The joint-bolt-African Vulture optimization algorithm(AVOA)model is proposed for the design of building connections to improve the stability of steel beam-to-column connections.For this algorithm,the type of steel is ...The joint-bolt-African Vulture optimization algorithm(AVOA)model is proposed for the design of building connections to improve the stability of steel beam-to-column connections.For this algorithm,the type of steel is first determined,and the number of bolts needed by the corresponding steel type is referenced in Eurocode 3.Then,the bearing capacity of the joint can be calculated.The joint-bolt-AVOA model is established by substituting the bolt number required by the steel into the algorithm to obtain the optimal bolt number required while ensuring joint stability.The results show that the number of bolts required by the joint-bolt-AVOA model based on the stability of steel is lower than that calculated by Eurocode 3.Therefore,AVOA can effectively optimize the number of bolts needed in building connections and save resources.展开更多
Intelligent construction has become an inevitable trend in the development of the construction industry.In the excavation project,using machine learning methods for early warning can improve construction efficiency an...Intelligent construction has become an inevitable trend in the development of the construction industry.In the excavation project,using machine learning methods for early warning can improve construction efficiency and quality and reduce the chances of damage in the excavation process.An interpretable gradient boosting based ensemble learning framework enhanced by the African Vultures Optimization Algorithm(AVOA)was proposed and evaluated in estimating the diaphragm wall deflections induced by excavation.We investigated and compared the performance of machine learning models in predicting deflections induced by excavation based on a database generated by finite element simulations.First,we exploratively analyzed these data to discover the relationship between features.We used several state-of-the-art intelligent models based on gradient boosting and several simple models for model selection.The hyperparameters for all models in evaluation are optimized using AVOA,and then the optimized models are assembled into a unified framework for fairness assessment.The comprehensive evaluation results show that the AVOA-CatBoost built in this paper performs well(RMSE=1.84,MAE=1.18,R2=0.9993)and cross-validation(RMSE=2.65±1.54,MAE=1.17±0.23,R2=0.998±0.002).In the end,in order to improve the transparency and usefulness of the model,we constructed an interpretable model from both global and local perspectives.展开更多
The integration of IoT and Deep Learning(DL)has significantly advanced real-time health monitoring and predictive maintenance in prognostic and health management(PHM).Electrocardiograms(ECGs)are widely used for cardio...The integration of IoT and Deep Learning(DL)has significantly advanced real-time health monitoring and predictive maintenance in prognostic and health management(PHM).Electrocardiograms(ECGs)are widely used for cardiovascular disease(CVD)diagnosis,but fluctuating signal patterns make classification challenging.Computer-assisted automated diagnostic tools that enhance ECG signal categorization using sophisticated algorithms and machine learning are helping healthcare practitioners manage greater patient populations.With this motivation,the study proposes a DL framework leveraging the PTB-XL ECG dataset to improve CVD diagnosis.Deep Transfer Learning(DTL)techniques extract features,followed by feature fusion to eliminate redundancy and retain the most informative features.Utilizing the African Vulture Optimization Algorithm(AVOA)for feature selection is more effective than the standard methods,as it offers an ideal balance between exploration and exploitation that results in an optimal set of features,improving classification performance while reducing redundancy.Various machine learning classifiers,including Support Vector Machine(SVM),eXtreme Gradient Boosting(XGBoost),Adaptive Boosting(AdaBoost),and Extreme Learning Machine(ELM),are used for further classification.Additionally,an ensemble model is developed to further improve accuracy.Experimental results demonstrate that the proposed model achieves the highest accuracy of 96.31%,highlighting its effectiveness in enhancing CVD diagnosis.展开更多
Improving the prediction accuracy of wind power is an effective means to reduce the impact of wind power on power grid.Therefore,we proposed an improved African vulture optimization algorithm(AVOA)to realize the predi...Improving the prediction accuracy of wind power is an effective means to reduce the impact of wind power on power grid.Therefore,we proposed an improved African vulture optimization algorithm(AVOA)to realize the prediction model of multi-objective optimization least squares support vector machine(LSSVM).Firstly,the original wind power time series was decomposed into a certain number of intrinsic modal components(IMFs)using variational modal decomposition(VMD).Secondly,random numbers in population initialization were replaced by Tent chaotic mapping,multi-objective LSSVM optimization was introduced by AVOA improved by elitist non-dominated sorting and crowding operator,and then each component was predicted.Finally,Tent