With the widespread application of large language models(LLMs)in natural language processing and code generation,traditional High-Level Language Programming courses are facing unprecedented challenges and opportunitie...With the widespread application of large language models(LLMs)in natural language processing and code generation,traditional High-Level Language Programming courses are facing unprecedented challenges and opportunities.As a core programming language for computer science majors,C language remains irreplaceable due to its foundational nature and engineering adaptability.This paper,based on the rapid development of large model technologies,proposes a systematic reform design for C language teaching,focusing on teaching objectives,content structure,teaching methods,and evaluation systems.The article suggests a teaching framework centered on“human-computer collaborative programming,”integrating prompt training,AI-assisted debugging,and code generation analysis,aiming to enhance students’problem modeling ability,programming expression skills,and AI collaboration literacy.展开更多
Satellite Component Layout Optimization(SCLO) is crucial in satellite system design.This paper proposes a novel Satellite Three-Dimensional Component Assignment and Layout Optimization(3D-SCALO) problem tailored to en...Satellite Component Layout Optimization(SCLO) is crucial in satellite system design.This paper proposes a novel Satellite Three-Dimensional Component Assignment and Layout Optimization(3D-SCALO) problem tailored to engineering requirements, aiming to optimize satellite heat dissipation while considering constraints on static stability, 3D geometric relationships between components, and special component positions. The 3D-SCALO problem is a challenging bilevel combinatorial optimization task, involving the optimization of discrete component assignment variables in the outer layer and continuous component position variables in the inner layer,with both influencing each other. To address this issue, first, a Mixed Integer Programming(MIP) model is proposed, which reformulates the original bilevel problem into a single-level optimization problem, enabling the exploration of a more comprehensive optimization space while avoiding iterative nested optimization. Then, to model the 3D geometric relationships between components within the MIP framework, a linearized 3D Phi-function method is proposed, which handles non-overlapping and safety distance constraints between cuboid components in an explicit and effective way. Subsequently, the Finite-Rectangle Method(FRM) is proposed to manage 3D geometric constraints for complex-shaped components by approximating them with a finite set of cuboids, extending the applicability of the geometric modeling approach. Finally, the feasibility and effectiveness of the proposed MIP model are demonstrated through two numerical examples"and a real-world engineering case, which confirms its suitability for complex-shaped components and real engineering applications.展开更多
To establish the optimal reference trajectory for a near-space vehicle under free terminal time,a time-optimal model predictive static programming method is proposed with adaptive fish swarm optimization.First,the mod...To establish the optimal reference trajectory for a near-space vehicle under free terminal time,a time-optimal model predictive static programming method is proposed with adaptive fish swarm optimization.First,the model predictive static programming method is developed by incorporating neighboring terms and trust region,enabling rapid generation of precise optimal solutions.Next,an adaptive fish swarm optimization technique is employed to identify a sub-optimal solution,while a momentum gradient descent method with learning rate decay ensures the convergence to the global optimal solution.To validate the feasibility and accuracy of the proposed method,a near-space vehicle example is analyzed and simulated during its glide phase.The simulation results demonstrate that the proposed method aligns with theoretical derivations and outperforms existing methods in terms of convergence speed and accuracy.Therefore,the proposed method offers significant practical value for solving the fast trajectory optimization problem in near-space vehicle applications.展开更多
In order to address the output feedback issue for linear discrete-time systems, this work suggests a brand-new adaptive dynamic programming(ADP) technique based on the internal model principle(IMP). The proposed metho...In order to address the output feedback issue for linear discrete-time systems, this work suggests a brand-new adaptive dynamic programming(ADP) technique based on the internal model principle(IMP). The proposed method, termed as IMP-ADP, does not require complete state feedback-merely the measurement of input and output data. More specifically, based on the IMP, the output control problem can first be converted into a stabilization problem. We then design an observer to reproduce the full state of the system by measuring the inputs and outputs. Moreover, this technique includes both a policy iteration algorithm and a value iteration algorithm to determine the optimal feedback gain without using a dynamic system model. It is important that with this concept one does not need to solve the regulator equation. Finally, this control method was tested on an inverter system of grid-connected LCLs to demonstrate that the proposed method provides the desired performance in terms of both tracking and disturbance rejection.展开更多
Hydrocracking is one of the most important petroleum refining processes that converts heavy oils into gases,naphtha,diesel,and other products through cracking reactions.Multi-objective optimization algorithms can help...Hydrocracking is one of the most important petroleum refining processes that converts heavy oils into gases,naphtha,diesel,and other products through cracking reactions.Multi-objective optimization algorithms can help refining enterprises determine the optimal operating parameters to maximize product quality while ensuring product yield,or to increase product yield while reducing energy consumption.This paper presents a multi-objective optimization scheme for hydrocracking based on an improved SPEA2-PE algorithm,which combines path evolution operator and adaptive step strategy to accelerate the convergence speed and improve the computational accuracy of the algorithm.The reactor model used in this article is simulated based on a twenty-five lumped kinetic model.Through model and test function verification,the proposed optimization scheme exhibits significant advantages in the multiobjective optimization process of hydrocracking.展开更多
