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
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
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
Crop planting structure optimization is a signi ficant way to increase agricultural economic bene fits and improve agricultural water management. The complexities of fluctuating stream conditions, varying economic pro...Crop planting structure optimization is a signi ficant way to increase agricultural economic bene fits and improve agricultural water management. The complexities of fluctuating stream conditions, varying economic pro fits, and uncertainties and errors in estimated modeling parameters, as well as the complexities among economic, social, natural resources and environmental aspects, have led to the necessity of developing optimization models for crop planting structure which consider uncertainty and multi-objectives elements. In this study,three single-objective programming models under uncertainty for crop planting structure optimization were developed, including an interval linear programming model, an inexact fuzzy chance-constrained programming(IFCCP) model and an inexact fuzzy linear programming(IFLP) model. Each of the three models takes grayness into account. Moreover, the IFCCP model considers fuzzy uncertainty of parameters/variables and stochastic characteristics of constraints, while the IFLP model takes into account the fuzzy uncertainty of both constraints and objective functions. To satisfy the sustainable development of crop planting structure planning, a fuzzy-optimizationtheory-based fuzzy linear multi-objective programming model was developed, which is capable of re flecting both uncertainties and multi-objective. In addition, a multiobjective fractional programming model for crop structure optimization was also developed to quantitatively express the multi-objective in one optimization model with the numerator representing maximum economic bene fits and the denominator representing minimum crop planting area allocation. These models better re flect actual situations,considering the uncertainties and multi-objectives of crop planting structure optimization systems. The five models developed were then applied to a real case study in MinqinCounty, north-west China. The advantages, the applicable conditions and the solution methods of each model are expounded. Detailed analysis of results of each model and their comparisons demonstrate the feasibility and applicability of the models developed, therefore decision makers can choose the appropriate model when making decisions.展开更多
This paper introduces the Surrogate-assisted Multi-objective Grey Wolf Optimizer(SMOGWO)as a novel methodology for addressing the complex problem of empty-heavy train allocation,with a focus on line utilization balanc...This paper introduces the Surrogate-assisted Multi-objective Grey Wolf Optimizer(SMOGWO)as a novel methodology for addressing the complex problem of empty-heavy train allocation,with a focus on line utilization balance.By integrating surrogate models to approximate the objective functions,SMOGWO significantly improves the efficiency and accuracy of the optimization process.The effectiveness of this approach is evaluated using the CEC2009 multi-objective test function suite,where SMOGWO achieves a superiority rate of 76.67%compared to other leading multi-objective algorithms.Furthermore,the practical applicability of SMOGWO is demonstrated through a case study on empty and heavy train allocation,which validates its ability to balance line capacity,minimize transportation costs,and optimize the technical combination of heavy trains.The research highlights SMOGWO's potential as a robust solution for optimization challenges in railway transportation,offering valuable contributions toward enhancing operational efficiency and promoting sustainable development in the sector.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
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
Multi-instance image generation remains a challenging task in the field of computer vision.While existing diffusionmodels demonstrate impressive fidelity in image generation,they often struggle with precisely controll...Multi-instance image generation remains a challenging task in the field of computer vision.While existing diffusionmodels demonstrate impressive fidelity in image generation,they often struggle with precisely controlling each object’s shape,pose,and size.Methods like layout-to-image and mask-to-image provide spatial guidance but frequently suffer from object shape distortion,overlaps,and poor consistency,particularly in complex scenes with multiple objects.To address these issues,we introduce PolyDiffusion,a contour-based diffusion framework that encodes each object’s contour as a boundary-coordinate sequence,decoupling object shapes and positions.This approach allows for better control over object geometry and spatial positioning,which is critical for achieving high-quality multiinstance generation.We formulate the training process as a multi-objective optimization problem,balancing three key objectives:a denoising diffusion loss to maintain overall image fidelity,a cross-attention contour alignment loss to ensure precise shape adherence,and a reward-guided denoising objective that minimizes the Fréchet distance to real images.In addition,the Object Space-Aware Attention module fuses contour tokens with visual features,while a prior-guided fusion mechanism utilizes inter-object spatial relationships and class semantics to enhance consistency across multiple objects.Experimental results on benchmark datasets such as COCO-Stuff and VOC-2012 demonstrate that PolyDiffusion significantly outperforms existing layout-to-image and mask-to-image methods,achieving notable improvements in both image quality and instance-level segmentation accuracy.The implementation of Poly Diffusion is available at https://github.com/YYYYYJS/PolyDiffusion(accessed on 06 August 2025).展开更多
