This paper introduces an optimization method(SCE-SR)that combines shuffled complex evolution(SCE)and stochastic ranking(SR)to solve constrained reservoir scheduling problems,ranking individuals with both objectives an...This paper introduces an optimization method(SCE-SR)that combines shuffled complex evolution(SCE)and stochastic ranking(SR)to solve constrained reservoir scheduling problems,ranking individuals with both objectives and constrains considered.A specialized strategy is used in the evolution process to ensure that the optimal results are feasible individuals.This method is suitable for handling multiple conflicting constraints,and is easy to implement,requiring little parameter tuning.The search properties of the method are ensured through the combination of deterministic and probabilistic approaches.The proposed SCE-SR was tested against hydropower scheduling problems of a single reservoir and a multi-reservoir system,and its performance is compared with that of two classical methods(the dynamic programming and genetic algorithm).The results show that the SCE-SR method is an effective and efficient method for optimizing hydropower generation and locating feasible regions quickly,with sufficient global convergence properties and robustness.The operation schedules obtained satisfy the basic scheduling requirements of reservoirs.展开更多
Accurately simulating the soil nitrogen(N)cycle is crucial for assessing food security and resource utilization efficiency.The accuracy of model predictions relies heavily on model parameterization.The sensitivity and...Accurately simulating the soil nitrogen(N)cycle is crucial for assessing food security and resource utilization efficiency.The accuracy of model predictions relies heavily on model parameterization.The sensitivity and uncertainty of the simulations of soil N cycle of winter wheat-summer maize rotation system in the North China Plain(NCP)to the parameters were analyzed.First,the N module in the Vegetation Interface Processes(VIP)model was expanded to capture the dynamics of soil N cycle calibrated with field measurements in three ecological stations from 2000 to 2015.Second,the Morris and Sobol algorithms were adopted to identify the sensitive parameters that impact soil nitrate stock,denitrification rate,and ammonia volatilization rate.Finally,the shuffled complex evolution developed at the University of Arizona(SCE-UA)algorithm was used to optimize the selected sensitive parameters to improve prediction accuracy.The results showed that the sensitive parameters related to soil nitrate stock included the potential nitrification rate,Michaelis constant,microbial C/N ratio,and slow humus C/N ratio,the sensitive parameters related to denitrification rate were the potential denitrification rate,Michaelis constant,and N2 O production rate,and the sensitive parameters related to ammonia volatilization rate included the coefficient of ammonia volatilization exchange and potential nitrification rate.Based on the optimized parameters,prediction efficiency was notably increased with the highest coefficient of determination being approximately 0.8.Moreover,the average relative interval length at the 95% confidence level for soil nitrate stock,denitrification rate,and ammonia volatilization rate were 11.92,0.008,and 4.26,respectively,and the percentages of coverage of the measured values in the 95% confidence interval were 68%,86%,and 92%,respectively.By identifying sensitive parameters related to soil N,the expanded VIP model optimized by the SCE-UA algorithm can effectively simulate the dynamics of soil nitrate stock,denitrification rate,and ammonia volatilization rate in the NCP.展开更多
In this study,we developed novel hybrid models namely Adaptive Neuro Fuzzy Inference System(ANFIS)optimized by Shuffled Complex Evolution(SCE)on the one hand and ANFIS with Artificial Bee Colony(ABC)on the other hand....In this study,we developed novel hybrid models namely Adaptive Neuro Fuzzy Inference System(ANFIS)optimized by Shuffled Complex Evolution(SCE)on the one hand and ANFIS with Artificial Bee Colony(ABC)on the other hand.These were used to predict compressive strength(Cs)of concrete relating to thirteen concrete-strength affecting parameters which are easy to determine in the laboratory.Field and laboratory tests data of 108 structural elements of 18 concrete bridges of the Ha Long-Van Don Expressway,Vietnam were considered.The dataset was randomly divided into a 70:30 ratio,for training(70%)and testing(30%)of the hybrid models.Performance of the developed fuzzy metaheuristic models was evaluated using standard statistical metrics:Correlation Coefficient(R),Root Mean Square Error(RMSE)and Mean Absolute Error(MAE).The results showed that both of the novel models depict close agreement between experimental and predicted results.However,the ANFIS-ABC model reflected better convergence of the results and better performance compared to that of ANFIS-SCE in the prediction of the concrete Cs.Thus,the ANFIS-ABC model can be used for the quick and accurate estimation of compressive strength of concrete based on easily determined parameters for the design of civil engineering structures including bridges.展开更多
基金supported by the National Key Research and Development Program of China(Grant No.2016YFC0401702)the Fundamental Research Funds for the Central Universities(Grant No.2018B11214)the National Natural Science Foundation of China(Grants No.51379059 and 51579002)
文摘This paper introduces an optimization method(SCE-SR)that combines shuffled complex evolution(SCE)and stochastic ranking(SR)to solve constrained reservoir scheduling problems,ranking individuals with both objectives and constrains considered.A specialized strategy is used in the evolution process to ensure that the optimal results are feasible individuals.This method is suitable for handling multiple conflicting constraints,and is easy to implement,requiring little parameter tuning.The search properties of the method are ensured through the combination of deterministic and probabilistic approaches.The proposed SCE-SR was tested against hydropower scheduling problems of a single reservoir and a multi-reservoir system,and its performance is compared with that of two classical methods(the dynamic programming and genetic algorithm).The results show that the SCE-SR method is an effective and efficient method for optimizing hydropower generation and locating feasible regions quickly,with sufficient global convergence properties and robustness.The operation schedules obtained satisfy the basic scheduling requirements of reservoirs.
