Traditional large-scale multi-objective optimization algorithms(LSMOEAs)encounter difficulties when dealing with sparse large-scale multi-objective optimization problems(SLM-OPs)where most decision variables are zero....Traditional large-scale multi-objective optimization algorithms(LSMOEAs)encounter difficulties when dealing with sparse large-scale multi-objective optimization problems(SLM-OPs)where most decision variables are zero.As a result,many algorithms use a two-layer encoding approach to optimize binary variable Mask and real variable Dec separately.Nevertheless,existing optimizers often focus on locating non-zero variable posi-tions to optimize the binary variables Mask.However,approxi-mating the sparse distribution of real Pareto optimal solutions does not necessarily mean that the objective function is optimized.In data mining,it is common to mine frequent itemsets appear-ing together in a dataset to reveal the correlation between data.Inspired by this,we propose a novel two-layer encoding learning swarm optimizer based on frequent itemsets(TELSO)to address these SLMOPs.TELSO mined the frequent terms of multiple particles with better target values to find mask combinations that can obtain better objective values for fast convergence.Experi-mental results on five real-world problems and eight benchmark sets demonstrate that TELSO outperforms existing state-of-the-art sparse large-scale multi-objective evolutionary algorithms(SLMOEAs)in terms of performance and convergence speed.展开更多
Steam power systems(SPSs)in industrial parks are the typical utility systems for heat and electricity supply.In SPSs,electricity is generated by steam turbines,and steam is generally produced and supplied at multiple ...Steam power systems(SPSs)in industrial parks are the typical utility systems for heat and electricity supply.In SPSs,electricity is generated by steam turbines,and steam is generally produced and supplied at multiple levels to serve the heat demands of consumers with different temperature grades,so that energy is utilized in cascade.While a large number of steam levels enhances energy utilization efficiency,it also tends to cause a complex steam pipeline network in the industrial park.In practice,a moderate number of steam levels is always adopted in SPSs,leading to temperature mismatches between heat supply and demand for some consumers.This study proposes a distributed steam turbine system(DSTS)consisting of main steam turbines on the energy supply side and auxiliary steam turbines on the energy consumption side,aiming to balance the heat production costs,the distance-related costs,and the electricity generation of SPSs in industrial parks.A mixed-integer nonlinear programming model is established for the optimization of SPSs,with the objective of minimizing the total annual cost(TAC).The optimal number of steam levels and the optimal configuration of DSTS for an industrial park can be determined by solving the model.A case study demonstrates that the TAC of the SPS is reduced by 220.6×10^(3)USD(2.21%)through the arrangement of auxiliary steam turbines.The sub-optimal number of steam levels and a non-optimal operating condition slightly increase the TAC by 0.46%and 0.28%,respectively.The sensitivity analysis indicates that the optimal number of steam levels tends to decrease from 3 to 2 as electricity price declines.展开更多
To properly describe and solve complex decision problems,research on theoretical properties and solution of mixed-integer quadratic programs is becoming very important.We establish in this paper different Lipschitz-ty...To properly describe and solve complex decision problems,research on theoretical properties and solution of mixed-integer quadratic programs is becoming very important.We establish in this paper different Lipschitz-type continuity results about the optimal value function and optimal solutions of mixed-integer parametric quadratic programs with parameters in the linear part of the objective function and in the right-hand sides of the linear constraints.The obtained results extend some existing results for continuous quadratic programs,and,more importantly,lay the foundation for further theoretical study and corresponding algorithm analysis on mixed-integer quadratic programs.展开更多
It is extremely challenging to solve the mixed-integer optimal control problems(MIOCPs)due to the complex computation in solving the integer decision variables.This paper presents a new method based on quantum anneali...It is extremely challenging to solve the mixed-integer optimal control problems(MIOCPs)due to the complex computation in solving the integer decision variables.This paper presents a new method based on quantum annealing(QA)to solve MIOCP.The QA is a metaheuristic which applies quantum tunneling in the annealing process.It has a faster convergence speed in optimal-searching and is less likely to run into local minima.Hence,QA is applied to deal with this kind of optimization problems.First,MIOCP is transformed into a mixed-integer nonlinear programming(MINLP).Then,a method based on QA is adopted to solve the MINLP and acquire the optimal solution.At last,two benchmark examples including Lotka-Volterra type fishing problem and distillation column are presented and solved.The effectiveness of the metliodology is verified by the acquired optimal schemes.展开更多
Refineries often need to find similar crude oil to replace the scarce crude oil for stabilizing the feedstock property. We introduced the method for calculation of crude blended properties firstly, and then created a ...Refineries often need to find similar crude oil to replace the scarce crude oil for stabilizing the feedstock property. We introduced the method for calculation of crude blended properties firstly, and then created a crude oil selection and blending optimization model based on the data of crude oil property. The model is a mixed-integer nonlinear programming(MINLP) with constraints, and the target is to maximize the similarity between the blended crude oil and the objective crude oil. Furthermore, the model takes into account the selection of crude oils and their blending ratios simultaneously, and transforms the problem of looking for similar crude oil into the crude oil selection and blending optimization problem. We applied the Improved Cuckoo Search(ICS) algorithm to solving the model. Through the simulations, ICS was compared with the genetic algorithm, the particle swarm optimization algorithm and the CPLEX solver. The results show that ICS has very good optimization efficiency. The blending solution can provide a reference for refineries to find the similar crude oil. And the method proposed can also give some references to selection and blending optimization of other materials.展开更多
