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A multi-objective control system for temperature control accuracy improvement during run-out table cooling process 认领 引用
1
作者 Bao-Qian Tian Dian-Yao Gong +6 位作者 Cheng-Yan Ding Li-Jie Dong Qiu-Na Wang Guo Yuan Ke-Long Zhang Wei Zheng Jie Sun 《Journal of Iron and Steel Research International》 SCIE EI CSCD 2026年第2期131-147,共17页
Existing control systems for coiling temperature struggle with significant time lags and multi-objective synchronous control during cooling,limiting their temperature control accuracy.To overcome these drawbacks,an on... Existing control systems for coiling temperature struggle with significant time lags and multi-objective synchronous control during cooling,limiting their temperature control accuracy.To overcome these drawbacks,an online cooling system featuring multi-objective collaborative control is proposed.The proposed system achieves the synchronous control of the ultra-fast cooling temperature,middle temperature,and coiling temperature.First,the run-out table cooling zone is divided into multiple logical control zones,and traditional mechanism models are improved by introducing multiple heat flux adaptive coefficients.Then,a dynamic feedforward control method is developed to correct potential deviations in the calculation process.Finally,to enhance the proposed control system’s accuracy and self-learning capability,a multi-objective real-time adaptation strategy is introduced for dynamic heat flux adaptive coefficients adjustment.Analysis and application results show that the proposed multi-objective collaborative control system significantly improves the temperature control accuracy while ensuring the consistency of mechanical properties.Comparison results indicate that,under the proposed control system,the coiling temperature control accuracy within ±20℃ for segments located at 50 m from the strip head is improved by 26%,compared with the original control system.In addition,using the proposed system,the standard deviation of the yield strength is decreased by 38%,compared with the original control system. 展开更多
关键词 Run-out table Temperature control Multi-objective collaborative control system Dynamic feedforward control Multi-objective real-time adaptation
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A MOGWO-based multi-objective optimal tuning strategy for model predictive direct thrust control architecture in gas turbine engine 认领 引用
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作者 Genchang WANG Xiaofeng LIU 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第3期384-409,共26页
In modern gas turbine engine control,Direct Thrust Control(DTC) is an effective method for achieving the desired thrust.Model Predictive Control(MPC) has the characteristics of handling constraints while accomplishing... In modern gas turbine engine control,Direct Thrust Control(DTC) is an effective method for achieving the desired thrust.Model Predictive Control(MPC) has the characteristics of handling constraints while accomplishing command tracking,making it a promising approach for implementing DTC.However,since the performance of DTC is highly sensitive to MPC's tuning parameters,developing an efficient optimization strategy for these parameters becomes imperative.Therefore,a Model Predictive Direct Thrust Control(MP-DTC) architecture is designed,and its asymptotic stability is proven.Additionally,the influence of the tuning parameters on control performance is analyzed.Then,a tuning strategy for MP-DTC architecture is proposed.This strategy combines the objectives of DTC to design a Multi-Objective Optimization(MOO) index function,and uses Multi-Objective Grey Wolf Optimizer(MOGWO) to solve its Pareto front and obtain the tuning parameters.In the Hardware-in-Loop(HIL) experiments,the proposed MPDTC architecture achieves the shortest settling time and smallest overshoot compared to the latest DTC scheme.Its MOGWO-based tuning strategy provides more Pareto-optimal solutions,ensuring optimal selection based on rapidity and stability,and maintains precise DTC even under component degradation and various operating conditions,thereby providing robustness,optimality,and generalizability. 展开更多
关键词 Direct thrust control Model predictive control Multi-objective grey wolf optimizer Multi-objective optimization Tuning parameters
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Multi-objective land use simulation by integrating urban spatial suitability and ecological carrying capacity evaluations 认领 引用
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作者 CHEN Zeyin LI Siying +3 位作者 LIU Zheng HUO Yixin WU Tao ZHOU Xingang 《Journal of Geographical Sciences》 SCIE CSCD 2026年第4期799-824,共26页
Balancing urbanization with ecological carrying capacity is essential for sustainable urban development.Traditional land use prediction and urban growth boundary(UGB)delineation methods often overlook ecological asses... Balancing urbanization with ecological carrying capacity is essential for sustainable urban development.Traditional land use prediction and urban growth boundary(UGB)delineation methods often overlook ecological assessments and fail to address policy conflicts.This study proposes an integrated model combining urban spatial suitability(USS)and ecological carrying capacity(ECC)evaluations with cellular automata(CA)model to improve simulation accuracy and support scenario-based UGB delineation.First,we identify spatial variations in urban development potential under different scenarios by adjusting the weights of USS and ECC.Then,a multi-objective planning model is used to optimize the future land-use structure,maximizing overall benefits.Finally,the development potential and optimized land allocation are incorporated into the CA model to simulate future land use and delineate UGB for each scenario.Results show that integrating USS and ECC evaluations improves simulation accuracy,with the Kappa coefficient increasing from 0.836(with only USS evaluation)to 0.908 and overall accuracy reaching 94.1%.While the economic development scenario yields the highest economic benefits,a stronger emphasis on ECC produces more compact and spatially organized urban forms,characterized by higher aggregation and lower fragmentation.This framework provides a robust basis for multi-scenario urban simulation and offers valuable guidance for the scientific UGB delineation. 展开更多
