ad-hoc mobile cloud( AMC),a task assignment algorithm based on the heuristic algorithm is proposed. The proposed task assignment algorithm based on particle swarm optimization and simulated annealing( PSO-SA) transforms the dependencies between tasks into a directed acyclic graph( DAG) model. The number in each node represents the computation workload of each task and the number on each edge represents the workload produced by the transmission. In order to simulate the environment of task assignment in AMC,mathematical models are developed to describe the dependencies between tasks and the costs of each task are defined. PSO-SA is used to make the decision for task assignment and for minimizing the cost of all devices,which includes the energy consumption and time delay of all devices.PSO-SA also takes the advantage of both particle swarm optimization and simulated annealing by selecting an optimal solution with a certain probability to avoid falling into local optimal solution and to guarantee the convergence speed. The simulation results show that compared with other existing algorithms,the PSO-SA has a smaller cost and the result of PSO-SA can be very close to the optimal solution.展开更多
The stability of underground entry-type excavations will directly affect the working environment and the safety of staff.Empirical critical span graphs and traditional statistics learning methods can not meet the requ...The stability of underground entry-type excavations will directly affect the working environment and the safety of staff.Empirical critical span graphs and traditional statistics learning methods can not meet the requirements of high accuracy for stability assessment of entry-type excavations.Therefore,this study proposes a new prediction method based on machine learning to scientifically adjust the critical span graph.Accordingly,the particle swarm optimization(PSO)algorithm is used to optimize the core parameters of the gradient boosting decision tree(GBDT),abbreviated as PSO-GBDT.Moreover,the classification performance of eight other classifiers including GDBT,k-nearest neighbors(KNN),two kinds of support vector machines(SVM),Gaussian naive Bayes(GNB),logistic regression(LR)and linear discriminant analysis(LDA)are also applied to compare with the proposed model.Findings revealed that compared with the other eight models,the prediction performance of PSO-GBDT is undoubtedly the most reliable,and its classification accuracy is up to 0.93.Therefore,this model has great potential to provide a more scientific and accurate choice for the stability prediction of underground excavations.In addition,each classification model is used to predict the stability category of several grid points divided by the critical span graph,and the updated critical span graph of each model is discussed in combination with previous studies.The results show that the PSO-GBDT model has the advantages of being scientific,accurate and efficient in updating the critical span graph,and its output decision boundary has strict theoretical support,which can help mine operators make favorable economic decisions.展开更多
针对互联网中Web服务具有动态变化且迅速增长的特点,提出了一种面向用户需求的服务工作流构造模型.该模型将功能相同或相似的服务聚集成一类服务集合,每类服务集合采用生成树的方式组织,并依据工作流的业务逻辑关系形成业务生成图;同时...针对互联网中Web服务具有动态变化且迅速增长的特点,提出了一种面向用户需求的服务工作流构造模型.该模型将功能相同或相似的服务聚集成一类服务集合,每类服务集合采用生成树的方式组织,并依据工作流的业务逻辑关系形成业务生成图;同时,在重定义粒子群算法的位置、速度、加/减法和乘法的基础上,结合遗传算法中的交叉、变异操作,设计了基于混合粒子群的QoS(quality of service)调度方法,保证在可选服务不断增长时能够满足用户的个性化需求.实验结果表明,该模型能够有效地屏蔽组成工作流的Web服务物理上的变化与差异,较好地组合了Internet中的Web服务资源,适合于虚拟计算环境的应用要求.展开更多
基金The National Natural Science Foundation of China(No.61741102,61471164,61601122)the Fundamental Research Funds for the Central Universities(No.SJLX_160040)
文摘In order to solve the problem of efficiently assigning tasks in an ad-hoc mobile cloud( AMC),a task assignment algorithm based on the heuristic algorithm is proposed. The proposed task assignment algorithm based on particle swarm optimization and simulated annealing( PSO-SA) transforms the dependencies between tasks into a directed acyclic graph( DAG) model. The number in each node represents the computation workload of each task and the number on each edge represents the workload produced by the transmission. In order to simulate the environment of task assignment in AMC,mathematical models are developed to describe the dependencies between tasks and the costs of each task are defined. PSO-SA is used to make the decision for task assignment and for minimizing the cost of all devices,which includes the energy consumption and time delay of all devices.PSO-SA also takes the advantage of both particle swarm optimization and simulated annealing by selecting an optimal solution with a certain probability to avoid falling into local optimal solution and to guarantee the convergence speed. The simulation results show that compared with other existing algorithms,the PSO-SA has a smaller cost and the result of PSO-SA can be very close to the optimal solution.
基金the National Science Foundation of China(Grant No.42177164)the Distinguished Youth Science Foundation of Hunan Province of China(Grant No.2022JJ10073)the Innovation-Driven Project of Central South University(Grant No.2020CX040).
文摘The stability of underground entry-type excavations will directly affect the working environment and the safety of staff.Empirical critical span graphs and traditional statistics learning methods can not meet the requirements of high accuracy for stability assessment of entry-type excavations.Therefore,this study proposes a new prediction method based on machine learning to scientifically adjust the critical span graph.Accordingly,the particle swarm optimization(PSO)algorithm is used to optimize the core parameters of the gradient boosting decision tree(GBDT),abbreviated as PSO-GBDT.Moreover,the classification performance of eight other classifiers including GDBT,k-nearest neighbors(KNN),two kinds of support vector machines(SVM),Gaussian naive Bayes(GNB),logistic regression(LR)and linear discriminant analysis(LDA)are also applied to compare with the proposed model.Findings revealed that compared with the other eight models,the prediction performance of PSO-GBDT is undoubtedly the most reliable,and its classification accuracy is up to 0.93.Therefore,this model has great potential to provide a more scientific and accurate choice for the stability prediction of underground excavations.In addition,each classification model is used to predict the stability category of several grid points divided by the critical span graph,and the updated critical span graph of each model is discussed in combination with previous studies.The results show that the PSO-GBDT model has the advantages of being scientific,accurate and efficient in updating the critical span graph,and its output decision boundary has strict theoretical support,which can help mine operators make favorable economic decisions.
基金the National Natural Science Foundation of Chinaunder Grant No.60674016(国家自然科学基金)the National High-Tech Research and Development Plan of Chinaunder Grant No.2006AA04Z172(国家高技术研究发展计划(863))+1 种基金the National Science Fund for Distinguished Young Scholars of Chinaunder Grant No.60425310(国家杰出青年科学基金)the Natural Science Foundation of Hu’nan Province of Chinaunder Grant No.05JJ40118(湖南省自然科学基金)
文摘针对互联网中Web服务具有动态变化且迅速增长的特点,提出了一种面向用户需求的服务工作流构造模型.该模型将功能相同或相似的服务聚集成一类服务集合,每类服务集合采用生成树的方式组织,并依据工作流的业务逻辑关系形成业务生成图;同时,在重定义粒子群算法的位置、速度、加/减法和乘法的基础上,结合遗传算法中的交叉、变异操作,设计了基于混合粒子群的QoS(quality of service)调度方法,保证在可选服务不断增长时能够满足用户的个性化需求.实验结果表明,该模型能够有效地屏蔽组成工作流的Web服务物理上的变化与差异,较好地组合了Internet中的Web服务资源,适合于虚拟计算环境的应用要求.