The renewable portfolio standard has been promoted in parallel with the reform of the electricity market,and the flexibility requirement of the power system has rapidly increased.To promote renewable energy consumptio...The renewable portfolio standard has been promoted in parallel with the reform of the electricity market,and the flexibility requirement of the power system has rapidly increased.To promote renewable energy consumption and improve power system flexibility,a bi-level optimal operation model of the electricity market is proposed.A probabilistic model of the flexibility requirement is established,considering the correlation between wind power,photovoltaic power,and load.A bi-level optimization model is established for the multi-markets;the upper and lower models represent the intra-provincial market and inter-provincial market models,respectively.To efficiently solve the model,it is transformed into a mixed-integer linear programming model using the Karush–Kuhn–Tucker condition and Lagrangian duality theory.The economy and flexibility of the model are verified using a provincial power grid as an example.展开更多
It took that the weight minimum and drive efficiency maximal were as double optimizing target,the optimization model had built the drilling string,and the optimization solution was used of the ant colony algorithm to ...It took that the weight minimum and drive efficiency maximal were as double optimizing target,the optimization model had built the drilling string,and the optimization solution was used of the ant colony algorithm to find in progress.Adopted a two-layer search of the continuous space ant colony algorithm with overlapping or variation global ant search operation strategy and conjugated gradient partial ant search operation strat- egy.The experiment indicates that the spiral drill weight reduces 16.77% and transports the efficiency enhance 7.05% through the optimization design,the ant colony algorithm application on the spiral drill optimized design has provided the basis for the system re- search screw coal mine machine.展开更多
Multi-beam antenna and beam hopping technologies are an effective solution for scarce satellite frequency resources.One of the primary challenges accompanying with Multi-Beam Satellites(MBS)is an efficient Dynamic Res...Multi-beam antenna and beam hopping technologies are an effective solution for scarce satellite frequency resources.One of the primary challenges accompanying with Multi-Beam Satellites(MBS)is an efficient Dynamic Resource Allocation(DRA)strategy.This paper presents a learning-based Hybrid-Action Deep Q-Network(HADQN)algorithm to address the sequential decision-making optimization problem in DRA.By using a parameterized hybrid action space,HADQN makes it possible to schedule the beam pattern and allocate transmitter power more flexibly.To pursue multiple long-term QoS requirements,HADQN adopts a multi-objective optimization method to decrease system transmission delay,loss ratio of data packets and power consumption load simultaneously.Experimental results demonstrate that the proposed HADQN algorithm is feasible and greatly reduces in-orbit energy consumption without compromising QoS performance.展开更多
In order to explore the potential of profit margin improvement,a novel three-scale integrated optimization model of furnace simulation,cyclic scheduling,and supply chain of ethylene plants is proposed and evaluated.A ...In order to explore the potential of profit margin improvement,a novel three-scale integrated optimization model of furnace simulation,cyclic scheduling,and supply chain of ethylene plants is proposed and evaluated.A decoupling strategy is proposed for the solution of the three-scale model,which uses our previously proposed reactor scale model for operation optimization and then transfers the obtained results as a parameter table in the joint MILP optimization of plant-supply chain scale for cyclic scheduling.This optimization framework simplifies the fundamental mixed-integer nonlinear programming(MINLP)into several sub-models,and improves the interpretability and extendibility.In the evaluation of an industrial case,a profit increase at a percentage of 3.25%is attained in optimization compared to the practical operations.Further sensitivity analysis is carried out for strategy evolving study when price policy,supply chain,and production requirement parameters are varied.These results could provide useful suggestions for petrochemical enterprises on thermal cracking production.展开更多
The superstructure optimization of biomass to biomethane system through digestion is conducted in this work. The system encompasses biofeedstock collection and transportation, anaerobic digestion, biogas upgrading, an...The superstructure optimization of biomass to biomethane system through digestion is conducted in this work. The system encompasses biofeedstock collection and transportation, anaerobic digestion, biogas upgrading, and digestate recycling. We propose a multicriteria mixed integer nonlinear programming(MINLP) model that seeks to minimize the energy consumption and maximize the green degree and the biomethane production constrained by technology selection, mass balance, energy balance, and environmental impact. A multi-objective MINLP model is proposed and solved with a fast nondominated sorting genetic algorithm Ⅱ(NSGA-Ⅱ). The resulting Pareto-optimal surface reveals the trade-off among the conflicting objectives. The optimal results indicate quantitatively that higher green degree and biomethane production objectives can be obtained at the expense of destroying the performance of the energy consumption objective.展开更多