关键词 land use prediction urban growth boundary cellular automata multi-scenario simulation urban spatial suitability ecological carrying capacity multi-objective planning
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Multi-objective topology optimization for cutout design in deployable composite thin-walled structures 认领 引用
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作者 Hao JIN Ning AN +3 位作者 Qilong JIA Chun SHAO Xiaofei MA Jinxiong ZHOU 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第1期674-694,共21页
Deployable Composite Thin-Walled Structures(DCTWS)are widely used in space applications due to their ability to compactly fold and self-deploy in orbit,enabled by cutouts.Cutout design is crucial for balancing structu... Deployable Composite Thin-Walled Structures(DCTWS)are widely used in space applications due to their ability to compactly fold and self-deploy in orbit,enabled by cutouts.Cutout design is crucial for balancing structural rigidity and flexibility,ensuring material integrity during large deformations,and providing adequate load-bearing capacity and stability once deployed.Most research has focused on optimizing cutout size and shape,while topology optimization offers a broader design space.However,the anisotropic properties of woven composite laminates,complex failure criteria,and multi-performance optimization needs have limited the exploration of topology optimization in this field.This work derives the sensitivities of bending stiffness,critical buckling load,and the failure index of woven composite materials with respect to element density,and formulates both single-objective and multi-objective topology optimization models using a linear weighted aggregation approach.The developed method was integrated with the commercial finite element software ABAQUS via a Python script,allowing efficient application to cutout design in various DCTWS configurations to maximize bending stiffness and critical buckling load under material failure constraints.Optimization of a classical tubular hinge resulted in improvements of 107.7%in bending stiffness and 420.5%in critical buckling load compared to level-set topology optimization results reported in the literature,validating the effectiveness of the approach.To facilitate future research and encourage the broader adoption of topology optimization techniques in DCTWS design,the source code for this work is made publicly available via a Git Hub link:http://gffzz188fe103f8f1460asc5n6xocqubkq6nuu.ffgz.tsg.suse.edu.cn/jinhao-ok1/Topo-for-DCTWS.git. 展开更多
关键词 Composite laminates Deployable structures Multi-objective optimization Thin-walled structures Topology optimization
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Multi-objective spatial optimization by considering land use suitability in the Yangtze River Delta region 认领 引用
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作者 CHENG Qianwen LI Manchun +4 位作者 LI Feixue LIN Yukun DING Chenyin XIAO Lishan LI Weiyue 《Journal of Geographical Sciences》 SCIE CSCD 2026年第1期45-78,共34页
Rapid urbanization in China has led to spatial antagonism between urban development and farmland protection and ecological security maintenance.Multi-objective spatial collaborative optimization is a powerful method f... Rapid urbanization in China has led to spatial antagonism between urban development and farmland protection and ecological security maintenance.Multi-objective spatial collaborative optimization is a powerful method for achieving sustainable regional development.Previous studies on multi-objective spatial optimization do not involve spatial corrections to simulation results based on the natural endowment of space resources.This study proposes an Ecological Security-Food Security-Urban Sustainable Development(ES-FS-USD)spatial optimization framework.This framework combines the non-dominated sorting genetic algorithm II(NSGA-II)and patch-generating land use simulation(PLUS)model with an ecological protection importance evaluation,comprehensive agricultural productivity evaluation,and urban sustainable development potential assessment and optimizes the territorial space in the Yangtze River Delta(YRD)region in 2035.The proposed sustainable development(SD)scenario can effectively reduce the destruction of landscape patterns of various land-use types while considering both ecological and economic benefits.The simulation results were further revised by evaluating the land-use suitability of the YRD region.According to the revised spatial pattern for the YRD in 2035,the farmland area accounts for 43.59%of the total YRD,which is 5.35%less than that in 2010.Forest,grassland,and water area account for 40.46%of the total YRD—an increase of 1.42%compared with the case in 2010.Construction land accounts for 14.72%of the total YRD—an increase of 2.77%compared with the case in 2010.The ES-FS-USD spatial optimization framework ensures that spatial optimization outcomes are aligned with the natural endowments of land resources,thereby promoting the sustainable use of land resources,improving the ability of spatial management,and providing valuable insights for decision makers. 展开更多
关键词 multi-objective spatial optimization multi-scenario simulation ecological protection importance comprehensive agricultural productivity urban sustainable development land-use suitability
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Multi-objective optimization design of biomimetic porous scaffolds based on cancellous bone structure 认领 引用