This study proposes a multi-objective optimization framework for electric winches in fiber-reinforced plastic(FRP)fishing vessels to address critical limitations of conventional designs,including excessive weight,mate...This study proposes a multi-objective optimization framework for electric winches in fiber-reinforced plastic(FRP)fishing vessels to address critical limitations of conventional designs,including excessive weight,material inefficiency,and performance redundancy.By integrating surrogate modeling techniques with a multi-objective genetic algorithm(MOGA),we have developed a systematic approach that encompasses parametric modeling,finite element analysis under extreme operational conditions,and multi-fidelity performance evaluation.Through a 10-t electric winch case study,the methodology’s effectiveness is demonstrated via parametric characterization of structural integrity,stiffness behavior,and mass distribution.The comparative analysis identified optimal surrogate models for predicting key performance metrics,which enabled the construction of a robust multi-objective optimization model.The MOGA-derived Pareto solutions produced a design configuration achieving 7.86%mass reduction,2.01%safety factor improvement,and 23.97%deformation mitigation.Verification analysis confirmed the optimization scheme’s reliability in balancing conflicting design requirements.This research establishes a generalized framework for marine deck machinery modernization,particularly addressing the structural compatibility challenges in FRP vessel retrofitting.The proposed methodology demonstrates significant potential for facilitating sustainable upgrades of fishing vessel equipment through systematic performance optimization.展开更多
Given the growing concern over global warming and the critical role of carbon dioxide(CO_(2))in this phenomenon,the study of CO_(2)-induced alterations in coal strength has garnered significant attention due to its im...Given the growing concern over global warming and the critical role of carbon dioxide(CO_(2))in this phenomenon,the study of CO_(2)-induced alterations in coal strength has garnered significant attention due to its implications for carbon sequestration.A large number of experiments have proved that CO_(2) interaction time(T),saturation pressure(P)and other parameters have significant effects on coal strength.However,accurate evaluation of CO_(2)-induced alterations in coal strength is still a difficult problem,so it is particularly important to establish accurate and efficient prediction models.This study explored the application of advancedmachine learning(ML)algorithms and Gene Expression Programming(GEP)techniques to predict CO_(2)-induced alterations in coal strength.Sixmodels were developed,including three metaheuristic-optimized XGBoost models(GWO-XGBoost,SSA-XGBoost,PO-XGBoost)and three GEP models(GEP-1,GEP-2,GEP-3).Comprehensive evaluations using multiple metrics revealed that all models demonstrated high predictive accuracy,with the SSA-XGBoost model achieving the best performance(R2—Coefficient of determination=0.99396,RMSE—Root Mean Square Error=0.62102,MAE—Mean Absolute Error=0.36164,MAPE—Mean Absolute Percentage Error=4.8101%,RPD—Residual Predictive Deviation=13.4741).Model interpretability analyses using SHAP(Shapley Additive exPlanations),ICE(Individual Conditional Expectation),and PDP(Partial Dependence Plot)techniques highlighted the dominant role of fixed carbon content(FC)and significant interactions between FC and CO_(2) saturation pressure(P).Theresults demonstrated that the proposedmodels effectively address the challenges of CO_(2)-induced strength prediction,providing valuable insights for geological storage safety and environmental applications.展开更多
The principal-subordinate hierarchical multi-objective programming model of initial water rights allocation was developed based on the principle of coordinated and sustainable development of different regions and wate...The principal-subordinate hierarchical multi-objective programming model of initial water rights allocation was developed based on the principle of coordinated and sustainable development of different regions and water sectors within a basin. With the precondition of strictly controlling maximum emissions rights, initial water rights were allocated between the first and the second levels of the hierarchy in order to promote fair and coordinated development across different regions of the basin and coordinated and efficient water use across different water sectors, realize the maximum comprehensive benefits to the basin, promote the unity of quantity and quality of initial water rights allocation, and eliminate water conflict across different regions and water sectors. According to interactive decision-making theory, a principal-subordinate hierarchical interactive iterative algorithm based on the satisfaction degree was developed and used to solve the initial water rights allocation model. A case study verified the validity of the model.展开更多
According to Hainan Island's biological characteristics, and existing structure of productivity of tropical crops and local climatic conditions, this paper carries on regional division of tropical crops by fuzzy m...According to Hainan Island's biological characteristics, and existing structure of productivity of tropical crops and local climatic conditions, this paper carries on regional division of tropical crops by fuzzy mathematics. Based on calculation of basic parameters for tl1e formation of production, near-tem optimum models of tropical crops structure of each region was established by means of multi-objective programming, and a far-term grey programming model was set up through the above-mentioned near-term model and prediction of future parameters. Conclusion shows that the near-term programming may raise the profit by 5. 1-55.7 percent and far-tem programming by 54-90 percent, both gainingobvious economic benefits.展开更多