multi-objective AVOA-LSSVM(TMOALSSVM)method was used to sum each component to obtain the final prediction result.The simulation results show that the improved AVOA based on Tent chaotic mapping,the improved non-dominated sorting algorithm with elite strategy,and the improved crowding operator are the optimal models for single-objective and multi-objective prediction.Among them,TMOALSSVM model has the smallest average error of stroke power values in four seasons,which are 0.0694,0.0545 and 0.0211,respectively.The average value of DS statistics in the four seasons is 0.9902,and the statistical value is the largest.The proposed model effectively predicts four seasons of wind power values on lateral and longitudinal precision,and faster and more accurately finds the optimal solution on the current solution space sets,which proves that the method has a certain scientific significance in the development of wind power prediction technology.展开更多
The uncertainties associated with multi-area power systems comprising both thermal and distributed renewable generation(DRG)sources such as solar and wind necessitate the use of an efficient load frequency control(LFC...The uncertainties associated with multi-area power systems comprising both thermal and distributed renewable generation(DRG)sources such as solar and wind necessitate the use of an efficient load frequency control(LFC)technique.Therefore,a hybrid version of two metaheuristic algorithms(arithmetic optimization and African vulture’s optimization algorithm)is developed.It is called the‘arithmetic optimized African vulture’s optimization algorithm(AOAVOA)’.This algorithm is used to tune a novel type-2 fuzzy-based proportional–derivative branched with dual degree-of-freedom proportional–integral–derivative controller for the LFC of a three-area hybrid deregulated power system.Thermal,electric vehicle(EV),and DRG sources(including a solar panel and a wind turbine system)are con-nected in area-1.Area-2 involves thermal and gas-generating units(GUs),while thermal and geothermal units are linked in area-3.Practical restrictions such as thermo-boiler dynamics,thermal-governor dead-band,and genera-tion rate constraints are also considered.The proposed LFC method is compared to other controllers and optimizers to demonstrate its superiority in rejecting step and random load disturbances.By functioning as energy storage ele-ments,EVs and DRG units can enhance dynamic responses during peak demand.As a result,the effect of the afore-mentioned units on dynamic reactions is also investigated.To validate its effectiveness,the closed-loop system is subjected to robust stability analysis and is compared to various existing control schemes from the literature.It is determined that the suggested AOAVOA improves fitness by 40.20%over the arithmetic optimizer(AO),while fre-quency regulation is improved by 4.55%over an AO-tuned type-2 fuzzy-based branched controller.展开更多
文摘The joint-bolt-African Vulture optimization algorithm(AVOA)model is proposed for the design of building connections to improve the stability of steel beam-to-column connections.For this algorithm,the type of steel is first determined,and the number of bolts needed by the corresponding steel type is referenced in Eurocode 3.Then,the bearing capacity of the joint can be calculated.The joint-bolt-AVOA model is established by substituting the bolt number required by the steel into the algorithm to obtain the optimal bolt number required while ensuring joint stability.The results show that the number of bolts required by the joint-bolt-AVOA model based on the stability of steel is lower than that calculated by Eurocode 3.Therefore,AVOA can effectively optimize the number of bolts needed in building connections and save resources.
基金National Natural Science Foundation of China(Grant Nos.42107214 and 52130905).
文摘Intelligent construction has become an inevitable trend in the development of the construction industry.In the excavation project,using machine learning methods for early warning can improve construction efficiency and quality and reduce the chances of damage in the excavation process.An interpretable gradient boosting based ensemble learning framework enhanced by the African Vultures Optimization Algorithm(AVOA)was proposed and evaluated in estimating the diaphragm wall deflections induced by excavation.We investigated and compared the performance of machine learning models in predicting deflections induced by excavation based on a database generated by finite element simulations.First,we exploratively analyzed these data to discover the relationship between features.We used several state-of-the-art intelligent models based on gradient boosting and several simple models for model selection.The hyperparameters for all models in evaluation are optimized using AVOA,and then the optimized models are assembled into a unified framework for fairness assessment.The comprehensive evaluation results show that the AVOA-CatBoost built in this paper performs well(RMSE=1.84,MAE=1.18,R2=0.9993)and cross-validation(RMSE=2.65±1.54,MAE=1.17±0.23,R2=0.998±0.002).In the end,in order to improve the transparency and usefulness of the model,we constructed an interpretable model from both global and local perspectives.
基金funded by Researchers Supporting ProjectNumber(RSPD2025R947),King Saud University,Riyadh,Saudi Arabia.