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作者 Sujing Tian He Gong +2 位作者 Xiang Zhang Jiazi Gao Liming Zhou 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2026年第6期546-564,共19页
As age advances,accumulation of bone regeneration inhibitors in osteoporotic patients increases,resulting in larger bone defect areas and varying degrees of defects.When studying bone regeneration in osteoporotic bone... As age advances,accumulation of bone regeneration inhibitors in osteoporotic patients increases,resulting in larger bone defect areas and varying degrees of defects.When studying bone regeneration in osteoporotic bone defects,researchers often lack specificity on different bone quality statuses.To design a porous scaffold more similar to cancellous bone to promote bone regeneration,a multi-objective optimization design of biomimetic porous scaffold based on cancellous bone images was carried out in this study.Vertebral cancellous bones from rats with different bone quality statuses caused by various ovariectomy durations served as examples.First,the microstructure,mechanical and biological properties parameters of vertebral cancellous bones were calculated based on images from 20-,30-,and 38-week-old rats without ovariectomy and 30-and 38-week-old rats with ovariectomy(10 weeks and 18 weeks after ovariectomy).Second,the effects of constant value(C),which affects scaffold thickness,scale factor of z-axis(N),influencing stretching and compression of unit cell,and unit cell size(L)on the mechanical and biological properties of Schoen Gyroid and Schoen I-WP were investigated.Third,Schoen Gyroid and Schoen I-WP were optimized and evaluated using non-dominated genetic algorithm-II(NSGA-II)and complex proportional assessment method,with the elastic modulus of cancellous bones from 30-and 38-week-old ovariectomized rats as performance constraint to obtain the best structure tailored to each ovariectomized group.The surface curvature of the scaffold could be changed by stretching or compressing the unit cell,and the pore size could be changed by altering the unit cell thickness and size to obtain scaffolds suitable for different extents of bone defects.The optimized scaffolds met mechanical and biological requirements.Schoen I-WP exhibited superior comprehensive performance compared to Schoen Gyroid.The optimized design framework proposed in this study can be applied to bone defects of any age,bone site,and bone quality status,and has potential application for personalized treatment of bone defects. 展开更多
关键词 Osteoporotic bone defect Biomimetic porous scaffold Comprehensive performance Multi-objective optimization Complex proportional assessment method
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Environment-adaptive design of multilayer optical structures for surface polariton excitation via dynamic multi-objective optimization 认领 引用
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作者 Ru Lei Lin Li +4 位作者 Yiqi Feng Ziyue Ren Kun Zhang Lixun Sun Ting Mei 《Defence Technology（防务技术）》 SCIE EI CAS CSCD 2026年第5期267-279,共13页
The graphene±dielectric multilayer architecture constitutes a fundamental and widely utilized platform for sustaining surface polariton(SP)propagation.Owing to their extraordinary prospects in defence critical te... The graphene±dielectric multilayer architecture constitutes a fundamental and widely utilized platform for sustaining surface polariton(SP)propagation.Owing to their extraordinary prospects in defence critical technologies,including radar-absorbing stealth coatings,high-power microwave shielding,and ultrafast optical switching,SPs have attracted intense and sustained interest.In this study,we develop an environment-adaptive design framework that models wavelength variation as a dynamic environmental change and automatically adjusts the design parameters in response.Our method employs a dynamic multi-objective optimization algorithm augmented with a predictive transfer strategy,optimizing SP coupling efficiency,structural compactness,and fabrication feasibility.Using a population history prediction mechanism,the framework not only adaptively generates multilayer designs across the full visible spectrum without full re-initialization,but also retains and exploits knowledge of how environmental variations influence the distribution of optimal solutions.This enables rapid adjustment of the optimization direction when parameters such as wavelength,angle,or doping change,thus avoiding the need to restart the search from scratch.Comprehensive comparisons demonstrate outstanding robustness under continuous wavelength shifts.The optimized graphene-coated distributed Bragg reflector(DBR)stacks achieve near-perfect absorption(>98%)at each individual wavelength across the visible spectrum.This work not only provides theoretical guidance for SP excitation experiments,but also contributes to the optimization of polariton device design,which is crucial for enhancing the performance of defence-related optical systems. 展开更多
关键词 Distributed bragg reflectors Surface polariton Dynamic multi-objective optimization
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Sustainability evaluation of novel side-stream extractive distillation incorporating intermediate reboiler process for recovering ethyl acetate and methanol from wastewater based on multi-objective optimization 认领 引用
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作者 Ruimin Zhang Shuang Yang +3 位作者 Jinlong Li Hui Wang Tan Dai Qing Ye 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2026年第2期232-248,共17页