An important decision for policy makers is selecting strategic petroleum reserve sites. However, policy makers may not choose the most suitable and efficient locations for strategic petroleum reserve（SPR） due to the...An important decision for policy makers is selecting strategic petroleum reserve sites. However, policy makers may not choose the most suitable and efficient locations for strategic petroleum reserve（SPR） due to the complexity in the choice of sites. This paper proposes a multi-objective programming model to determine the optimal locations for China＇s SPR storage sites. This model considers not only the minimum response time but also the minimum transportation cost based on a series of reasonable assumptions and constraint conditions. The factors influencing SPR sites are identified to determine potential demand points and candidate storage sites. Estimation and suggestions are made for the selection of China＇s future SPR storage sites based on the results of this model. When the number of petroleum storage sites is less than or equals 25 and the maximum capacity of storage sites is restricted to 10 million tonnes, the model＇s result best fit for the current layout scheme selected thirteen storage sites in four scenarios. Considering the current status of SPR in China,Tianjin, Qingdao, Dalian, Daqing and Zhanjiang, Chengdu,Xi＇an, and Yueyang are suggested to be the candidate locations for the third phase of the construction plan. The locations of petroleum storage sites suggested in this work could be used as a reference for decision makers.展开更多
The current portfolio model for property-liability insurance company is only single period that can not meet the practical demands of portfolio management, and the purpose of this paper is to develop a multiperiod mod...The current portfolio model for property-liability insurance company is only single period that can not meet the practical demands of portfolio management, and the purpose of this paper is to develop a multiperiod model for its portfolio problem. The model is a multistage stochastic programming which considers transaction costs, cash flow between time periods, and the matching of asset and liability; it does not depend on the assumption for normality of return distribution. Additionally, an investment constraint is added. The numerical example manifests that the multiperiod model can more effectively assist the property-liability insurer to determine the optimal composition of insurance and investment portfolio and outperforms the single period one.展开更多
This study delineates the development of the optimization framework for the preliminary design phase of Floating Offshore Wind Turbines(FOWTs),and the central challenge addressed is the optimization of the FOWT platfo...This study delineates the development of the optimization framework for the preliminary design phase of Floating Offshore Wind Turbines(FOWTs),and the central challenge addressed is the optimization of the FOWT platform dimensional parameters in relation to motion responses.Although the three-dimensional potential flow(TDPF)panel method is recognized for its precision in calculating FOWT motion responses,its computational intensity necessitates an alternative approach for efficiency.Herein,a novel application of varying fidelity frequency-domain computational strategies is introduced,which synthesizes the strip theory with the TDPF panel method to strike a balance between computational speed and accuracy.The Co-Kriging algorithm is employed to forge a surrogate model that amalgamates these computational strategies.Optimization objectives are centered on the platform’s motion response in heave and pitch directions under general sea conditions.The steel usage,the range of design variables,and geometric considerations are optimization constraints.The angle of the pontoons,the number of columns,the radius of the central column and the parameters of the mooring lines are optimization constants.This informed the structuring of a multi-objective optimization model utilizing the Non-dominated Sorting Genetic Algorithm Ⅱ(NSGA-Ⅱ)algorithm.For the case of the IEA UMaine VolturnUS-S Reference Platform,Pareto fronts are discerned based on the above framework and delineate the relationship between competing motion response objectives.The efficacy of final designs is substantiated through the time-domain calculation model,which ensures that the motion responses in extreme sea conditions are superior to those of the initial design.展开更多
Partial cooperation models are studied for many years to solve the bilevel programming problems where the follower’s optimal reaction is not unique. However, in these existed models, the follower’s cooperation level...Partial cooperation models are studied for many years to solve the bilevel programming problems where the follower’s optimal reaction is not unique. However, in these existed models, the follower’s cooperation level does not depend on the leader’s decision. A new model is proposed to solve this deficiency. It is proved the feasibility of the new model when the reaction set of the lower level is lower semicontinuous. And the numerical results show that the new model has optimal solutions when the reaction set of the lower level is discrete, lower semi-continuous and non-lower semi-continuous.展开更多
Estimation of the rock mass modulus of deformation(Em)is one of the most important design parameters in designing many structures in and on rock.This parameter can be obtained by in situ tests,empirical relations betw...Estimation of the rock mass modulus of deformation(Em)is one of the most important design parameters in designing many structures in and on rock.This parameter can be obtained by in situ tests,empirical relations between deformation modulus and rock mass classifcation,and estimating from laboratory tests results.In this paper,a back analysis calculation is performed to present an equation for estimation of the rock mass modulus of deformation using genetic programming(GP)and numerical modeling.A database of 40,960 datasets,including vertical stress(rz),horizontal to vertical stresses ratio(k),Poisson’s ratio(m),radius of circular tunnel(r)and wall displacement of circular tunnel on the horizontal diameter(d)for input parameters and modulus of deformation for output,was established.The selected parameters are easy to determine and rock mass modulus of deformation can be obtained from instrumentation data of any size circular galleries.The resulting RMSE of 0.86 and correlation coeffcient of97%of the proposed equation demonstrated the capability of the computer program(CP)generated by GP.展开更多
The developments of modern mathematics and computer science make artificial neural networks become most useful tools in wide range of fields. Modeling methods of artificial neural networks are described in this paper...The developments of modern mathematics and computer science make artificial neural networks become most useful tools in wide range of fields. Modeling methods of artificial neural networks are described in this paper. The programming technique by using Matlab neural networks toolbox is discussed. The application in Material Hot Working of neural networks is also introduced.展开更多