Radiation therapy plans are optimized as a single treatment plan, but delivered over 30 - 50 treatment sessions (known as fractions). This paper proposes a new mixed-integer linear programming model to simultaneously ...Radiation therapy plans are optimized as a single treatment plan, but delivered over 30 - 50 treatment sessions (known as fractions). This paper proposes a new mixed-integer linear programming model to simultaneously incorporate fractionation and cumulative constraints in Intensity Modulated Radiation Therapy (IMRT) planning optimization used in cancer treatment. The method is compared against a standard practice of posing only cumulative limits in the optimization. In a prostate case, incorporating both forms of limits into planning converted an undeliverable plan obtained by considering only the cumulative limits into a deliverable one within 3% of the value obtained by ignoring the fraction size limits. A two-phase boosting strategy is studied as well, where the first phase aims to radiate primary and secondary targets simultaneously, and the second phase aims to escalate the tumor dose. Using of the simultaneous strategy on both phases, the dose difference between the primary and secondary targets was enhanced, with better sparing of the rectum and bladder.展开更多
The double-chain space manipulator(DCSM)can complete collaborative tasks in a large workspace,which is of great significance for its development.The complex structure and multiple variables of the DCSM present signifi...The double-chain space manipulator(DCSM)can complete collaborative tasks in a large workspace,which is of great significance for its development.The complex structure and multiple variables of the DCSM present significant challenges for DCSM design.In this paper,an integrated type and dimension method for DCSM design of using a Q-learning-based mixed-integer optimization method was proposed.Based on the analysis of the mechanism characteristics of the DCSM,a model-free kinematics modeling method was proposed for unknown configurations,and the discrete variables,including the number and axis direction of joints,and the continuous variables,including the link lengths,were linearized,enabling the subsequent efficient optimization.Then,a performance index system,including workspace,comprehensive operability and follow-up sensitivity,was established,which reflects the coupling relationship between the main chain and the branch chains with regard to performance.By introducing the ideas of judgment and decision-making from Q-learning into the mechanism design,efficient optimization of multiple variables under complex performance constraints was achieved.The analysis results indicate that the method proposed in this paper has high convergence speed and computational efficiency,and can obtain multiple feasible solutions of different types.This study provides the basis for the design of manipulators with complex configurations and multiple variables.展开更多
The efficient design of distributed energy systems is affected by many uncertain factors.However,many current studies have not fully considered uncertainty,and the solutions obtained are not feasible in extreme situat...The efficient design of distributed energy systems is affected by many uncertain factors.However,many current studies have not fully considered uncertainty,and the solutions obtained are not feasible in extreme situations.In this regard,the planning of a distributed energy system in a hospital in North China is modeled as a mixed integer linear programming,combining global sensitivity analysis and robust optimization to obtain robust optimization schemes with different degrees of conservatism.According to the findings,the deterministic case’s total yearly cost of distributed energy systems is between 4.42 and 6.84×106 EUR,while the corresponding total annual carbon emissions are between 54.4 and 37.6 thousand tons.The annual total carbon emissions of the system will be reduced by up to 30.9%,and the corresponding annual total cost will be increased by 54.7%.Considering the uncertainty of the system,through the global sensitivity analysis,it is found that the price of natural gas has the greatest impact on the system.Only when the price of solid oxide fuel cells falls to 712 EUR/kW,it will replace other traditional technologies.Finally,robust optimization of natural gas price changes is carried out to explore solutions with different degrees of conservatism.展开更多
This work addresses the multiscale optimization of the puri cation processes of antibody fragments. Chromatography decisions in the manufacturing processes are optimized, including the number of chromatography columns...This work addresses the multiscale optimization of the puri cation processes of antibody fragments. Chromatography decisions in the manufacturing processes are optimized, including the number of chromatography columns and their sizes, the number of cycles per batch, and the operational ow velocities. Data-driven models of chromatography throughput are developed considering loaded mass, ow velocity, and column bed height as the inputs, using manufacturing-scale simulated datasets based on microscale experimental data. The piecewise linear regression modeling method is adapted due to its simplicity and better prediction accuracy in comparison with other methods. Two alternative mixed-integer nonlinear programming (MINLP) models are proposed to minimize the total cost of goods per gram of the antibody puri cation process, incorporating the data-driven models. These MINLP models are then reformulated as mixed-integer linear programming (MILP) models using linearization techniques and multiparametric disaggregation. Two industrially relevant cases with different chromatography column size alternatives are investigated to demonstrate the applicability of the proposed models.展开更多