This paper presents an improved virtual coupling train set(VCTS)operation control framework to deal with the lack of opti-mization of speed curves in the traditional techniques.The framework takes into account the tem...This paper presents an improved virtual coupling train set(VCTS)operation control framework to deal with the lack of opti-mization of speed curves in the traditional techniques.The framework takes into account the temporary speed limit on the railway line and the communication delay between trains,and it uses a VCTS consisting of three trains as an experimental object.It creates the virtual coupling train tracking and control process by improving the driving strategy of the leader train and using the leader-follower model.The follower train uses the improved speed curve of the leader train as its speed refer-ence curve through knowledge migration,and this completes the multi-objective optimization of the driving strategy for the VCTS.The experimental results confirm that the deep reinforcement learning algorithm effectively achieves the optimization goal of the train driving strategy.They also reveal that the intrinsic curiosity module prioritized experience replay dueling double deep Q-network(ICM-PER-D3QN)algorithm outperforms the deep Q-network(DQN)algorithm in optimizing the driving strategy of the leader train.The ICM-PER-D3QN algorithm enhances the leader train driving strategy by an average of 57%when compared to the DQN algorithm.Furthermore,the particle swarm optimization(PSO)-based model predictive control(MPC)algorithm has also demonstrated tracking accuracy and further improved safety during VCTS operation,with an average increase of 37.7%in tracking accuracy compared to the traditional MPC algorithm.展开更多
Purpose-With the rapid advancement of China’s high-speed rail network,the density of train operations is on the rise.To address the challenge of shortening train tracking intervals while enhancing transportation effi...Purpose-With the rapid advancement of China’s high-speed rail network,the density of train operations is on the rise.To address the challenge of shortening train tracking intervals while enhancing transportation efficiency,the multi-objective dynamic optimization of the train operation process has emerged as a critical issue.Design/methodology/approach-Train dynamic model is established by analyzing the force of the train in the process of tracing operation.The train tracing operation model is established according to the dynamic mechanical model of the train tracking process,and the dynamic optimization analysis is carried out with comfort,energy saving and punctuality as optimization objectives.To achieve multi-objective dynamic optimization,a novel train tracking operation calculation method is proposed,utilizing the improved grey wolf optimization algorithm(MOGWO).The proposed method is simulated and verified based on the train characteristics and line data of CR400AF electric multiple units.Findings-The simulation results prove that the optimized MOGWO algorithm can be computed quickly during train tracks,the optimum results can be given within 5s and the algorithm can converge effectively in different optimization target directions.The optimized speed profile of the MOGWO algorithm is smoother and more stable and meets the target requirements of energy saving,punctuality and comfort while maximally respecting the speed limit profile.Originality/value-The MOGWO train tracking interval optimization method enhances the tracking process while ensuring a safe tracking interval.This approach enables the trailing train to operate more comfortably,energy-efficiently and punctually,aligning with passenger needs and industry trends.The method offers valuable insights for optimizing the high-speed train tracking process.展开更多
The intermittency and volatility of wind and photovoltaic power generation exacerbate issues such as wind and solar curtailment,hindering the efficient utilization of renewable energy and the low-carbon development of...The intermittency and volatility of wind and photovoltaic power generation exacerbate issues such as wind and solar curtailment,hindering the efficient utilization of renewable energy and the low-carbon development of energy systems.To enhance the consumption capacity of green power,the green power system consumption optimization scheduling model(GPS-COSM)is proposed,which comprehensively integrates green power system,electric boiler,combined heat and power unit,thermal energy storage,and electrical energy storage.The optimization objectives are to minimize operating cost,minimize carbon emission,and maximize the consumption of wind and solar curtailment.The multi-objective particle swarm optimization algorithm is employed to solve the model,and a fuzzy membership function is introduced to evaluate the satisfaction level of the Pareto optimal solution set,thereby selecting the optimal compromise solution to achieve a dynamic balance among economic efficiency,environmental friendliness,and energy utilization efficiency.Three typical operating modes are designed for comparative analysis.The results demonstrate that the mode involving the coordinated operation of electric boiler,thermal energy storage,and electrical energy storage performs the best in terms of economic efficiency,environmental friendliness,and renewable energy utilization efficiency,achieving the wind and solar curtailment consumption rate of 99.58%.The application of electric boiler significantly enhances the direct accommodation capacity of the green power system.Thermal energy storage optimizes intertemporal regulation,while electrical energy storage strengthens the system’s dynamic regulation capability.The coordinated optimization of multiple devices significantly reduces reliance on fossil fuels.展开更多