文摘The integration of IoT and Deep Learning(DL)has significantly advanced real-time health monitoring and predictive maintenance in prognostic and health management(PHM).Electrocardiograms(ECGs)are widely used for cardiovascular disease(CVD)diagnosis,but fluctuating signal patterns make classification challenging.Computer-assisted automated diagnostic tools that enhance ECG signal categorization using sophisticated algorithms and machine learning are helping healthcare practitioners manage greater patient populations.With this motivation,the study proposes a DL framework leveraging the PTB-XL ECG dataset to improve CVD diagnosis.Deep Transfer Learning(DTL)techniques extract features,followed by feature fusion to eliminate redundancy and retain the most informative features.Utilizing the African Vulture Optimization Algorithm(AVOA)for feature selection is more effective than the standard methods,as it offers an ideal balance between exploration and exploitation that results in an optimal set of features,improving classification performance while reducing redundancy.Various machine learning classifiers,including Support Vector Machine(SVM),eXtreme Gradient Boosting(XGBoost),Adaptive Boosting(AdaBoost),and Extreme Learning Machine(ELM),are used for further classification.Additionally,an ensemble model is developed to further improve accuracy.Experimental results demonstrate that the proposed model achieves the highest accuracy of 96.31%,highlighting its effectiveness in enhancing CVD diagnosis.
基金supported by National Natural Science Foundation of China(Nos.61662042,62062049)Science and Technology Plan of Gansu Province(Nos.21JR7RA288,21JR7RE174).
文摘Improving the prediction accuracy of wind power is an effective means to reduce the impact of wind power on power grid.Therefore,we proposed an improved African vulture optimization algorithm(AVOA)to realize the prediction model of multi-objective optimization least squares support vector machine(LSSVM).Firstly,the original wind power time series was decomposed into a certain number of intrinsic modal components(IMFs)using variational modal decomposition(VMD).Secondly,random numbers in population initialization were replaced by Tent chaotic mapping,multi-objective LSSVM optimization was introduced by AVOA improved by elitist non-dominated sorting and crowding operator,and then each component was predicted.Finally,Tent multi-objective AVOA-LSSVM(TMOALSSVM)method was used to sum each component to obtain the final prediction result.The simulation results show that the improved AVOA based on Tent chaotic mapping,the improved non-dominated sorting algorithm with elite strategy,and the improved crowding operator are the optimal models for single-objective and multi-objective prediction.Among them,TMOALSSVM model has the smallest average error of stroke power values in four seasons,which are 0.0694,0.0545 and 0.0211,respectively.The average value of DS statistics in the four seasons is 0.9902,and the statistical value is the largest.The proposed model effectively predicts four seasons of wind power values on lateral and longitudinal precision,and faster and more accurately finds the optimal solution on the current solution space sets,which proves that the method has a certain scientific significance in the development of wind power prediction technology.
文摘The uncertainties associated with multi-area power systems comprising both thermal and distributed renewable generation(DRG)sources such as solar and wind necessitate the use of an efficient load frequency control(LFC)technique.Therefore,a hybrid version of two metaheuristic algorithms(arithmetic optimization and African vulture’s optimization algorithm)is developed.It is called the‘arithmetic optimized African vulture’s optimization algorithm(AOAVOA)’.This algorithm is used to tune a novel type-2 fuzzy-based proportional–derivative branched with dual degree-of-freedom proportional–integral–derivative controller for the LFC of a three-area hybrid deregulated power system.Thermal,electric vehicle(EV),and DRG sources(including a solar panel and a wind turbine system)are con-nected in area-1.Area-2 involves thermal and gas-generating units(GUs),while thermal and geothermal units are linked in area-3.Practical restrictions such as thermo-boiler dynamics,thermal-governor dead-band,and genera-tion rate constraints are also considered.The proposed LFC method is compared to other controllers and optimizers to demonstrate its superiority in rejecting step and random load disturbances.By functioning as energy storage ele-ments,EVs and DRG units can enhance dynamic responses during peak demand.As a result,the effect of the afore-mentioned units on dynamic reactions is also investigated.To validate its effectiveness,the closed-loop system is subjected to robust stability analysis and is compared to various existing control schemes from the literature.It is determined that the suggested AOAVOA improves fitness by 40.20%over the arithmetic optimizer(AO),while fre-quency regulation is improved by 4.55%over an AO-tuned type-2 fuzzy-based branched controller.