Although side-stream extractive distillation(SED)is widely applied in azeotropic mixture separation due to its high efficiencyand energy-saving advantages,the use of expensive high-pressure steam increases economic co... Although side-stream extractive distillation(SED)is widely applied in azeotropic mixture separation due to its high efficiencyand energy-saving advantages,the use of expensive high-pressure steam increases economic costs.The introduction of intermediate reboiler(IR)can reduce the consumption of high-pressure steam and thus reduce the operating cost.This work selects the extractive distillation process,using dimethyl sulfoxide(DMSO)as the solvent to separate ethyl acetate and methanol from wastewater.Based on the system characteristics,two SED processes are designed:SED-1 process directly obtains high-purity DMSO from the bottom of the SED column,whereas SED-2 process obtains a DMSO/water mixture at the bottom.To reduce high-pressure steam requirements,an IR is incorporated,leading to the proposal of SED-IR-1 and SED-IR-2 processes.Finally,heat-integrated processes(H-SEDIR-1 and H-SED-IR-2)are proposed based on the optimal SED-IR-1 and SED-IR-2 processes,which utilized the solvent stream waste heat to heat the IR to further reduce the energy consumption and operating cost.The results demonstrate that the H-SED-IR-1 process exhibits optimal economic performance with a 26.19% reduction in total annual cost compared with the conventional extractive distillation(CED)process,while the innovative H-SED-IR-2 process shows outstanding environmental benefits,achieving 38.78% and 39.97% reductions in CO2emissions and entropy generation,respectively,compared to the CED process. 展开更多
关键词 Ternary system with double binary azeotropes Side-stream extractive distillation Intermediate reboiler Multi-objective optimization Heat integration technology
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CMo-IABA:Constrained Multi-Objective Invisible and Adaptive Backdoor Attack for Deep Neural Networks-Based SAR Image Classification 认领 引用
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作者 Guo-Qiang Zeng Hai-Nan Wei +1 位作者 Kang-Di Lu Guang-Gang Geng 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第7期1731-1746,共16页
Deep neural networks(DNNs)have been widely applied in the field of synthetic aperture radar(SAR)image while they are facing more and more serious threats from a variety of malicious attacks.As one of the malicious att... Deep neural networks(DNNs)have been widely applied in the field of synthetic aperture radar(SAR)image while they are facing more and more serious threats from a variety of malicious attacks.As one of the malicious attacks with strong destructiveness and stealth,backdoor attacks have severely affected DNNs,but there are no related research studies concerning the backdoor attacks against the DNNs-based SAR image classification models.In this work,we make the first attempt to automatically design a constrained multi-objective invisible and adaptive backdoor attack termed as CMo-IABA for DNNs-based SAR image classification.In the CMo-IABA,we firstly generate an initial trigger-based backdoor attack randomly by a random combination of pixels with random noise conforming to the Gaussian distribution.Then,we design multi-objective functions by considering the trade-off between maximizing the attack success rate and minimizing L2 distance-based invisibility.The classification error between the backdoor DNN and the clean model is considered as the constraint to maintain the original performance of the model.To solve the optimization problem,a discrete non-dominated sorting genetic algorithm-II is introduced as the search engine with the developed crossover operation and mutation operation.The superiority of the proposed CMo-IABA to five state-of-the-art backdoor attacks on six different types of DNNs-based SAR image classification models has been demonstrated by the experimental results on Fudan University SAR(FUSAR)-ship and moving and stationary target acquisition and recognition(MSTAR)datasets in terms of attack success rate and L2 distance-based invisibility. 展开更多
关键词 Backdoor attack constrained multi-objective optimization deep neural network(DNN) image classification invisibility synthetic aperture radar
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Multi-Objective Approach for Optimizing Production Parameters of Low-Permeability Oil Well to Enhance Energy Efficiency 认领 引用
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作者 LIU Peijin DING Haojian +3 位作者 YAN Dongyang SUN Haofeng HUANG Tao LI Jie 《Journal of Shanghai Jiaotong university(Science)》 EI 2026年第2期486-498,共13页
Aiming at the practical problems of high energy consumption and low energy efficiency during the exploitation of low-permeability oil wells because of insufficient traceability and poor matching performance of product... Aiming at the practical problems of high energy consumption and low energy efficiency during the exploitation of low-permeability oil wells because of insufficient traceability and poor matching performance of production parameters,this paper proposes a multi-objective approach for optimizing production parameters of low-permeability oil well to enhance its energy efficiency.First,a sub-model of daily liquid production yield and a sub-model of unit production energy consumption cost for single low-permeability oil well were established,and the Gaussian mixture model method was employed to compensate for the errors in the sub-model of unit production energy consumption cost,to solve the problem of the influence of uncertain facts during the oil well exploitation and to improve the precision of the model.Second,a multi-objective optimization model was established by taking into account the decision variables and constraints of the model,to maximize the daily liquid production yield while minimizing the unit production energy consumption cost.Subsequently,the non-dominated sorting genetic algorithm was employed to solve the multi-objective optimization model and obtain the production parameters.Finally,the solution set with obvious features was taken as the production parameters and applied to the actual production verification of low-permeability oil wells in a certain oil production plant of the ChangQing Oilfield.The results showed an increase in oil well production yield,and a significant energy-saving effect,thereby verifying the effectiveness of the proposed model and optimization algorithm in this paper. 展开更多