To improve the efficiency of ship traffic in frequently traded sea areas and respond to the national“dual-carbon”strategy,a multi-objective ship route induction model is proposed.Considering the energy-saving and en...To improve the efficiency of ship traffic in frequently traded sea areas and respond to the national“dual-carbon”strategy,a multi-objective ship route induction model is proposed.Considering the energy-saving and environmental issues of ships,this study aims to improve the transportation efficiency of ships by providing a ship route induction method.Ship data from a certain bay during a defined period are collected,and an improved backpropagation neural network algorithm is used to forecast ship traffic.On the basis of the forecasted data and ship route induction objectives,dynamic programming of ship routes is performed.Experimental results show that the routes planned using this induction method reduce the combined cost by 17.55%compared with statically induced routes.This method has promising engineering applications in improving ship navigation efficiency,promoting energy conservation,and reducing emissions.展开更多
This work addresses the cut order planning(COP)problem for multi-color garment production,which is the first step in the clothing industry.First,a multi-objective optimization model of multicolor COP(MCOP)is establish...This work addresses the cut order planning(COP)problem for multi-color garment production,which is the first step in the clothing industry.First,a multi-objective optimization model of multicolor COP(MCOP)is established with production error and production cost as optimization objectives,combined with constraints such as the number of equipment and the number of layers.Second,a decoupled multi-objective optimization algorithm(DMOA)is proposed based on the linear programming decoupling strategy and non-dominated sorting in genetic algorithmsⅡ(NSGAII).The size-combination matrix and the fabric-layer matrix are decoupled to improve the accuracy of the algorithm.Meanwhile,an improved NSGAII algorithm is designed to obtain the optimal Pareto solution to the MCOP problem,thereby constructing a practical intelligent production optimization algorithm.Finally,the effectiveness and superiority of the proposed DMOA are verified through practical cases and comparative experiments,which can effectively optimize the production process for garment enterprises.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 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.
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founded by the Doctoral Programs Foundation of the Ministry of Education of China (20130008110021)the National Natural Science Foundation of China (91425302, 41271536)International Science and Technology Cooperation Program of China (2013DFG70990)
文摘Crop planting structure optimization is a signi ficant way to increase agricultural economic bene fits and improve agricultural water management. The complexities of fluctuating stream conditions, varying economic pro fits, and uncertainties and errors in estimated modeling parameters, as well as the complexities among economic, social, natural resources and environmental aspects, have led to the necessity of developing optimization models for crop planting structure which consider uncertainty and multi-objectives elements. In this study,three single-objective programming models under uncertainty for crop planting structure optimization were developed, including an interval linear programming model, an inexact fuzzy chance-constrained programming(IFCCP) model and an inexact fuzzy linear programming(IFLP) model. Each of the three models takes grayness into account. Moreover, the IFCCP model considers fuzzy uncertainty of parameters/variables and stochastic characteristics of constraints, while the IFLP model takes into account the fuzzy uncertainty of both constraints and objective functions. To satisfy the sustainable development of crop planting structure planning, a fuzzy-optimizationtheory-based fuzzy linear multi-objective programming model was developed, which is capable of re flecting both uncertainties and multi-objective. In addition, a multiobjective fractional programming model for crop structure optimization was also developed to quantitatively express the multi-objective in one optimization model with the numerator representing maximum economic bene fits and the denominator representing minimum crop planting area allocation. These models better re flect actual situations,considering the uncertainties and multi-objectives of crop planting structure optimization systems. The five models developed were then applied to a real case study in MinqinCounty, north-west China. The advantages, the applicable conditions and the solution methods of each model are expounded. Detailed analysis of results of each model and their comparisons demonstrate the feasibility and applicability of the models developed, therefore decision makers can choose the appropriate model when making decisions.
基金supported by the National Natural Science Foundation of China(Project No.5217232152102391)+2 种基金Sichuan Province Science and Technology Innovation Talent Project(2024JDRC0020)China Shenhua Energy Company Limited Technology Project(GJNY-22-7/2300-K1220053)Key science and technology projects in the transportation industry of the Ministry of Transport(2022-ZD7-132).