This study presented a simulation-based two-stage interval-stochastic programming （STIP） model to support water resources management in the Kaidu-Konqi watershed in Northwest China. The modeling system coupled a dis...This study presented a simulation-based two-stage interval-stochastic programming （STIP） model to support water resources management in the Kaidu-Konqi watershed in Northwest China. The modeling system coupled a distributed hydrological model with an interval two-stage stochastic programing （ITSP）. The distributed hydrological model was used for establishing a rainfall-runoff forecast system, while random parameters were pro- vided by the statistical analysis of simulation outcomes water resources management planning in Kaidu-Konqi The developed STIP model was applied to a real case of watershed, where three scenarios with different water re- sources management policies were analyzed. The results indicated that water shortage mainly occurred in agri- culture, ecology and forestry sectors. In comparison, the water demand from municipality, industry and stock- breeding sectors can be satisfied due to their lower consumptions and higher economic values. Different policies for ecological water allocation can result in varied system benefits, and can help to identify desired water allocation plans with a maximum economic benefit and a minimum risk of system disruption under uncertainty.展开更多
A novel Parsimonious Genetic Programming （PGP） algorithm together with a novel aero-engine optimum data-driven dynamic start process model based on PGP is proposed. In application of this method, first, the traditio...A novel Parsimonious Genetic Programming （PGP） algorithm together with a novel aero-engine optimum data-driven dynamic start process model based on PGP is proposed. In application of this method, first, the traditional Genetic Programming（GP） is used to generate the nonlinear input-output models that are represented in a binary tree structure; then, the Orthogonal Least Squares algorithm （OLS） is used to estimate the contribution of the branches of the tree （refer to basic function term that cannot be decomposed anymore according to special rule） to the accuracy of the model, which contributes to eliminate complex redundant subtrees and enhance GP＇s convergence speed; and finally, a simple, reliable and exact linear-in-parameter nonlinear model via GP evolution is obtained. The real aero-engine start process test data simulation and the comparisons with Support Vector Machines （SVM） validate that the proposed method can generate more applicable, interpretable models and achieve comparable, even superior results to SVM.展开更多
Considering the decision-making variables of the capacities of branch roads and the optimization targets of lowering the saturation of arterial roads and the reconstruction expense of branch roads, the bi-level progra...Considering the decision-making variables of the capacities of branch roads and the optimization targets of lowering the saturation of arterial roads and the reconstruction expense of branch roads, the bi-level programming model for reconstructing the branch roads was set up. The upper level model was for determining the enlarged capacities of the branch roads, and the lower level model was for calculating the flows of road sections via the user equilibrium traffic assignment method. The genetic algorithm for solving the bi-level model was designed to obtain the reconstruction capacities of the branch roads. The results show that by the bi-level model and its algorithm, the optimum scheme of urban branch roads reconstruction can be gained, which reduces the saturation of arterial roads apparently, and alleviates traffic congestion. In the data analysis the arterial saturation decreases from 1.100 to 0.996, which verifies the micro-circulation transportation's function of urban branch road network.展开更多
The automatic algorithm programming model can increase the dependability and efficiency of algorithm program development,including specification generation,program refinement,and formal verification.However,the existi...The automatic algorithm programming model can increase the dependability and efficiency of algorithm program development,including specification generation,program refinement,and formal verification.However,the existing model has two flaws:incompleteness of program refinement and inadequate automation of formal verification.This paper proposes an automatic algorithm programming model based on the improved Morgan’s refinement calculus.It extends the Morgan’s refinement calculus rules and designs the C++generation system for realizing the complete process of refinement.Meanwhile,the automation tools VCG(Verification Condition Generator)and Isabelle are used to improve the automation of formal verification.An example of a stock’s maximum income demonstrates the effectiveness of the proposed model.Furthermore,the proposed model has some relevance for automatic software generation.展开更多
The prediction model for mechanical properties of RAC was established through the Bayesian optimization-based Gaussian process regression(BO-GPR)method,where the input variables in BO-GPR model depend on the mix ratio...The prediction model for mechanical properties of RAC was established through the Bayesian optimization-based Gaussian process regression(BO-GPR)method,where the input variables in BO-GPR model depend on the mix ratio of concrete.Then the compressive strength prediction model,the material cost,and environmental factors were simultaneously considered as objectives,while a multi-objective gray wolf optimization algorithm was developed for finding the optimal mix ratio.A total of 730 RAC datasets were used for training and testing the predication model,while the optimal design method for mix ratio was verified through RAC experiments.The experimental results show that the predicted,testing,and expected compressive strengths are nearly consistent,illustrating the effectiveness of the proposed method.展开更多
基金Education and Teaching Research Project of Beijing University of Technology(ER2024KCB08)。
文摘With the widespread application of large language models(LLMs)in natural language processing and code generation,traditional High-Level Language Programming courses are facing unprecedented challenges and opportunities.As a core programming language for computer science majors,C language remains irreplaceable due to its foundational nature and engineering adaptability.This paper,based on the rapid development of large model technologies,proposes a systematic reform design for C language teaching,focusing on teaching objectives,content structure,teaching methods,and evaluation systems.The article suggests a teaching framework centered on“human-computer collaborative programming,”integrating prompt training,AI-assisted debugging,and code generation analysis,aiming to enhance students’problem modeling ability,programming expression skills,and AI collaboration literacy.