In recent years,the proportion of installed wind power in the three north regions where wind power bases are concentrated is increasing,but the peak regulation capacity of the power grid in the three north regions of ...In recent years,the proportion of installed wind power in the three north regions where wind power bases are concentrated is increasing,but the peak regulation capacity of the power grid in the three north regions of China is limited,resulting in insufficient local wind power consumption capacity.Therefore,this paper proposes a two-layer optimal scheduling strategy based on wind power consumption benefits to improve the power grid’s wind power consumption capacity.The objective of the uppermodel is tominimize the peak-valley difference of the systemload,which ismainly to optimize the system load by using the demand response resources,and to reduce the peak-valley difference of the system load to improve the peak load regulation capacity of the grid.The lower scheduling model is aimed at maximizing the system operation benefit,and the scheduling model is selected based on the rolling schedulingmethod.The load-side schedulingmodel needs to reallocate the absorbed wind power according to the response speed,absorption benefit,and curtailment penalty cost of the two DR dispatching resources.Finally,the measured data of a power grid are simulated by MATLAB,and the results show that:the proposed strategy can improve the power grid’s wind power consumption capacity and get a large wind power consumption benefit.展开更多
In fossil energy pollution is serious and the“double carbon”goal is being promoted,as a symbol of fresh energy in the electrical system,solar and wind power have an increasing installed capacity,only conventional un...In fossil energy pollution is serious and the“double carbon”goal is being promoted,as a symbol of fresh energy in the electrical system,solar and wind power have an increasing installed capacity,only conventional units obviously can not solve the new energy as the main body of the scheduling problem.To enhance the systemscheduling ability,based on the participation of thermal power units,incorporate the high energy-carrying load of electro-melting magnesiuminto the regulation object,and consider the effects on the wind unpredictability of the power.Firstly,the operating characteristics of high energy load and wind power are analyzed,and the principle of the participation of electrofusedmagnesiumhigh energy-carrying loads in the elimination of obstructedwind power is studied.Second,a two-layer optimization model is suggested,with the objective function being the largest amount of wind power consumed and the lowest possible cost of system operation.In the upper model,the high energy-carrying load regulates the blocked wind power,and in the lower model,the second-order cone approximation algorithm is used to solve the optimizationmodelwithwind power uncertainty,so that a two-layer optimizationmodel that takes into account the regulation of the high energy-carrying load of the electrofused magnesium and the uncertainty of the wind power is established.Finally,the model is solved using Gurobi,and the results of the simulation demonstrate that the suggested model may successfully lower wind abandonment,lower system operation costs,increase the accuracy of day-ahead scheduling,and lower the final product error of the thermal electricity unit.展开更多
As the power system transitions to a new green and low-carbon paradigm,the penetration of renewable energy in China’s power system is gradually increasing.However,the variability and uncertainty of renewable energy o...As the power system transitions to a new green and low-carbon paradigm,the penetration of renewable energy in China’s power system is gradually increasing.However,the variability and uncertainty of renewable energy output limit its profitability in the electricity market and hinder its market-based integration.This paper first constructs a wind-solar-thermalmulti-energy complementary system,analyzes its external game relationships,and develops a bi-level market optimization model.Then,it considers the contribution levels of internal participants to establish a comprehensive internal distribution evaluation index system.Finally,simulation studies using the IEEE 30-bus system demonstrate that the multi-energy complementary system stabilizes nodal outputs,enhances the profitability of market participants,and promotes the market-based integration of renewable energy.展开更多
The reliable and coordinated operation of energy systems is becoming increasingly important as renewable energy penetration grows and electricity and gas infrastructures become more interconnected.This study ad-dresse...The reliable and coordinated operation of energy systems is becoming increasingly important as renewable energy penetration grows and electricity and gas infrastructures become more interconnected.This study ad-dresses the challenge of aligning multiple stakeholders’objectives in integrated electricity and gas distribution systems by proposing a sequential constrained optimization method.The method solves the multi-objective optimization problem by sequentially prioritizing each entity’s objective while incorporating others as adaptive-weighted sub-objectives and constraints.This process ensures that all entities participate in a fair and balanced decision-making procedure,ultimately converging to a consensus-based solution.The algorithm is validated using IEEE 33-bus and 118-bus test systems coupled with gas networks.Results show that the proposed method improves optimal resource allocation effectiveness by up to 3.66 compared to individual-objective or aggregated-objective benchmarks.Specifically,the method achieves performance improvements ranging from 0.02 pu to 1.7 pu across four distinct entities,highlighting its superiority in balancing conflicting operational goals.Moreover,the method demonstrates low computational delay and converges in fewer than 15 iterations for all tested cases.The algorithm adapts flexibly to different system configurations and maintains solution stability even under asymmetric stakeholder preferences.These findings indicate that the proposed sequential constrained optimization framework is a scalable and effective approach for equitable,multi-agent coordination in integrated multi-energy systems.展开更多