关键词 low-permeability oil well production parameter model error compensation multi-objective optimization non-dominated genetic algorithm
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Hybrid Flow Shop Rescheduling Approach Based on Hybrid-Driven Mechanism and Improved Multi-Objective WOA 认领 引用
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作者 Feng Lv Xin Xu +1 位作者 Cheng Yang Yixuan Tang 《Computers, Materials & Continua》 SCIE EI 2026年第7期1982-2009,共28页
To ensure an effective disturbance response and maintain continuous production in hybrid flow shops,this paper focuses on the design of a rescheduling method.A rescheduling model is constructed that minimizes the make... To ensure an effective disturbance response and maintain continuous production in hybrid flow shops,this paper focuses on the design of a rescheduling method.A rescheduling model is constructed that minimizes the makespan,total tardiness,and scheme deviation degree.A hybrid rescheduling driving mechanism based on the latest completion time is designed to effectively trigger rescheduling.The Whale Optimization Algorithm(WOA)is improved by integrating the good point set theory,nonlinear control parameter strategy,and Differential Evolution(DE)algorithm.Moreover,non-dominated sorting and a dynamic external archive mechanism based on crowding distance are introduced to make it suitable for multi-objective optimization problems.The superiority of the Improved Multi-objective Whale Optimization Algorithm(IMOWOA)and the effectiveness of the improved mechanisms are verified through comparative experiments and ablation experiments.Taking the final assembly production line of an agricultural machinery equipment enterprise as an example,a rescheduling scheme is generated based on the practical production requirements,which verifies the feasibility and effectiveness of the proposed method. 展开更多
关键词 Hybrid flow shop production disturbance production rescheduling rescheduling driving mechanism improved multi-objective whale optimization algorithm
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A Multi-Objective Deep Reinforcement Learning Algorithm for Computation Offloading in Internet of Vehicles 认领 引用
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作者 Junjun Ren Guoqiang Chen +1 位作者 Zheng-Yi Chai Dong Yuan 《Computers, Materials & Continua》 SCIE EI 2026年第1期2111-2136,共26页
Vehicle Edge Computing(VEC)and Cloud Computing(CC)significantly enhance the processing efficiency of delay-sensitive and computation-intensive applications by offloading compute-intensive tasks from resource-constrain... Vehicle Edge Computing(VEC)and Cloud Computing(CC)significantly enhance the processing efficiency of delay-sensitive and computation-intensive applications by offloading compute-intensive tasks from resource-constrained onboard devices to nearby Roadside Unit(RSU),thereby achieving lower delay and energy consumption.However,due to the limited storage capacity and energy budget of RSUs,it is challenging to meet the demands of the highly dynamic Internet of Vehicles(IoV)environment.Therefore,determining reasonable service caching and computation offloading strategies is crucial.To address this,this paper proposes a joint service caching scheme for cloud-edge collaborative IoV computation offloading.By modeling the dynamic optimization problem using Markov Decision Processes(MDP),the scheme jointly optimizes task delay,energy consumption,load balancing,and privacy entropy to achieve better quality of service.Additionally,a dynamic adaptive multi-objective deep reinforcement learning algorithm is proposed.Each Double Deep Q-Network(DDQN)agent obtains rewards for different objectives based on distinct reward functions and dynamically updates the objective weights by learning the value changes between objectives using Radial Basis Function Networks(RBFN),thereby efficiently approximating the Pareto-optimal decisions for multiple objectives.Extensive experiments demonstrate that the proposed algorithm can better coordinate the three-tier computing resources of cloud,edge,and vehicles.Compared to existing algorithms,the proposed method reduces task delay and energy consumption by 10.64%and 5.1%,respectively. 展开更多
关键词 Deep reinforcement learning internet of vehicles multi-objective optimization cloud-edge computing computation offloading service caching
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Multi-objective integrated cotton cultivation(MOICC):A synergistic framework for sustainable production 认领 引用
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作者 Yanjun Zhang Jianlong Dai Hezhong Dong 《Journal of Integrative Agriculture》 SCIE CAS CSCD 2026年第4期1316-1329,共14页
Global cotton production faces mounting pressure to reconcile rising fiber demand with urgent sustainability imperatives,including water scarcity mitigation,greenhouse gas reduction,and agrochemical pollution control.... Global cotton production faces mounting pressure to reconcile rising fiber demand with urgent sustainability imperatives,including water scarcity mitigation,greenhouse gas reduction,and agrochemical pollution control.Traditional practices,constrained by fragmented objectives and inherent trade-offs among yield,fiber quality,labor efficiency,and ecological impact,struggle to address these systemic challenges.Building upon previous concept of collaborative cultivation,this review for the first time introduces and comprehensively elaborates multi-objective integrated cotton cultivation(MOICC)-also referred to as integrated cotton cultivation(ICC)-a transformative framework centered on three