文摘This paper introduces the Surrogate-assisted Multi-objective Grey Wolf Optimizer(SMOGWO)as a novel methodology for addressing the complex problem of empty-heavy train allocation,with a focus on line utilization balance.By integrating surrogate models to approximate the objective functions,SMOGWO significantly improves the efficiency and accuracy of the optimization process.The effectiveness of this approach is evaluated using the CEC2009 multi-objective test function suite,where SMOGWO achieves a superiority rate of 76.67%compared to other leading multi-objective algorithms.Furthermore,the practical applicability of SMOGWO is demonstrated through a case study on empty and heavy train allocation,which validates its ability to balance line capacity,minimize transportation costs,and optimize the technical combination of heavy trains.The research highlights SMOGWO's potential as a robust solution for optimization challenges in railway transportation,offering valuable contributions toward enhancing operational efficiency and promoting sustainable development in the sector.
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 in part by the Scientific Research Fund of National Natural Science Foundation of China(Grant No.62372168)the Hunan Provincial Natural Science Foundation of China(Grant No.2023JJ30266)+2 种基金the Research Project on teaching reform in Hunan province(No.HNJG-2022-0791)the Hunan University of Science and Technology(No.2022-44-8)the National Social Science Funds of China(19BZX044).
文摘Multi-instance image generation remains a challenging task in the field of computer vision.While existing diffusionmodels demonstrate impressive fidelity in image generation,they often struggle with precisely controlling each object’s shape,pose,and size.Methods like layout-to-image and mask-to-image provide spatial guidance but frequently suffer from object shape distortion,overlaps,and poor consistency,particularly in complex scenes with multiple objects.To address these issues,we introduce PolyDiffusion,a contour-based diffusion framework that encodes each object’s contour as a boundary-coordinate sequence,decoupling object shapes and positions.This approach allows for better control over object geometry and spatial positioning,which is critical for achieving high-quality multiinstance generation.We formulate the training process as a multi-objective optimization problem,balancing three key objectives:a denoising diffusion loss to maintain overall image fidelity,a cross-attention contour alignment loss to ensure precise shape adherence,and a reward-guided denoising objective that minimizes the Fréchet distance to real images.In addition,the Object Space-Aware Attention module fuses contour tokens with visual features,while a prior-guided fusion mechanism utilizes inter-object spatial relationships and class semantics to enhance consistency across multiple objects.Experimental results on benchmark datasets such as COCO-Stuff and VOC-2012 demonstrate that PolyDiffusion significantly outperforms existing layout-to-image and mask-to-image methods,achieving notable improvements in both image quality and instance-level segmentation accuracy.The implementation of Poly Diffusion is available at https://github.com/YYYYYJS/PolyDiffusion(accessed on 06 August 2025).
基金supported by the National Natural Science Foundation of China under Grant 52162050.
文摘This paper presents an improved virtual coupling train set(VCTS)operation control framework to deal with the lack of opti-mization of speed curves in the traditional techniques.The framework takes into account the temporary speed limit on the railway line and the communication delay between trains,and it uses a VCTS consisting of three trains as an experimental object.It creates the virtual coupling train tracking and control process by improving the driving strategy of the leader train and using the leader-follower model.The follower train uses the improved speed curve of the leader train as its speed refer-ence curve through knowledge migration,and this completes the multi-objective optimization of the driving strategy for the VCTS.The experimental results confirm that the deep reinforcement learning algorithm effectively achieves the optimization goal of the train driving strategy.They also reveal that the intrinsic curiosity module prioritized experience replay dueling double deep Q-network(ICM-PER-D3QN)algorithm outperforms the deep Q-network(DQN)algorithm in optimizing the driving strategy of the leader train.The ICM-PER-D3QN algorithm enhances the leader train driving strategy by an average of 57%when compared to the DQN algorithm.Furthermore,the particle swarm optimization(PSO)-based model predictive control(MPC)algorithm has also demonstrated tracking accuracy and further improved safety during VCTS operation,with an average increase of 37.7%in tracking accuracy compared to the traditional MPC algorithm.
基金funded by the China Academy of Railway Sciences Corporation Limited Scientific Research Project(No:2023YJ080).