Mixed-integer optimal control problems(MIOCPs) usually play important roles in many real-world engineering applications. However, the MIOCP is a typical NP-hard problem with considerable computational complexity, resu...Mixed-integer optimal control problems(MIOCPs) usually play important roles in many real-world engineering applications. However, the MIOCP is a typical NP-hard problem with considerable computational complexity, resulting in slow convergence or premature convergence by most current heuristic optimization algorithms. Accordingly, this study proposes a new and effective hybrid algorithm based on quantum computing theory to solve the MIOCP. The algorithm consists of two parts:(i) Quantum Annealing(QA) specializes in solving integer optimization with high efficiency owing to the unique annealing process based on quantum tunneling, and(ii) Double-Elite Quantum Ant Colony Algorithm(DEQACA) which adopts double-elite coevolutionary mechanism to enhance global searching is developed for the optimization of continuous decisions. The hybrid QA/DEQACA algorithm integrates the strengths of such algorithms to better balance the exploration and exploitation abilities. The overall evolution performs to seek out the optimal mixed-integer decisions by interactive parallel computing of the QA and the DEQACA. Simulation results on benchmark functions and practical engineering optimization problems verify that the proposed numerical method is more excel at achieving promising results than other two state-of-the-art heuristics.展开更多
基金supported by the Scientific Research Project of Xiang Jiang Lab(22XJ02003)the University Fundamental Research Fund(23-ZZCX-JDZ-28)+5 种基金the National Science Fund for Outstanding Young Scholars(62122093)the National Natural Science Foundation of China(72071205)the Hunan Graduate Research Innovation Project(ZC23112101-10)the Hunan Natural Science Foundation Regional Joint Project(2023JJ50490)the Science and Technology Project for Young and Middle-aged Talents of Hunan(2023TJ-Z03)the Science and Technology Innovation Program of Humnan Province(2023RC1002)。
文摘Traditional large-scale multi-objective optimization algorithms(LSMOEAs)encounter difficulties when dealing with sparse large-scale multi-objective optimization problems(SLM-OPs)where most decision variables are zero.As a result,many algorithms use a two-layer encoding approach to optimize binary variable Mask and real variable Dec separately.Nevertheless,existing optimizers often focus on locating non-zero variable posi-tions to optimize the binary variables Mask.However,approxi-mating the sparse distribution of real Pareto optimal solutions does not necessarily mean that the objective function is optimized.In data mining,it is common to mine frequent itemsets appear-ing together in a dataset to reveal the correlation between data.Inspired by this,we propose a novel two-layer encoding learning swarm optimizer based on frequent itemsets(TELSO)to address these SLMOPs.TELSO mined the frequent terms of multiple particles with better target values to find mask combinations that can obtain better objective values for fast convergence.Experi-mental results on five real-world problems and eight benchmark sets demonstrate that TELSO outperforms existing state-of-the-art sparse large-scale multi-objective evolutionary algorithms(SLMOEAs)in terms of performance and convergence speed.
基金Financial support from the National Natural Science Foundation of China under Grant(22393954 and 22078358)is gratefully acknowledged.
文摘Steam power systems(SPSs)in industrial parks are the typical utility systems for heat and electricity supply.In SPSs,electricity is generated by steam turbines,and steam is generally produced and supplied at multiple levels to serve the heat demands of consumers with different temperature grades,so that energy is utilized in cascade.While a large number of steam levels enhances energy utilization efficiency,it also tends to cause a complex steam pipeline network in the industrial park.In practice,a moderate number of steam levels is always adopted in SPSs,leading to temperature mismatches between heat supply and demand for some consumers.This study proposes a distributed steam turbine system(DSTS)consisting of main steam turbines on the energy supply side and auxiliary steam turbines on the energy consumption side,aiming to balance the heat production costs,the distance-related costs,and the electricity generation of SPSs in industrial parks.A mixed-integer nonlinear programming model is established for the optimization of SPSs,with the objective of minimizing the total annual cost(TAC).The optimal number of steam levels and the optimal configuration of DSTS for an industrial park can be determined by solving the model.A case study demonstrates that the TAC of the SPS is reduced by 220.6×10^(3)USD(2.21%)through the arrangement of auxiliary steam turbines.The sub-optimal number of steam levels and a non-optimal operating condition slightly increase the TAC by 0.46%and 0.28%,respectively.The sensitivity analysis indicates that the optimal number of steam levels tends to decrease from 3 to 2 as electricity price declines.
基金Supported by the National Natural Science Foundation of China(10571141,70971109)the Key Projectof the National Natural Science Foundation of China(70531030)
文摘To properly describe and solve complex decision problems,research on theoretical properties and solution of mixed-integer quadratic programs is becoming very important.We establish in this paper different Lipschitz-type continuity results about the optimal value function and optimal solutions of mixed-integer parametric quadratic programs with parameters in the linear part of the objective function and in the right-hand sides of the linear constraints.The obtained results extend some existing results for continuous quadratic programs,and,more importantly,lay the foundation for further theoretical study and corresponding algorithm analysis on mixed-integer quadratic programs.
基金the National Natural Science Foundation of China(No.61573378)the BUPT Excellent Ph.D.Students Foundation(No.CX2019113)。
文摘It is extremely challenging to solve the mixed-integer optimal control problems(MIOCPs)due to the complex computation in solving the integer decision variables.This paper presents a new method based on quantum annealing(QA)to solve MIOCP.The QA is a metaheuristic which applies quantum tunneling in the annealing process.It has a faster convergence speed in optimal-searching and is less likely to run into local minima.Hence,QA is applied to deal with this kind of optimization problems.First,MIOCP is transformed into a mixed-integer nonlinear programming(MINLP).Then,a method based on QA is adopted to solve the MINLP and acquire the optimal solution.At last,two benchmark examples including Lotka-Volterra type fishing problem and distillation column are presented and solved.The effectiveness of the metliodology is verified by the acquired optimal schemes.