pillars:dynamic tradeoff management(e.g.,region-specific priority adjustment),systematic technology integration(precision seeding,dense planting,chemical regulation,water-nutrient synergy,and targeted defoliation),and resource circularity(spatiotemporal optimization and waste recycling).MOICC overcomes sustainability bottlenecks by leveraging key physiological mechanisms,including ethylene signaling to enhance stress-resilient seedling establishment,jasmonate-mediated pathways to improve waterutrient efficiency,canopy light competition coupled with hormonal regulation to eliminate manual pruning,and growth regulators to concentrate boll maturation.Case studies from diverse Chinese agro-ecosystems(e.g.,Xinjiang,Yangtze/Yellow River basins)and intercropping systems demonstrate significant synergies:increased yield(8-22%),improved resource efficiency(water use efficiency increased by≥20%,and nitrogen productivity up to 35 kg kg-1),and enhanced environmental performance(labor reduction of 30-40%,carbon footprint reduction of 24-37%,and agrochemical savings:nitrogen reduction of 15-20%and pesticides reduction of 25%).Crucially,MOICC resolves core conflicts through integrated optimization:yield vs.quality(via≥70%inner-position bolls),labor-saving vs.eco-safety(precision defoliant timing),and productivity vs.emissions(root-zone nitrogen monitoring).Future research priorities include deciphering multi-scale stress adaptation,developing intelligent decision-support systems(e.g.,AHP-NSGA-II integration),advancing carbon-neutral value chains,addressing socio-economic adoption barriers,and fostering policy synergy.Overall,MOICC establishes a conceptually globally scalable pathway toward high-yield,superior-quality,resource-efficient,and ecologically sustainable cotton production,with potential applicability to other major cropping systems. 展开更多
关键词 cotton(Gossypium hirsutum L.) multi-objective integrated cultivation sustainable agriculture resource-use efficiency technology synergy
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Multi-Objective Optimization of a Tapered Cathode Flow Channel in a Proton Exchange Membrane Fuel Cell 认领 引用
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作者 Wei Dong Baoqi Guo +2 位作者 Weiwei Zhao Hui Jian Zhenzong He 《Frontiers in Heat and Mass Transfer》 EI CAS 2026年第2期115-133,共19页
This study explores the design of a tapered cathode flow channel in a proton exchange membrane fuel cell(PEMFC),leveraging artificial intelligence and multi-objective optimization techniques to attain an optimal confi... This study explores the design of a tapered cathode flow channel in a proton exchange membrane fuel cell(PEMFC),leveraging artificial intelligence and multi-objective optimization techniques to attain an optimal configuration.First,the influence of the channel height ratio and mass flow rate on PEMFC performance was systematically examined.The results reveal that decreasing the height ratio and increasing the mass flow rate lead to reduction in the standard deviation of current density,accompanied by a monotonic rise in pressure drop.The average current density initially rises before exhibiting a slight decline.Subsequently,a surrogate model based on a Backpropagation(BP)neural network was constructed,with height ratio and mass flow rate as input variables,to accurately predict the average current density,its standard deviation,and the channel pressure drop.The findings demonstrate that the BP-based surrogate model can reliably predict current density,its standard deviation,and channel pressure drop.The Mean Relative Errors(MREs)for current density,standard deviation,and pressure drop are 0.84%,1.44%,and 1.77%,respectively,with all coefficients of determination(R2)exceeding 0.999.Finally,Pareto optimal solutions for current density,standard deviation,and pressure drop of the tapered PEMFC were obtained through integration a multi-objective genetic algorithm.Results show that the optimized tapered PEMFC achieves the current density of 3141.41 A/m2,the standard deviation of 53.58 A/m2,and the channel pressure drop of 5.49 Pa.Compared with the conventional channel,the optimized PEMFC exhibits an 7.02%increase in current density and an 3.7%reduction in standard deviation,while maintaining the pressure drop within an acceptable range. 展开更多
关键词 PEMFC tapered flow channel current density multi-objective optimization
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Boundary Decision-Based Multi-Objective Robust Optimization for Microgrid Dispatching 认领 引用
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作者 Junjian Wu Jingliao Sun +2 位作者 Yejun Xiang Zhenyu Zhou Zhengchai Shi 《Energy Engineering》 EI 2026年第7期404-423,共20页
The inherent unpredictability of renewable energy generation poses significant challenges to the reliable and economic dispatch of grid-connected microgrids.In response,this paper proposes a novel robust optimization ... The inherent unpredictability of renewable energy generation poses significant challenges to the reliable and economic dispatch of grid-connected microgrids.In response,this paper proposes a novel robust optimization strategy grounded in uncertain boundary decision-making and enhanced through innovations in the multi-objective cross-entropy method.An uncertainty budget-aware environmental economic dispatch model is first established,integrating photovoltaic and wind power generation.By employing mathematical sophistication-particularly Lagrangian transformation-the proposed method effectively resolves embedded uncertainties,transforming the original model into a deterministic multi-objective optimization framework robust against renewable energy volatility.Furthermore,by incorporating the dynamic operational demands of microgrids,this paper culminates in a robust optimization approach that is both fundamentally based on and adaptively responsive to uncertainty boundaries.To address the critical challenges of convergence and diversity in multi-objective optimization,crossover operators and an adaptive parameter update mechanism are introduced,significantly refining the conventional multi-objective cross-entropy algorithm.Case studies demonstrate the rationality and effectiveness of the proposed dispatch strategy and corroborate the superior performance and applicability of the enhanced algorithm. 展开更多
