The present study proposed an enhanced cuckoo search(ECS) algorithm combined with artificial neural network(ANN) as the surrogate model to solve structural reliability problems. In order to enhance the accuracy and co...The present study proposed an enhanced cuckoo search(ECS) algorithm combined with artificial neural network(ANN) as the surrogate model to solve structural reliability problems. In order to enhance the accuracy and convergence rate of the original cuckoo search(CS) algorithm, the main parameters namely, abandon probability of worst nests paand search step sizeα0 are dynamically adjusted via nonlinear control equations. In addition, a global-best guided equation incorporating the information of global best nest is introduced to the ECS to enhance its exploitation. Then, the proposed ECS is linked to the well-trained ANN model for structural reliability analysis. The computational capability of the proposed algorithm is validated using five typical structural reliability problems and an engineering application. The comparison results show the efficiency and accuracy of the proposed algorithm.展开更多
Well organized datacentres with interconnected servers constitute the cloud computing infrastructure.User requests are submitted through an interface to these servers that provide service to them in an on-demand basis...Well organized datacentres with interconnected servers constitute the cloud computing infrastructure.User requests are submitted through an interface to these servers that provide service to them in an on-demand basis.The scientific applications that get executed at cloud by making use of the heterogeneous resources being allocated to them in a dynamic manner are grouped under NP hard problem category.Task scheduling in cloud poses numerous challenges impacting the cloud performance.If not handled properly,user satisfaction becomes questionable.More recently researchers had come up with meta-heuristic type of solutions for enriching the task scheduling activity in the cloud environment.The prime aim of task scheduling is to utilize the resources available in an optimal manner and reduce the time span of task execution.An improvised seagull optimization algorithm which combines the features of the Cuckoo search(CS)and seagull optimization algorithm(SOA)had been proposed in this work to enhance the performance of the scheduling activity inside the cloud computing environment.The proposed algorithm aims to minimize the cost and time parameters that are spent during task scheduling in the heterogeneous cloud environment.Performance evaluation of the proposed algorithm had been performed using the Cloudsim 3.0 toolkit by comparing it with Multi objective-Ant Colony Optimization(MO-ACO),ACO and Min-Min algorithms.The proposed SOA-CS technique had produced an improvement of 1.06%,4.2%,and 2.4%for makespan and had reduced the overall cost to the extent of 1.74%,3.93%and 2.77%when compared with PSO,ACO,IDEA algorithms respectively when 300 vms are considered.The comparative simulation results obtained had shown that the proposed improvised seagull optimization algorithm fares better than other contemporaries.展开更多
Accurate photovoltaic(PV)power prediction can effectively help the power sector to make rational energy planning and dispatching decisions,promote PV consumption,make full use of renewable energy and alleviate energy ...Accurate photovoltaic(PV)power prediction can effectively help the power sector to make rational energy planning and dispatching decisions,promote PV consumption,make full use of renewable energy and alleviate energy problems.To address this research objective,this paper proposes a prediction model based on kernel principal component analysis(KPCA),modified cuckoo search algorithm(MCS)and deep convolutional neural networks(DCNN).Firstly,KPCA is utilized to reduce the dimension of the feature,which aims to reduce the redundant input vectors.Then using MCS to optimize the parameters of DCNN.Finally,the photovoltaic power forecasting method of KPCA-MCS-DCNN is established.In order to verify the prediction performance of the proposed model,this paper selects a photovoltaic power station in China for example analysis.The results show that the new hybrid KPCA-MCS-DCNN model has higher prediction accuracy and better robustness.展开更多
基金supported by the National Natural Science Foundation of China(51875465)
文摘The present study proposed an enhanced cuckoo search(ECS) algorithm combined with artificial neural network(ANN) as the surrogate model to solve structural reliability problems. In order to enhance the accuracy and convergence rate of the original cuckoo search(CS) algorithm, the main parameters namely, abandon probability of worst nests paand search step sizeα0 are dynamically adjusted via nonlinear control equations. In addition, a global-best guided equation incorporating the information of global best nest is introduced to the ECS to enhance its exploitation. Then, the proposed ECS is linked to the well-trained ANN model for structural reliability analysis. The computational capability of the proposed algorithm is validated using five typical structural reliability problems and an engineering application. The comparison results show the efficiency and accuracy of the proposed algorithm.
基金the National Natural Science Foundation of China(11574059)partly supported by the Guangxi Natural Science Foundation(2015GXNSFBA139252)partly supported by the program(YQ15104)from Guangxi Key Laboratory of Automatic Detecting Technology and Instruments
文摘Well organized datacentres with interconnected servers constitute the cloud computing infrastructure.User requests are submitted through an interface to these servers that provide service to them in an on-demand basis.The scientific applications that get executed at cloud by making use of the heterogeneous resources being allocated to them in a dynamic manner are grouped under NP hard problem category.Task scheduling in cloud poses numerous challenges impacting the cloud performance.If not handled properly,user satisfaction becomes questionable.More recently researchers had come up with meta-heuristic type of solutions for enriching the task scheduling activity in the cloud environment.The prime aim of task scheduling is to utilize the resources available in an optimal manner and reduce the time span of task execution.An improvised seagull optimization algorithm which combines the features of the Cuckoo search(CS)and seagull optimization algorithm(SOA)had been proposed in this work to enhance the performance of the scheduling activity inside the cloud computing environment.The proposed algorithm aims to minimize the cost and time parameters that are spent during task scheduling in the heterogeneous cloud environment.Performance evaluation of the proposed algorithm had been performed using the Cloudsim 3.0 toolkit by comparing it with Multi objective-Ant Colony Optimization(MO-ACO),ACO and Min-Min algorithms.The proposed SOA-CS technique had produced an improvement of 1.06%,4.2%,and 2.4%for makespan and had reduced the overall cost to the extent of 1.74%,3.93%and 2.77%when compared with PSO,ACO,IDEA algorithms respectively when 300 vms are considered.The comparative simulation results obtained had shown that the proposed improvised seagull optimization algorithm fares better than other contemporaries.
文摘Accurate photovoltaic(PV)power prediction can effectively help the power sector to make rational energy planning and dispatching decisions,promote PV consumption,make full use of renewable energy and alleviate energy problems.To address this research objective,this paper proposes a prediction model based on kernel principal component analysis(KPCA),modified cuckoo search algorithm(MCS)and deep convolutional neural networks(DCNN).Firstly,KPCA is utilized to reduce the dimension of the feature,which aims to reduce the redundant input vectors.Then using MCS to optimize the parameters of DCNN.Finally,the photovoltaic power forecasting method of KPCA-MCS-DCNN is established.In order to verify the prediction performance of the proposed model,this paper selects a photovoltaic power station in China for example analysis.The results show that the new hybrid KPCA-MCS-DCNN model has higher prediction accuracy and better robustness.