基金supported by the National Natural Science Foundation of China(No.92371206)the Postgraduate Scientific Research Innovation Project of Hunan Province,China(No.CX2023063).
文摘Satellite Component Layout Optimization(SCLO) is crucial in satellite system design.This paper proposes a novel Satellite Three-Dimensional Component Assignment and Layout Optimization(3D-SCALO) problem tailored to engineering requirements, aiming to optimize satellite heat dissipation while considering constraints on static stability, 3D geometric relationships between components, and special component positions. The 3D-SCALO problem is a challenging bilevel combinatorial optimization task, involving the optimization of discrete component assignment variables in the outer layer and continuous component position variables in the inner layer,with both influencing each other. To address this issue, first, a Mixed Integer Programming(MIP) model is proposed, which reformulates the original bilevel problem into a single-level optimization problem, enabling the exploration of a more comprehensive optimization space while avoiding iterative nested optimization. Then, to model the 3D geometric relationships between components within the MIP framework, a linearized 3D Phi-function method is proposed, which handles non-overlapping and safety distance constraints between cuboid components in an explicit and effective way. Subsequently, the Finite-Rectangle Method(FRM) is proposed to manage 3D geometric constraints for complex-shaped components by approximating them with a finite set of cuboids, extending the applicability of the geometric modeling approach. Finally, the feasibility and effectiveness of the proposed MIP model are demonstrated through two numerical examples"and a real-world engineering case, which confirms its suitability for complex-shaped components and real engineering applications.
基金supported by the National Science Foundation for Distinguished Young Scholars of China(No.52425212)National Key Research and Development Program of China(No.2021YFA0717100)National Natural Science Foundation of China(Nos.12072270,U2013206,and 52442214).
文摘To establish the optimal reference trajectory for a near-space vehicle under free terminal time,a time-optimal model predictive static programming method is proposed with adaptive fish swarm optimization.First,the model predictive static programming method is developed by incorporating neighboring terms and trust region,enabling rapid generation of precise optimal solutions.Next,an adaptive fish swarm optimization technique is employed to identify a sub-optimal solution,while a momentum gradient descent method with learning rate decay ensures the convergence to the global optimal solution.To validate the feasibility and accuracy of the proposed method,a near-space vehicle example is analyzed and simulated during its glide phase.The simulation results demonstrate that the proposed method aligns with theoretical derivations and outperforms existing methods in terms of convergence speed and accuracy.Therefore,the proposed method offers significant practical value for solving the fast trajectory optimization problem in near-space vehicle applications.
基金supported by the National Science Fund for Distinguished Young Scholars (62225303)the Fundamental Research Funds for the Central Universities (buctrc202201)+1 种基金China Scholarship Council,and High Performance Computing PlatformCollege of Information Science and Technology,Beijing University of Chemical Technology。
文摘In order to address the output feedback issue for linear discrete-time systems, this work suggests a brand-new adaptive dynamic programming(ADP) technique based on the internal model principle(IMP). The proposed method, termed as IMP-ADP, does not require complete state feedback-merely the measurement of input and output data. More specifically, based on the IMP, the output control problem can first be converted into a stabilization problem. We then design an observer to reproduce the full state of the system by measuring the inputs and outputs. Moreover, this technique includes both a policy iteration algorithm and a value iteration algorithm to determine the optimal feedback gain without using a dynamic system model. It is important that with this concept one does not need to solve the regulator equation. Finally, this control method was tested on an inverter system of grid-connected LCLs to demonstrate that the proposed method provides the desired performance in terms of both tracking and disturbance rejection.
基金supported by National Key Research and Development Program of China (2023YFB3307800)National Natural Science Foundation of China (Key Program: 62136003, 62373155)+1 种基金Major Science and Technology Project of Xinjiang (No. 2022A01006-4)the Fundamental Research Funds for the Central Universities。
文摘Hydrocracking is one of the most important petroleum refining processes that converts heavy oils into gases,naphtha,diesel,and other products through cracking reactions.Multi-objective optimization algorithms can help refining enterprises determine the optimal operating parameters to maximize product quality while ensuring product yield,or to increase product yield while reducing energy consumption.This paper presents a multi-objective optimization scheme for hydrocracking based on an improved SPEA2-PE algorithm,which combines path evolution operator and adaptive step strategy to accelerate the convergence speed and improve the computational accuracy of the algorithm.The reactor model used in this article is simulated based on a twenty-five lumped kinetic model.Through model and test function verification,the proposed optimization scheme exhibits significant advantages in the multiobjective optimization process of hydrocracking.