关键词 Microgrid environmental economic dispatch uncertain boundary robust optimization multi-objective cross entropy algorithm
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Multi-objective trajectory optimization for spaceborne antennas with nonlinear coupling using hp-adaptive pseudospectral discretization 认领 引用
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作者 Feng GAO Guanghui SUN 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第2期517-530,共14页
Spaceborne antennas are essential for remote sensing,deep-space communication,and Earth observation,yet their trajectory planning is complicated by nonlinear base-manipulator coupling and antenna flexibility.To addres... Spaceborne antennas are essential for remote sensing,deep-space communication,and Earth observation,yet their trajectory planning is complicated by nonlinear base-manipulator coupling and antenna flexibility.To address these challenges,this paper proposes a multi-objective trajectory optimization framework.The system dynamics capture both nonlinear rigid-flexible coupling and antenna deformation through a reduced-order formulation.To enhance discretization efficiency,a predictive-terminal hp-adaptive pseudospectral method is employed,assigning collocation density based on task-phase characteristics:finer resolution is applied to dynamic segments requiring higher accuracy,especially near the terminal phase.This enables efficient transcription of the continuous-time problem into a Nonlinear Programming Problem(NLP).The resulting NLP is then solved using a multi-objective optimization strategy based on the nondominated sorting genetic algorithm II,which explores trade-offs among antenna pointing accuracy,energy consumption,and structural vibration.Numerical results demonstrate that the proposed method achieves a reduction of approximately 14.0% in control energy and 41.8%in peak actuation compared to a GPOPS-II baseline,while significantly enhancing vibration suppression.The resulting Pareto front reveals structured trade-offs and clustered solutions,offering robust and diverse options for precision,low-disturbance mission planning. 展开更多
关键词 hp-adaptive pseudospectral method Multi-objective trajectory optimization Nonlinear dynamics Rigid-flexible coupling Spaceborne antenna Structural vibration suppression
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A Stochastic Multi-Objective Framework for Wind DG Allocation and Dynamic Reconfiguration:Minimizing Losses and Enhancing Reliability with an Improved Grey Wolf Optimizer 认领 引用
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作者 Ali S.Alghamdi 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第4期704-744,共41页
The integration of wind-based DG introduces significant variability and uncertainty into the operation of distribution networks,which complicates the planning and decision-making process.This paper presents a dualobje... The integration of wind-based DG introduces significant variability and uncertainty into the operation of distribution networks,which complicates the planning and decision-making process.This paper presents a dualobjective stochastic optimization framework for the optimal allocation of wind DG,considering dynamic network reconfiguration across multiple loading conditions.Probabilistic modeling of wind speed is integrated using the Weibull distribution and the associated wind power uncertainty is discretized through a scenario-based point estimation method.Variability in load is accounted for by considering multiple loading levels,and the integrated uncertainty space is constructed as the Cartesian product of wind scenarios and load profiles.The optimization seeks to minimize the total energy losses together with the enhancement of reliability,quantified through the expected energy not supplied.For the solution of the complex,nonlinear,multi-objective problem,the Improved Multi-Objective Grey Wolf Optimizer(I-MGWO)is developed,including quasi-oppositional population seeding,adaptive stochastic coeficient strategy,and dynamic convex combination position update.Simulation results on the IEEE 33-bus system demonstrate that the proposed integrated strategy of simultaneous wind DG allocation and network reconfiguration gives synergistic improvements,yielding up to 55.7%reduction in energy losses,and a reduction of up to 61.4%in EENS over the base case.In both convergence speed and solution quality,I-MGWO consistently outperforms conventional algorithms and gives a robust and computationally efficient tool for distribution system planning under uncertainty. 展开更多
关键词 Wind power generation distributed generation allocation active distribution network network reconfiguration uncertainty modeling stochastic optimization multi-objective optimization reliability assessment Grey Wolf Optimizer
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Multi-objective constrained tilt corridor and transition trajectory strategy of a compound tilt-rotor UAV 认领 引用
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作者 Zhuang LIANG Xixiang YANG +2 位作者 Duoneng LIU Zhongxi HOU Li FAN 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第4期393-411,共19页