基金supported by the National Natural Science Foundation of China(No.21365008)the Science Foundation of Guangxi province of China(No.2012GXNSFAA053230)
文摘Refineries often need to find similar crude oil to replace the scarce crude oil for stabilizing the feedstock property. We introduced the method for calculation of crude blended properties firstly, and then created a crude oil selection and blending optimization model based on the data of crude oil property. The model is a mixed-integer nonlinear programming(MINLP) with constraints, and the target is to maximize the similarity between the blended crude oil and the objective crude oil. Furthermore, the model takes into account the selection of crude oils and their blending ratios simultaneously, and transforms the problem of looking for similar crude oil into the crude oil selection and blending optimization problem. We applied the Improved Cuckoo Search(ICS) algorithm to solving the model. Through the simulations, ICS was compared with the genetic algorithm, the particle swarm optimization algorithm and the CPLEX solver. The results show that ICS has very good optimization efficiency. The blending solution can provide a reference for refineries to find the similar crude oil. And the method proposed can also give some references to selection and blending optimization of other materials.
基金supported by the National Key R&D Program of China(2018YFA0702200)Science and Technology Project of State Grid Shandong Electric Power Corporation(52062518000Q)。
文摘The renewable portfolio standard has been promoted in parallel with the reform of the electricity market,and the flexibility requirement of the power system has rapidly increased.To promote renewable energy consumption and improve power system flexibility,a bi-level optimal operation model of the electricity market is proposed.A probabilistic model of the flexibility requirement is established,considering the correlation between wind power,photovoltaic power,and load.A bi-level optimization model is established for the multi-markets;the upper and lower models represent the intra-provincial market and inter-provincial market models,respectively.To efficiently solve the model,it is transformed into a mixed-integer linear programming model using the Karush–Kuhn–Tucker condition and Lagrangian duality theory.The economy and flexibility of the model are verified using a provincial power grid as an example.
基金the Liaoning Technical University Outstanding Youth Science Foundation(jx09-10)
文摘It took that the weight minimum and drive efficiency maximal were as double optimizing target,the optimization model had built the drilling string,and the optimization solution was used of the ant colony algorithm to find in progress.Adopted a two-layer search of the continuous space ant colony algorithm with overlapping or variation global ant search operation strategy and conjugated gradient partial ant search operation strat- egy.The experiment indicates that the spiral drill weight reduces 16.77% and transports the efficiency enhance 7.05% through the optimization design,the ant colony algorithm application on the spiral drill optimized design has provided the basis for the system re- search screw coal mine machine.
基金co-supported by the National Natural Science Foundation of China(No.U20B2056)the Office of Military and Civilian Integration Development Committee of Shanghai,China(No.2020-jmrh1-kj25).
文摘Multi-beam antenna and beam hopping technologies are an effective solution for scarce satellite frequency resources.One of the primary challenges accompanying with Multi-Beam Satellites(MBS)is an efficient Dynamic Resource Allocation(DRA)strategy.This paper presents a learning-based Hybrid-Action Deep Q-Network(HADQN)algorithm to address the sequential decision-making optimization problem in DRA.By using a parameterized hybrid action space,HADQN makes it possible to schedule the beam pattern and allocate transmitter power more flexibly.To pursue multiple long-term QoS requirements,HADQN adopts a multi-objective optimization method to decrease system transmission delay,loss ratio of data packets and power consumption load simultaneously.Experimental results demonstrate that the proposed HADQN algorithm is feasible and greatly reduces in-orbit energy consumption without compromising QoS performance.
基金the National Natural Science Foundation of China for its financial support(U1462206,21991100,21991104)。
文摘In order to explore the potential of profit margin improvement,a novel three-scale integrated optimization model of furnace simulation,cyclic scheduling,and supply chain of ethylene plants is proposed and evaluated.A decoupling strategy is proposed for the solution of the three-scale model,which uses our previously proposed reactor scale model for operation optimization and then transfers the obtained results as a parameter table in the joint MILP optimization of plant-supply chain scale for cyclic scheduling.This optimization framework simplifies the fundamental mixed-integer nonlinear programming(MINLP)into several sub-models,and improves the interpretability and extendibility.In the evaluation of an industrial case,a profit increase at a percentage of 3.25%is attained in optimization compared to the practical operations.Further sensitivity analysis is carried out for strategy evolving study when price policy,supply chain,and production requirement parameters are varied.These results could provide useful suggestions for petrochemical enterprises on thermal cracking production.
基金the financial support from the National Basic Research Program of China(No.2013CB733506)the National Natural Science Fund for Distinguished Young Scholars(No.21425625)National Natural Science Foundation of China(No.21576269,21576262)
文摘The superstructure optimization of biomass to biomethane system through digestion is conducted in this work. The system encompasses biofeedstock collection and transportation, anaerobic digestion, biogas upgrading, and digestate recycling. We propose a multicriteria mixed integer nonlinear programming(MINLP) model that seeks to minimize the energy consumption and maximize the green degree and the biomethane production constrained by technology selection, mass balance, energy balance, and environmental impact. A multi-objective MINLP model is proposed and solved with a fast nondominated sorting genetic algorithm Ⅱ(NSGA-Ⅱ). The resulting Pareto-optimal surface reveals the trade-off among the conflicting objectives. The optimal results indicate quantitatively that higher green degree and biomethane production objectives can be obtained at the expense of destroying the performance of the energy consumption objective.