基金supported by the Basic Public Welfare Research Program of Zhejiang Province(No.LGN22E050005).
文摘This study proposes a multi-objective optimization framework for electric winches in fiber-reinforced plastic(FRP)fishing vessels to address critical limitations of conventional designs,including excessive weight,material inefficiency,and performance redundancy.By integrating surrogate modeling techniques with a multi-objective genetic algorithm(MOGA),we have developed a systematic approach that encompasses parametric modeling,finite element analysis under extreme operational conditions,and multi-fidelity performance evaluation.Through a 10-t electric winch case study,the methodology’s effectiveness is demonstrated via parametric characterization of structural integrity,stiffness behavior,and mass distribution.The comparative analysis identified optimal surrogate models for predicting key performance metrics,which enabled the construction of a robust multi-objective optimization model.The MOGA-derived Pareto solutions produced a design configuration achieving 7.86%mass reduction,2.01%safety factor improvement,and 23.97%deformation mitigation.Verification analysis confirmed the optimization scheme’s reliability in balancing conflicting design requirements.This research establishes a generalized framework for marine deck machinery modernization,particularly addressing the structural compatibility challenges in FRP vessel retrofitting.The proposed methodology demonstrates significant potential for facilitating sustainable upgrades of fishing vessel equipment through systematic performance optimization.
基金partially supported by the National Natural Science Foundation of China(42177164,52474121)the Outstanding Youth Project of Hunan Provincial Department of Education(23B0008).
文摘Given the growing concern over global warming and the critical role of carbon dioxide(CO_(2))in this phenomenon,the study of CO_(2)-induced alterations in coal strength has garnered significant attention due to its implications for carbon sequestration.A large number of experiments have proved that CO_(2) interaction time(T),saturation pressure(P)and other parameters have significant effects on coal strength.However,accurate evaluation of CO_(2)-induced alterations in coal strength is still a difficult problem,so it is particularly important to establish accurate and efficient prediction models.This study explored the application of advancedmachine learning(ML)algorithms and Gene Expression Programming(GEP)techniques to predict CO_(2)-induced alterations in coal strength.Sixmodels were developed,including three metaheuristic-optimized XGBoost models(GWO-XGBoost,SSA-XGBoost,PO-XGBoost)and three GEP models(GEP-1,GEP-2,GEP-3).Comprehensive evaluations using multiple metrics revealed that all models demonstrated high predictive accuracy,with the SSA-XGBoost model achieving the best performance(R2—Coefficient of determination=0.99396,RMSE—Root Mean Square Error=0.62102,MAE—Mean Absolute Error=0.36164,MAPE—Mean Absolute Percentage Error=4.8101%,RPD—Residual Predictive Deviation=13.4741).Model interpretability analyses using SHAP(Shapley Additive exPlanations),ICE(Individual Conditional Expectation),and PDP(Partial Dependence Plot)techniques highlighted the dominant role of fixed carbon content(FC)and significant interactions between FC and CO_(2) saturation pressure(P).Theresults demonstrated that the proposedmodels effectively address the challenges of CO_(2)-induced strength prediction,providing valuable insights for geological storage safety and environmental applications.
基金supported by the Public Welfare Industry Special Fund Project of the Ministry of Water Resources of China (Grant No. 200701028)the Humanities and Social Science Foundation Program of Hohai University (Grant No. 2008421411)
文摘The principal-subordinate hierarchical multi-objective programming model of initial water rights allocation was developed based on the principle of coordinated and sustainable development of different regions and water sectors within a basin. With the precondition of strictly controlling maximum emissions rights, initial water rights were allocated between the first and the second levels of the hierarchy in order to promote fair and coordinated development across different regions of the basin and coordinated and efficient water use across different water sectors, realize the maximum comprehensive benefits to the basin, promote the unity of quantity and quality of initial water rights allocation, and eliminate water conflict across different regions and water sectors. According to interactive decision-making theory, a principal-subordinate hierarchical interactive iterative algorithm based on the satisfaction degree was developed and used to solve the initial water rights allocation model. A case study verified the validity of the model.
文摘According to Hainan Island's biological characteristics, and existing structure of productivity of tropical crops and local climatic conditions, this paper carries on regional division of tropical crops by fuzzy mathematics. Based on calculation of basic parameters for tl1e formation of production, near-tem optimum models of tropical crops structure of each region was established by means of multi-objective programming, and a far-term grey programming model was set up through the above-mentioned near-term model and prediction of future parameters. Conclusion shows that the near-term programming may raise the profit by 5. 1-55.7 percent and far-tem programming by 54-90 percent, both gainingobvious economic benefits.