Additional tilting mechanisms in tilt-rotor expand the control dimensions of the aircraft,thereby augmenting the complexity of the overall control system,particularly during the transition phase.This study examines th... Additional tilting mechanisms in tilt-rotor expand the control dimensions of the aircraft,thereby augmenting the complexity of the overall control system,particularly during the transition phase.This study examines the dynamic model,flight corridor for tilt operations,and trajectory planning relevant to a Distributed Compound Tiltrotor(DCT)and proposes a transition strategy for the transition corridor based on multi-objective constraints.In the modeling section,the stall effect of the tilt wing of the DCT under large tilt angles is considered.To overcome the limitation of traditional Static Tilt Corridors(STC),which fail to account for the dynamic constraints during the aircraft's tilting process,a Multi-objective Constrained Tilt Corridor(MCTC)is proposed.This method is constrained by dynamic imbalance rules and power limitations,extending the reverse transition tilt angle to 120°,offering a more precise representation of the variation in the feasible domain during both forward and backward transitions of the DCT.Based on the extended MCTC,a transition trajectory planning strategy is developed by extracting the key features of the optimal trajectory.The design problem for the transition strategy is then formulated as a constrained optimal control problem.Simulation results demonstrate that this strategy maintains the transition trajectory within the middle of the MCTC and reduces the backward transition time by 5.2 s compared to traditional methods,improving both the robustness and safety of the transition.Finally,a trajectory tracking controller has been developed to evaluate the effectiveness of the transition strategy proposed in this paper. 展开更多
关键词 Distributed compound aircraft Multi-objective optimization Tilt corridor Tilt-rotor Transition control
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Knee point-guided heterogeneous surrogate-assisted optimization for multi-objective coal gasification system 认领 引用
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作者 Wenlu Li Nan Guo +5 位作者 Tiewei Shang Yueyang Sun Dapeng Li Xiaolong Gao Wei Xiong Junfei Qiao 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2026年第2期168-180,共13页
Coal gasificationtechnology plays a pivotal role in chemical production as a key process for efficiently converting coal into liquid fuels and chemical feedstocks.During gasification,high-temperature reactions generat... Coal gasificationtechnology plays a pivotal role in chemical production as a key process for efficiently converting coal into liquid fuels and chemical feedstocks.During gasification,high-temperature reactions generate syngas,and optimizing its operational parameters is essential for improving syngas quality,carbon efficiencyand liquid fuel yield.However,the intricate chemical reactions and heat transfer mechanisms in gasificationnecessitate costly simulations or experimental testing,making it an expensive multi-objective optimization problem.To address this challenge,this paper proposes a Knee Point-guided Heterogeneous Surrogate-assisted Evolutionary Algorithm(KG-HSEA)that integrates Kriging and Feedforward Neural Networks(FNN)to construct a heterogeneous surrogate model,leveraging their complementary strengths to reduce computational costs while maintaining predictive accuracy.By incorporating a knee point-guided search mechanism,the method prioritizes solutions that embody critical trade-offs among conflictingobjectives.Moreover,an adaptive sampling strategy combined with dual-archive management is employed to dynamically update the surrogate model,ensuring it adapts to unstable operating conditions while maintaining robust convergence-diversity balance in coal gasificationprocesses.Experimental results show that KG-HSEA achieved a 71.9% superiority rate with 23 optimal solutions out of 32 benchmark problems,highlighting its potential for efficientand feasible coal gasificationoptimization. 展开更多
关键词 Coal gasification Expensive multi-objective optimization Kriging Feedforward neural network(FNN) Knee point Model management
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Thermodynamic coupling mechanisms in precooled turbine-based combined cycle:From fuel properties to multi-objective performance characterization 认领 引用
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作者 Yusen XU Yifan WANG +4 位作者 Huoxing LIU Kun HE Jun ZENG Qinqin WANG Qinghui MA 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第6期162-179,共18页
The Precooled Turbine-Based Combined Cycle(PCTBCC)synergistically integrates precooled turbine engine with bypass ramjet,establishing itself as a pivotal hypersonic propulsion solution.This study systematically analyz... The Precooled Turbine-Based Combined Cycle(PCTBCC)synergistically integrates precooled turbine engine with bypass ramjet,establishing itself as a pivotal hypersonic propulsion solution.This study systematically analyzes the thermodynamic parameters coupling mechanisms and optimizes the precooling strategy of PCTBCC,demonstrating a 33.6%improvement in specific impulse.Based on unified parametric benchmarks,the critical Mach numbers and propulsion performance of methane,hydrogen,ammonia,and methanol fuels are evaluated,which quantitatively elucidates the impact mechanisms of fuel thermophysical properties on propulsion performance.The results reveal that PCTBCC exhibits a 45.2%performance advantage over ramjets.Furthermore,this research establishes mapping relationships between fuel combinations'thermophysical properties and propulsion performance/storage requirements,delineating the theoretical performance ceiling for multi-fuel PCTBCC.The results indicate that specific fuel combinations achieve a 25%reduction in storage volume compared to pure hydrogen while maintaining balanced performance parameters,including a critical Mach number of 4.03 and a specific impulse of 2191 s. 展开更多
关键词 Fuel thermophysical properties Hypersonic vehicles Multi-objective parameter optimization Precooled turbine-based combined cycle Propulsion
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