The mixed model of improved exponential and power function and unequal interval gray GM(1,1)model have poor accuracy in predicting the maximum pull-out load of anchor bolts.An optimal combination model was derived usi...The mixed model of improved exponential and power function and unequal interval gray GM(1,1)model have poor accuracy in predicting the maximum pull-out load of anchor bolts.An optimal combination model was derived using the optimally weighted combination theory and the minimum sum of logarithmic squared errors as the objective function.Two typical anchor bolt pull-out engineering cases were selected to compare the performance of the proposed model with those of existing ones.Results showed that the optimal combination model was suitable not only for the slow P-s curve but also for the steep P-s curve.Its accuracy and stable reliability,as well as its prediction capability classification,were better than those of the other prediction models.Therefore,the optimal combination model is an effective processing method for predicting the maximum pull-out load of anchor bolts according to measured data.展开更多
A new combined model is proposed to obtain predictive data value applied in state estimation for radial power distribution networks. The time delay part of the model is calculated by a recursive least squares algorith...A new combined model is proposed to obtain predictive data value applied in state estimation for radial power distribution networks. The time delay part of the model is calculated by a recursive least squares algorithm of system identification, which can gradually forget past information. The grey series part of the model uses an equal dimension new information model (EDNIM) and it applies 3 points smoothing method to preprocess the original data and modify remnant difference by GM(1,1). Through the optimization of the coefficient of the model, we are able to minimize the error variance of predictive data. A case study shows that the proposed method achieved high calculation precision and speed and it can be used to obtain the predictive value in real time state estimation of power distribution networks.展开更多
This paper investigates the production scheduling problems of allocating resources and sequencing jobs in the seru production system(SPS).As a new-type manufacturing mode arising from Japanese production practices,ser...This paper investigates the production scheduling problems of allocating resources and sequencing jobs in the seru production system(SPS).As a new-type manufacturing mode arising from Japanese production practices,seru production can achieve efficiency,flexibility,and responsiveness simultaneously.The production environment in which a set of jobs must be scheduled over a set of serus according to due date and different execution modes is considered,and a combination optimization model is provided.Motivated by the problem complexity and the characteristics of the proposed seru scheduling model,a nested partitioning method(NPM)is designed as the solution approach.Finally,computational studies are conducted,and the practicability of the proposed seru scheduling model is proven.Moreover,the efficiency of the nested partitioning solution method is demonstrated by the computational results obtained from different scenarios,and the good scalability of the proposed approach is proven via comparative analysis.展开更多
The power sector is an important factor in ensuring the development of the national economy.Scientific simulation and prediction of power consumption help achieve the balance between power generation and power consump...The power sector is an important factor in ensuring the development of the national economy.Scientific simulation and prediction of power consumption help achieve the balance between power generation and power consumption.In this paper,a Multi-strategy Hybrid Coati Optimizer(MCOA)is used to optimize the parameters of the three-parameter combinatorial optimization model TDGM(1,1,r,ξ,Csz)to realize the simulation and prediction of China's daily electricity consumption.Firstly,a novel MCOA is proposed in this paper,by making the following improvements to the Coati Optimization Algorithm(COA):(ⅰ)Introduce improved circle chaotic mapping strategy.(ⅱ)Fusing Aquila Optimizer,to enhance MCOA's exploration capabilities.(ⅲ)Adopt an adaptive optimal neighborhood jitter learning strategy.Effectively improve MCOA escape from local optimal solutions.(ⅳ)Incorporating Differential Evolution to enhance the diversity of the population.Secondly,the superiority of the MCOA algorithm is verified by comparing it with the newly proposed algorithm,the improved optimiza-tion algorithm,and the hybrid algorithm on the CEC2019 and CEC2020 test sets.Finally,in this paper,MCOA is used to optimize the parameters of TDGM(1,1,r,ξ,Csz),and this model is applied to forecast the daily electricity consumption in China and compared with the predictions of 14 models,including seven intelligent algorithm-optimized TDGM(1,1,r,ξ,Csz),and seven forecasting models.The experimental results show that the error of the proposed method is minimized,which verifies the validity of the proposed method.展开更多
基金The National Natural Science Foundation of China(No.51778485).