文摘Purpose-With the rapid advancement of China’s high-speed rail network,the density of train operations is on the rise.To address the challenge of shortening train tracking intervals while enhancing transportation efficiency,the multi-objective dynamic optimization of the train operation process has emerged as a critical issue.Design/methodology/approach-Train dynamic model is established by analyzing the force of the train in the process of tracing operation.The train tracing operation model is established according to the dynamic mechanical model of the train tracking process,and the dynamic optimization analysis is carried out with comfort,energy saving and punctuality as optimization objectives.To achieve multi-objective dynamic optimization,a novel train tracking operation calculation method is proposed,utilizing the improved grey wolf optimization algorithm(MOGWO).The proposed method is simulated and verified based on the train characteristics and line data of CR400AF electric multiple units.Findings-The simulation results prove that the optimized MOGWO algorithm can be computed quickly during train tracks,the optimum results can be given within 5s and the algorithm can converge effectively in different optimization target directions.The optimized speed profile of the MOGWO algorithm is smoother and more stable and meets the target requirements of energy saving,punctuality and comfort while maximally respecting the speed limit profile.Originality/value-The MOGWO train tracking interval optimization method enhances the tracking process while ensuring a safe tracking interval.This approach enables the trailing train to operate more comfortably,energy-efficiently and punctually,aligning with passenger needs and industry trends.The method offers valuable insights for optimizing the high-speed train tracking process.
基金funded by the National Key Research and Development Program of China(2024YFE0106800)Natural Science Foundation of Shandong Province(ZR2021ME199).
文摘The intermittency and volatility of wind and photovoltaic power generation exacerbate issues such as wind and solar curtailment,hindering the efficient utilization of renewable energy and the low-carbon development of energy systems.To enhance the consumption capacity of green power,the green power system consumption optimization scheduling model(GPS-COSM)is proposed,which comprehensively integrates green power system,electric boiler,combined heat and power unit,thermal energy storage,and electrical energy storage.The optimization objectives are to minimize operating cost,minimize carbon emission,and maximize the consumption of wind and solar curtailment.The multi-objective particle swarm optimization algorithm is employed to solve the model,and a fuzzy membership function is introduced to evaluate the satisfaction level of the Pareto optimal solution set,thereby selecting the optimal compromise solution to achieve a dynamic balance among economic efficiency,environmental friendliness,and energy utilization efficiency.Three typical operating modes are designed for comparative analysis.The results demonstrate that the mode involving the coordinated operation of electric boiler,thermal energy storage,and electrical energy storage performs the best in terms of economic efficiency,environmental friendliness,and renewable energy utilization efficiency,achieving the wind and solar curtailment consumption rate of 99.58%.The application of electric boiler significantly enhances the direct accommodation capacity of the green power system.Thermal energy storage optimizes intertemporal regulation,while electrical energy storage strengthens the system’s dynamic regulation capability.The coordinated optimization of multiple devices significantly reduces reliance on fossil fuels.
基金Supported by the National Key R&D Program of China project (2017YFC0805309)the National Natural Science Foundation of China (60602020)。
文摘To improve the efficiency of ship traffic in frequently traded sea areas and respond to the national“dual-carbon”strategy,a multi-objective ship route induction model is proposed.Considering the energy-saving and environmental issues of ships,this study aims to improve the transportation efficiency of ships by providing a ship route induction method.Ship data from a certain bay during a defined period are collected,and an improved backpropagation neural network algorithm is used to forecast ship traffic.On the basis of the forecasted data and ship route induction objectives,dynamic programming of ship routes is performed.Experimental results show that the routes planned using this induction method reduce the combined cost by 17.55%compared with statically induced routes.This method has promising engineering applications in improving ship navigation efficiency,promoting energy conservation,and reducing emissions.
基金Supported by the Natural Science Foundation of Zhejiang Province(No.LQ22F030015).
文摘This work addresses the cut order planning(COP)problem for multi-color garment production,which is the first step in the clothing industry.First,a multi-objective optimization model of multicolor COP(MCOP)is established with production error and production cost as optimization objectives,combined with constraints such as the number of equipment and the number of layers.Second,a decoupled multi-objective optimization algorithm(DMOA)is proposed based on the linear programming decoupling strategy and non-dominated sorting in genetic algorithmsⅡ(NSGAII).The size-combination matrix and the fabric-layer matrix are decoupled to improve the accuracy of the algorithm.Meanwhile,an improved NSGAII algorithm is designed to obtain the optimal Pareto solution to the MCOP problem,thereby constructing a practical intelligent production optimization algorithm.Finally,the effectiveness and superiority of the proposed DMOA are verified through practical cases and comparative experiments,which can effectively optimize the production process for garment enterprises.
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.