文摘Radiation therapy plans are optimized as a single treatment plan, but delivered over 30 - 50 treatment sessions (known as fractions). This paper proposes a new mixed-integer linear programming model to simultaneously incorporate fractionation and cumulative constraints in Intensity Modulated Radiation Therapy (IMRT) planning optimization used in cancer treatment. The method is compared against a standard practice of posing only cumulative limits in the optimization. In a prostate case, incorporating both forms of limits into planning converted an undeliverable plan obtained by considering only the cumulative limits into a deliverable one within 3% of the value obtained by ignoring the fraction size limits. A two-phase boosting strategy is studied as well, where the first phase aims to radiate primary and secondary targets simultaneously, and the second phase aims to escalate the tumor dose. Using of the simultaneous strategy on both phases, the dose difference between the primary and secondary targets was enhanced, with better sparing of the rectum and bladder.
基金supported by the Beijing Municipal Natural Science Foundation for Distinguished Young Scholars(Grant No.JQ22007)the National Natural Science Foundation of China(Grant No.52275001)。
文摘The double-chain space manipulator(DCSM)can complete collaborative tasks in a large workspace,which is of great significance for its development.The complex structure and multiple variables of the DCSM present significant challenges for DCSM design.In this paper,an integrated type and dimension method for DCSM design of using a Q-learning-based mixed-integer optimization method was proposed.Based on the analysis of the mechanism characteristics of the DCSM,a model-free kinematics modeling method was proposed for unknown configurations,and the discrete variables,including the number and axis direction of joints,and the continuous variables,including the link lengths,were linearized,enabling the subsequent efficient optimization.Then,a performance index system,including workspace,comprehensive operability and follow-up sensitivity,was established,which reflects the coupling relationship between the main chain and the branch chains with regard to performance.By introducing the ideas of judgment and decision-making from Q-learning into the mechanism design,efficient optimization of multiple variables under complex performance constraints was achieved.The analysis results indicate that the method proposed in this paper has high convergence speed and computational efficiency,and can obtain multiple feasible solutions of different types.This study provides the basis for the design of manipulators with complex configurations and multiple variables.
基金supported by the Major Program of the National Natural Science Foundation of China(Grant No.52090064)the China National Key R&D Program(Grant No.2021YFE0194500).
文摘The efficient design of distributed energy systems is affected by many uncertain factors.However,many current studies have not fully considered uncertainty,and the solutions obtained are not feasible in extreme situations.In this regard,the planning of a distributed energy system in a hospital in North China is modeled as a mixed integer linear programming,combining global sensitivity analysis and robust optimization to obtain robust optimization schemes with different degrees of conservatism.According to the findings,the deterministic case’s total yearly cost of distributed energy systems is between 4.42 and 6.84×106 EUR,while the corresponding total annual carbon emissions are between 54.4 and 37.6 thousand tons.The annual total carbon emissions of the system will be reduced by up to 30.9%,and the corresponding annual total cost will be increased by 54.7%.Considering the uncertainty of the system,through the global sensitivity analysis,it is found that the price of natural gas has the greatest impact on the system.Only when the price of solid oxide fuel cells falls to 712 EUR/kW,it will replace other traditional technologies.Finally,robust optimization of natural gas price changes is carried out to explore solutions with different degrees of conservatism.
文摘This work addresses the multiscale optimization of the puri cation processes of antibody fragments. Chromatography decisions in the manufacturing processes are optimized, including the number of chromatography columns and their sizes, the number of cycles per batch, and the operational ow velocities. Data-driven models of chromatography throughput are developed considering loaded mass, ow velocity, and column bed height as the inputs, using manufacturing-scale simulated datasets based on microscale experimental data. The piecewise linear regression modeling method is adapted due to its simplicity and better prediction accuracy in comparison with other methods. Two alternative mixed-integer nonlinear programming (MINLP) models are proposed to minimize the total cost of goods per gram of the antibody puri cation process, incorporating the data-driven models. These MINLP models are then reformulated as mixed-integer linear programming (MILP) models using linearization techniques and multiparametric disaggregation. Two industrially relevant cases with different chromatography column size alternatives are investigated to demonstrate the applicability of the proposed models.
基金The study was supported by the State Grid Henan Economic Research Institute Regional Autonomy Project.
文摘In recent years,the proportion of installed wind power in the three north regions where wind power bases are concentrated is increasing,but the peak regulation capacity of the power grid in the three north regions of China is limited,resulting in insufficient local wind power consumption capacity.Therefore,this paper proposes a two-layer optimal scheduling strategy based on wind power consumption benefits to improve the power grid’s wind power consumption capacity.The objective of the uppermodel is tominimize the peak-valley difference of the systemload,which ismainly to optimize the system load by using the demand response resources,and to reduce the peak-valley difference of the system load to improve the peak load regulation capacity of the grid.The lower scheduling model is aimed at maximizing the system operation benefit,and the scheduling model is selected based on the rolling schedulingmethod.The load-side schedulingmodel needs to reallocate the absorbed wind power according to the response speed,absorption benefit,and curtailment penalty cost of the two DR dispatching resources.Finally,the measured data of a power grid are simulated by MATLAB,and the results show that:the proposed strategy can improve the power grid’s wind power consumption capacity and get a large wind power consumption benefit.