文摘The mixed model of improved exponential and power function and unequal interval gray GM(1,1)model have poor accuracy in predicting the maximum pull-out load of anchor bolts.An optimal combination model was derived using the optimally weighted combination theory and the minimum sum of logarithmic squared errors as the objective function.Two typical anchor bolt pull-out engineering cases were selected to compare the performance of the proposed model with those of existing ones.Results showed that the optimal combination model was suitable not only for the slow P-s curve but also for the steep P-s curve.Its accuracy and stable reliability,as well as its prediction capability classification,were better than those of the other prediction models.Therefore,the optimal combination model is an effective processing method for predicting the maximum pull-out load of anchor bolts according to measured data.
文摘A new combined model is proposed to obtain predictive data value applied in state estimation for radial power distribution networks. The time delay part of the model is calculated by a recursive least squares algorithm of system identification, which can gradually forget past information. The grey series part of the model uses an equal dimension new information model (EDNIM) and it applies 3 points smoothing method to preprocess the original data and modify remnant difference by GM(1,1). Through the optimization of the coefficient of the model, we are able to minimize the error variance of predictive data. A case study shows that the proposed method achieved high calculation precision and speed and it can be used to obtain the predictive value in real time state estimation of power distribution networks.
基金This research was sponsored by National Natural Science Foundation of China(Grant No.71401075,71801129)the Fundamental Research Funds for the Central Universities(No.30922011406)+1 种基金System Science and Enterprise Development Research Center(Grant No.Xq22B06)Grant-in-Aid for Scientific Research(C)of Japan(Grant No.20K01897).
文摘This paper investigates the production scheduling problems of allocating resources and sequencing jobs in the seru production system(SPS).As a new-type manufacturing mode arising from Japanese production practices,seru production can achieve efficiency,flexibility,and responsiveness simultaneously.The production environment in which a set of jobs must be scheduled over a set of serus according to due date and different execution modes is considered,and a combination optimization model is provided.Motivated by the problem complexity and the characteristics of the proposed seru scheduling model,a nested partitioning method(NPM)is designed as the solution approach.Finally,computational studies are conducted,and the practicability of the proposed seru scheduling model is proven.Moreover,the efficiency of the nested partitioning solution method is demonstrated by the computational results obtained from different scenarios,and the good scalability of the proposed approach is proven via comparative analysis.
基金supported by the National Natural Science Foundation of China(Grant Nos.52375264 and 62376212).
文摘The power sector is an important factor in ensuring the development of the national economy.Scientific simulation and prediction of power consumption help achieve the balance between power generation and power consumption.In this paper,a Multi-strategy Hybrid Coati Optimizer(MCOA)is used to optimize the parameters of the three-parameter combinatorial optimization model TDGM(1,1,r,ξ,Csz)to realize the simulation and prediction of China's daily electricity consumption.Firstly,a novel MCOA is proposed in this paper,by making the following improvements to the Coati Optimization Algorithm(COA):(ⅰ)Introduce improved circle chaotic mapping strategy.(ⅱ)Fusing Aquila Optimizer,to enhance MCOA's exploration capabilities.(ⅲ)Adopt an adaptive optimal neighborhood jitter learning strategy.Effectively improve MCOA escape from local optimal solutions.(ⅳ)Incorporating Differential Evolution to enhance the diversity of the population.Secondly,the superiority of the MCOA algorithm is verified by comparing it with the newly proposed algorithm,the improved optimiza-tion algorithm,and the hybrid algorithm on the CEC2019 and CEC2020 test sets.Finally,in this paper,MCOA is used to optimize the parameters of TDGM(1,1,r,ξ,Csz),and this model is applied to forecast the daily electricity consumption in China and compared with the predictions of 14 models,including seven intelligent algorithm-optimized TDGM(1,1,r,ξ,Csz),and seven forecasting models.The experimental results show that the error of the proposed method is minimized,which verifies the validity of the proposed method.