基金supported by the National Natural Science Foundation of China (Nos. 71273277/71373285/71303258)the Philosophy and Social Sciences Major Research Project of the Ministry of Education (No. 11JZD048)
文摘An important decision for policy makers is selecting strategic petroleum reserve sites. However, policy makers may not choose the most suitable and efficient locations for strategic petroleum reserve（SPR） due to the complexity in the choice of sites. This paper proposes a multi-objective programming model to determine the optimal locations for China＇s SPR storage sites. This model considers not only the minimum response time but also the minimum transportation cost based on a series of reasonable assumptions and constraint conditions. The factors influencing SPR sites are identified to determine potential demand points and candidate storage sites. Estimation and suggestions are made for the selection of China＇s future SPR storage sites based on the results of this model. When the number of petroleum storage sites is less than or equals 25 and the maximum capacity of storage sites is restricted to 10 million tonnes, the model＇s result best fit for the current layout scheme selected thirteen storage sites in four scenarios. Considering the current status of SPR in China,Tianjin, Qingdao, Dalian, Daqing and Zhanjiang, Chengdu,Xi＇an, and Yueyang are suggested to be the candidate locations for the third phase of the construction plan. The locations of petroleum storage sites suggested in this work could be used as a reference for decision makers.
文摘The current portfolio model for property-liability insurance company is only single period that can not meet the practical demands of portfolio management, and the purpose of this paper is to develop a multiperiod model for its portfolio problem. The model is a multistage stochastic programming which considers transaction costs, cash flow between time periods, and the matching of asset and liability; it does not depend on the assumption for normality of return distribution. Additionally, an investment constraint is added. The numerical example manifests that the multiperiod model can more effectively assist the property-liability insurer to determine the optimal composition of insurance and investment portfolio and outperforms the single period one.
基金financially supported by the National Natural Science Foundation of China(Grant No.52371261)the Science and Technology Projects of Liaoning Province(Grant No.2023011352-JH1/110).
文摘This study delineates the development of the optimization framework for the preliminary design phase of Floating Offshore Wind Turbines(FOWTs),and the central challenge addressed is the optimization of the FOWT platform dimensional parameters in relation to motion responses.Although the three-dimensional potential flow(TDPF)panel method is recognized for its precision in calculating FOWT motion responses,its computational intensity necessitates an alternative approach for efficiency.Herein,a novel application of varying fidelity frequency-domain computational strategies is introduced,which synthesizes the strip theory with the TDPF panel method to strike a balance between computational speed and accuracy.The Co-Kriging algorithm is employed to forge a surrogate model that amalgamates these computational strategies.Optimization objectives are centered on the platform’s motion response in heave and pitch directions under general sea conditions.The steel usage,the range of design variables,and geometric considerations are optimization constraints.The angle of the pontoons,the number of columns,the radius of the central column and the parameters of the mooring lines are optimization constants.This informed the structuring of a multi-objective optimization model utilizing the Non-dominated Sorting Genetic Algorithm Ⅱ(NSGA-Ⅱ)algorithm.For the case of the IEA UMaine VolturnUS-S Reference Platform,Pareto fronts are discerned based on the above framework and delineate the relationship between competing motion response objectives.The efficacy of final designs is substantiated through the time-domain calculation model,which ensures that the motion responses in extreme sea conditions are superior to those of the initial design.
基金supported by the National Natural Science Foundationof China (70771080)the National Science Foundation of Hubei Province(20091107)Hubei Province Key Laboratory of Systems Science in Metallurgical Process (B201003)
文摘Partial cooperation models are studied for many years to solve the bilevel programming problems where the follower’s optimal reaction is not unique. However, in these existed models, the follower’s cooperation level does not depend on the leader’s decision. A new model is proposed to solve this deficiency. It is proved the feasibility of the new model when the reaction set of the lower level is lower semicontinuous. And the numerical results show that the new model has optimal solutions when the reaction set of the lower level is discrete, lower semi-continuous and non-lower semi-continuous.
文摘Estimation of the rock mass modulus of deformation(Em)is one of the most important design parameters in designing many structures in and on rock.This parameter can be obtained by in situ tests,empirical relations between deformation modulus and rock mass classifcation,and estimating from laboratory tests results.In this paper,a back analysis calculation is performed to present an equation for estimation of the rock mass modulus of deformation using genetic programming(GP)and numerical modeling.A database of 40,960 datasets,including vertical stress(rz),horizontal to vertical stresses ratio(k),Poisson’s ratio(m),radius of circular tunnel(r)and wall displacement of circular tunnel on the horizontal diameter(d)for input parameters and modulus of deformation for output,was established.The selected parameters are easy to determine and rock mass modulus of deformation can be obtained from instrumentation data of any size circular galleries.The resulting RMSE of 0.86 and correlation coeffcient of97%of the proposed equation demonstrated the capability of the computer program(CP)generated by GP.