基金funded by the National Key R&D Program of China,Grant Number 2019YFB1505400.
文摘In fossil energy pollution is serious and the“double carbon”goal is being promoted,as a symbol of fresh energy in the electrical system,solar and wind power have an increasing installed capacity,only conventional units obviously can not solve the new energy as the main body of the scheduling problem.To enhance the systemscheduling ability,based on the participation of thermal power units,incorporate the high energy-carrying load of electro-melting magnesiuminto the regulation object,and consider the effects on the wind unpredictability of the power.Firstly,the operating characteristics of high energy load and wind power are analyzed,and the principle of the participation of electrofusedmagnesiumhigh energy-carrying loads in the elimination of obstructedwind power is studied.Second,a two-layer optimization model is suggested,with the objective function being the largest amount of wind power consumed and the lowest possible cost of system operation.In the upper model,the high energy-carrying load regulates the blocked wind power,and in the lower model,the second-order cone approximation algorithm is used to solve the optimizationmodelwithwind power uncertainty,so that a two-layer optimizationmodel that takes into account the regulation of the high energy-carrying load of the electrofused magnesium and the uncertainty of the wind power is established.Finally,the model is solved using Gurobi,and the results of the simulation demonstrate that the suggested model may successfully lower wind abandonment,lower system operation costs,increase the accuracy of day-ahead scheduling,and lower the final product error of the thermal electricity unit.
基金funded by the National Key R&D Program of China,grant number 2019YFB1505400.
文摘As the power system transitions to a new green and low-carbon paradigm,the penetration of renewable energy in China’s power system is gradually increasing.However,the variability and uncertainty of renewable energy output limit its profitability in the electricity market and hinder its market-based integration.This paper first constructs a wind-solar-thermalmulti-energy complementary system,analyzes its external game relationships,and develops a bi-level market optimization model.Then,it considers the contribution levels of internal participants to establish a comprehensive internal distribution evaluation index system.Finally,simulation studies using the IEEE 30-bus system demonstrate that the multi-energy complementary system stabilizes nodal outputs,enhances the profitability of market participants,and promotes the market-based integration of renewable energy.
基金supported by the research fund of Hanyang University(HY-202400000003289)supported by the Korea Institute of Energy Technol-ogy Evaluation and Planning(KETEP)and the Ministry of Trade,In-dustry&Energy(MOTIE)of the Republic of Korea(No.RS-2025-02313547).
文摘The reliable and coordinated operation of energy systems is becoming increasingly important as renewable energy penetration grows and electricity and gas infrastructures become more interconnected.This study ad-dresses the challenge of aligning multiple stakeholders’objectives in integrated electricity and gas distribution systems by proposing a sequential constrained optimization method.The method solves the multi-objective optimization problem by sequentially prioritizing each entity’s objective while incorporating others as adaptive-weighted sub-objectives and constraints.This process ensures that all entities participate in a fair and balanced decision-making procedure,ultimately converging to a consensus-based solution.The algorithm is validated using IEEE 33-bus and 118-bus test systems coupled with gas networks.Results show that the proposed method improves optimal resource allocation effectiveness by up to 3.66 compared to individual-objective or aggregated-objective benchmarks.Specifically,the method achieves performance improvements ranging from 0.02 pu to 1.7 pu across four distinct entities,highlighting its superiority in balancing conflicting operational goals.Moreover,the method demonstrates low computational delay and converges in fewer than 15 iterations for all tested cases.The algorithm adapts flexibly to different system configurations and maintains solution stability even under asymmetric stakeholder preferences.These findings indicate that the proposed sequential constrained optimization framework is a scalable and effective approach for equitable,multi-agent coordination in integrated multi-energy systems.
基金supported by the National Natural Science Foundation of China under Grant No.61573378the BUPT Excellent Ph.D.Students Foundation under Grant No.CX2019113。
文摘Mixed-integer optimal control problems(MIOCPs) usually play important roles in many real-world engineering applications. However, the MIOCP is a typical NP-hard problem with considerable computational complexity, resulting in slow convergence or premature convergence by most current heuristic optimization algorithms. Accordingly, this study proposes a new and effective hybrid algorithm based on quantum computing theory to solve the MIOCP. The algorithm consists of two parts:(i) Quantum Annealing(QA) specializes in solving integer optimization with high efficiency owing to the unique annealing process based on quantum tunneling, and(ii) Double-Elite Quantum Ant Colony Algorithm(DEQACA) which adopts double-elite coevolutionary mechanism to enhance global searching is developed for the optimization of continuous decisions. The hybrid QA/DEQACA algorithm integrates the strengths of such algorithms to better balance the exploration and exploitation abilities. The overall evolution performs to seek out the optimal mixed-integer decisions by interactive parallel computing of the QA and the DEQACA. Simulation results on benchmark functions and practical engineering optimization problems verify that the proposed numerical method is more excel at achieving promising results than other two state-of-the-art heuristics.
