Metaheuristic algorithms are pivotal in cloud task scheduling. However, the complexity and uncertainty of the scheduling problem severely limit algorithms. To bypass this circumvent, numerous algorithms have been prop...Metaheuristic algorithms are pivotal in cloud task scheduling. However, the complexity and uncertainty of the scheduling problem severely limit algorithms. To bypass this circumvent, numerous algorithms have been proposed. The Hiking Optimization Algorithm (HOA) have been used in multiple fields. However, HOA suffers from local optimization, slow convergence, and low efficiency of late iteration search when solving cloud task scheduling problems. Thus, this paper proposes an improved HOA called CMOHOA. It collaborates with multi-strategy to improve HOA. Specifically, Chebyshev chaos is introduced to increase population diversity. Then, a hybrid speed update strategy is designed to enhance convergence speed. Meanwhile, an adversarial learning strategy is introduced to enhance the search capability in the late iteration. Different scenarios of scheduling problems are used to test the CMOHOA’s performance. First, CMOHOA was used to solve basic cloud computing task scheduling problems, and the results showed that it reduced the average total cost by 10% or more. Secondly, CMOHOA has been applied to edge fog cloud scheduling problems, and the results show that it reduces the average total scheduling cost by 2% or more. Finally, CMOHOA reduced the average total cost by 7% or more in scheduling problems for information transmission.展开更多
Crop yield prediction helps to enhance the stability of agricultural product supply and promote sustainable agricultural development,both of which are crucial for food production and security.To develop simple yet hig...Crop yield prediction helps to enhance the stability of agricultural product supply and promote sustainable agricultural development,both of which are crucial for food production and security.To develop simple yet highly accurate crop yield prediction models,this study proposed a spring-and summer-maize yield prediction model based on the deep hybrid kernel extreme learning machine(DHKELM)algorithm.In this study,four tree-based feature importance analysis algorithms,including classification and regression tree,gradient boosting decision tree,random forest,and extreme gradient boosting algorithms,were utilized to analyze the importance of the factors affecting the yield of spring and summer maize.Then,based on the analysis of the four algorithms,different combinations of factors were established to obtain the optimal combination of features.Moreover,to improve the prediction accuracy of the machine learning model,this study utilized three optimization algorithms,including the bald eagle search algorithm,chaos game optimization(CGO)algorithm,and carnivorous plant algorithm,to optimize the hyperparameters in the DHKELM algorithm.The results of the study showed that planting density and plant height were important factors affecting maize yield,and net solar radiation(R_(n))received during the reproductive period exhibited the highest relative importance.Appropriate feature combinations can effectively improve model prediction accuracy.The optimal feature combination for spring maize included planting density,plant height,R_(n),mean temperature(T_(mean)),minimum temperature(T_(min)),and cumulative temperature,and the optimal feature combination for summer maize included Rn,plant height,planting density,T_(min),and T_(mean).Among the three optimization algorithms,the CGO algorithm exhibited the best optimization effect and could significantly improve the prediction accuracy of the DHKELM algorithm.When the optimal combination of features was used as input,the CGO-DHKELM model used for maize yield prediction provided the following values:RMSE=1.488 t/hm^(2),R^(2)=0.862,MAE=1.051 t/hm^(2),and NSE=0.852 for spring maize;RMSE=1.498 t/hm^(2),R^(2)=0.892,MAE=1.055 t/hm2,and NSE=0.891 for summer maize.Thus,the findings of the study provide a reference for high-precision prediction of spring and summer maize yields in China.展开更多
基金supported by the National Natural Science Foundation of China (52275480)the Guizhou Provincial Science and Technology Program of Qiankehe Zhongdi Guiding ([2023]02)+1 种基金the Guizhou Provincial Science and Technology Program of Qiankehe Platform Talent Project (GCC[2023]001)the Guizhou Provincial Science and Technology Project of Qiankehe Platform Project (KXJZ[2024]002).
文摘Metaheuristic algorithms are pivotal in cloud task scheduling. However, the complexity and uncertainty of the scheduling problem severely limit algorithms. To bypass this circumvent, numerous algorithms have been proposed. The Hiking Optimization Algorithm (HOA) have been used in multiple fields. However, HOA suffers from local optimization, slow convergence, and low efficiency of late iteration search when solving cloud task scheduling problems. Thus, this paper proposes an improved HOA called CMOHOA. It collaborates with multi-strategy to improve HOA. Specifically, Chebyshev chaos is introduced to increase population diversity. Then, a hybrid speed update strategy is designed to enhance convergence speed. Meanwhile, an adversarial learning strategy is introduced to enhance the search capability in the late iteration. Different scenarios of scheduling problems are used to test the CMOHOA’s performance. First, CMOHOA was used to solve basic cloud computing task scheduling problems, and the results showed that it reduced the average total cost by 10% or more. Secondly, CMOHOA has been applied to edge fog cloud scheduling problems, and the results show that it reduces the average total scheduling cost by 2% or more. Finally, CMOHOA reduced the average total cost by 7% or more in scheduling problems for information transmission.
基金National Natural Science Foundation of China(Grant No.52309050,32372680)Youth Backbone Teacher Project of Henan University of Science and Technology(Grant No.13450013 and 3450010)+1 种基金Key Scientific Research Projects of Colleges and Universities in Henan Province(Grant No.24B416001)Innovative Research Team(Science and Technology)in the University of Henan Province(Grant No.23IRTSTHN024).
文摘Crop yield prediction helps to enhance the stability of agricultural product supply and promote sustainable agricultural development,both of which are crucial for food production and security.To develop simple yet highly accurate crop yield prediction models,this study proposed a spring-and summer-maize yield prediction model based on the deep hybrid kernel extreme learning machine(DHKELM)algorithm.In this study,four tree-based feature importance analysis algorithms,including classification and regression tree,gradient boosting decision tree,random forest,and extreme gradient boosting algorithms,were utilized to analyze the importance of the factors affecting the yield of spring and summer maize.Then,based on the analysis of the four algorithms,different combinations of factors were established to obtain the optimal combination of features.Moreover,to improve the prediction accuracy of the machine learning model,this study utilized three optimization algorithms,including the bald eagle search algorithm,chaos game optimization(CGO)algorithm,and carnivorous plant algorithm,to optimize the hyperparameters in the DHKELM algorithm.The results of the study showed that planting density and plant height were important factors affecting maize yield,and net solar radiation(R_(n))received during the reproductive period exhibited the highest relative importance.Appropriate feature combinations can effectively improve model prediction accuracy.The optimal feature combination for spring maize included planting density,plant height,R_(n),mean temperature(T_(mean)),minimum temperature(T_(min)),and cumulative temperature,and the optimal feature combination for summer maize included Rn,plant height,planting density,T_(min),and T_(mean).Among the three optimization algorithms,the CGO algorithm exhibited the best optimization effect and could significantly improve the prediction accuracy of the DHKELM algorithm.When the optimal combination of features was used as input,the CGO-DHKELM model used for maize yield prediction provided the following values:RMSE=1.488 t/hm^(2),R^(2)=0.862,MAE=1.051 t/hm^(2),and NSE=0.852 for spring maize;RMSE=1.498 t/hm^(2),R^(2)=0.892,MAE=1.055 t/hm2,and NSE=0.891 for summer maize.Thus,the findings of the study provide a reference for high-precision prediction of spring and summer maize yields in China.