文摘The developments of modern mathematics and computer science make artificial neural networks become most useful tools in wide range of fields. Modeling methods of artificial neural networks are described in this paper. The programming technique by using Matlab neural networks toolbox is discussed. The application in Material Hot Working of neural networks is also introduced.
基金supported by the National Basic Research Program of China(2010CB951002)the Dr.Western-funded Project of Chinese Academy of Science(XBBS201010 and XBBS201005)+1 种基金the National Natural Sciences Foundation of China (51190095)the Open Research Fund Program of State Key Laboratory of Hydro-science and Engineering(sklhse-2012-A03)
文摘This study presented a simulation-based two-stage interval-stochastic programming （STIP） model to support water resources management in the Kaidu-Konqi watershed in Northwest China. The modeling system coupled a distributed hydrological model with an interval two-stage stochastic programing （ITSP）. The distributed hydrological model was used for establishing a rainfall-runoff forecast system, while random parameters were pro- vided by the statistical analysis of simulation outcomes water resources management planning in Kaidu-Konqi The developed STIP model was applied to a real case of watershed, where three scenarios with different water re- sources management policies were analyzed. The results indicated that water shortage mainly occurred in agri- culture, ecology and forestry sectors. In comparison, the water demand from municipality, industry and stock- breeding sectors can be satisfied due to their lower consumptions and higher economic values. Different policies for ecological water allocation can result in varied system benefits, and can help to identify desired water allocation plans with a maximum economic benefit and a minimum risk of system disruption under uncertainty.
基金National Defense Advanced Research Foundation of China
文摘A novel Parsimonious Genetic Programming （PGP） algorithm together with a novel aero-engine optimum data-driven dynamic start process model based on PGP is proposed. In application of this method, first, the traditional Genetic Programming（GP） is used to generate the nonlinear input-output models that are represented in a binary tree structure; then, the Orthogonal Least Squares algorithm （OLS） is used to estimate the contribution of the branches of the tree （refer to basic function term that cannot be decomposed anymore according to special rule） to the accuracy of the model, which contributes to eliminate complex redundant subtrees and enhance GP＇s convergence speed; and finally, a simple, reliable and exact linear-in-parameter nonlinear model via GP evolution is obtained. The real aero-engine start process test data simulation and the comparisons with Support Vector Machines （SVM） validate that the proposed method can generate more applicable, interpretable models and achieve comparable, even superior results to SVM.
基金Project(2006CB705507) supported by the National Basic Research and Development Program of ChinaProject(20060533036) supported by the Specialized Research Foundation for the Doctoral Program of Higher Education of China
文摘Considering the decision-making variables of the capacities of branch roads and the optimization targets of lowering the saturation of arterial roads and the reconstruction expense of branch roads, the bi-level programming model for reconstructing the branch roads was set up. The upper level model was for determining the enlarged capacities of the branch roads, and the lower level model was for calculating the flows of road sections via the user equilibrium traffic assignment method. The genetic algorithm for solving the bi-level model was designed to obtain the reconstruction capacities of the branch roads. The results show that by the bi-level model and its algorithm, the optimum scheme of urban branch roads reconstruction can be gained, which reduces the saturation of arterial roads apparently, and alleviates traffic congestion. In the data analysis the arterial saturation decreases from 1.100 to 0.996, which verifies the micro-circulation transportation's function of urban branch road network.
基金Supported by the National Natural Science Foundation of China(61862033,61902162)Key Project of Science and Technology Research of Department of Education of Jiangxi Province(GJJ210307)Postgraduate Innovation Fund Project of Education Department of Jiangxi Province(YC2021-S306)。
文摘The automatic algorithm programming model can increase the dependability and efficiency of algorithm program development,including specification generation,program refinement,and formal verification.However,the existing model has two flaws:incompleteness of program refinement and inadequate automation of formal verification.This paper proposes an automatic algorithm programming model based on the improved Morgan’s refinement calculus.It extends the Morgan’s refinement calculus rules and designs the C++generation system for realizing the complete process of refinement.Meanwhile,the automation tools VCG(Verification Condition Generator)and Isabelle are used to improve the automation of formal verification.An example of a stock’s maximum income demonstrates the effectiveness of the proposed model.Furthermore,the proposed model has some relevance for automatic software generation.
基金Funded by the National Natural Science Foundation of China(No.51908183)the Natural Science Foundation of Hebei Province(No.E2023202101)。
文摘The prediction model for mechanical properties of RAC was established through the Bayesian optimization-based Gaussian process regression(BO-GPR)method,where the input variables in BO-GPR model depend on the mix ratio of concrete.Then the compressive strength prediction model,the material cost,and environmental factors were simultaneously considered as objectives,while a multi-objective gray wolf optimization algorithm was developed for finding the optimal mix ratio.A total of 730 RAC datasets were used for training and testing the predication model,while the optimal design method for mix ratio was verified through RAC experiments.The experimental results show that the predicted,testing,and expected compressive strengths are nearly consistent,illustrating the effectiveness of the proposed method.