基金financially supported by the National Natural Science Foundations of China(Nos.41790424 and 41471026)。
文摘Accurately simulating the soil nitrogen(N)cycle is crucial for assessing food security and resource utilization efficiency.The accuracy of model predictions relies heavily on model parameterization.The sensitivity and uncertainty of the simulations of soil N cycle of winter wheat-summer maize rotation system in the North China Plain(NCP)to the parameters were analyzed.First,the N module in the Vegetation Interface Processes(VIP)model was expanded to capture the dynamics of soil N cycle calibrated with field measurements in three ecological stations from 2000 to 2015.Second,the Morris and Sobol algorithms were adopted to identify the sensitive parameters that impact soil nitrate stock,denitrification rate,and ammonia volatilization rate.Finally,the shuffled complex evolution developed at the University of Arizona(SCE-UA)algorithm was used to optimize the selected sensitive parameters to improve prediction accuracy.The results showed that the sensitive parameters related to soil nitrate stock included the potential nitrification rate,Michaelis constant,microbial C/N ratio,and slow humus C/N ratio,the sensitive parameters related to denitrification rate were the potential denitrification rate,Michaelis constant,and N2 O production rate,and the sensitive parameters related to ammonia volatilization rate included the coefficient of ammonia volatilization exchange and potential nitrification rate.Based on the optimized parameters,prediction efficiency was notably increased with the highest coefficient of determination being approximately 0.8.Moreover,the average relative interval length at the 95% confidence level for soil nitrate stock,denitrification rate,and ammonia volatilization rate were 11.92,0.008,and 4.26,respectively,and the percentages of coverage of the measured values in the 95% confidence interval were 68%,86%,and 92%,respectively.By identifying sensitive parameters related to soil N,the expanded VIP model optimized by the SCE-UA algorithm can effectively simulate the dynamics of soil nitrate stock,denitrification rate,and ammonia volatilization rate in the NCP.
文摘In this study,we developed novel hybrid models namely Adaptive Neuro Fuzzy Inference System(ANFIS)optimized by Shuffled Complex Evolution(SCE)on the one hand and ANFIS with Artificial Bee Colony(ABC)on the other hand.These were used to predict compressive strength(Cs)of concrete relating to thirteen concrete-strength affecting parameters which are easy to determine in the laboratory.Field and laboratory tests data of 108 structural elements of 18 concrete bridges of the Ha Long-Van Don Expressway,Vietnam were considered.The dataset was randomly divided into a 70:30 ratio,for training(70%)and testing(30%)of the hybrid models.Performance of the developed fuzzy metaheuristic models was evaluated using standard statistical metrics:Correlation Coefficient(R),Root Mean Square Error(RMSE)and Mean Absolute Error(MAE).The results showed that both of the novel models depict close agreement between experimental and predicted results.However,the ANFIS-ABC model reflected better convergence of the results and better performance compared to that of ANFIS-SCE in the prediction of the concrete Cs.Thus,the ANFIS-ABC model can be used for the quick and accurate estimation of compressive strength of concrete based on easily determined parameters for the design of civil engineering structures including bridges.
