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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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A Fuzzy Multi-Objective Framework for Energy Optimization and Reliable Routing in Wireless Sensor Networks via Particle Swarm Optimization 认领 引用 被引量：1
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作者 Medhat A.Tawfeek Ibrahim Alrashdi +1 位作者 Madallah Alruwaili Fatma M.Talaat 《Computers, Materials & Continua》 SCIE EI 2025年第5期2773-2792,共20页
Wireless Sensor Networks(WSNs)are one of the best technologies of the 21st century and have seen tremendous growth over the past decade.Much work has been put into its development in various aspects such as architectu... Wireless Sensor Networks(WSNs)are one of the best technologies of the 21st century and have seen tremendous growth over the past decade.Much work has been put into its development in various aspects such as architectural attention,routing protocols,location exploration,time exploration,etc.This research aims to optimize routing protocols and address the challenges arising from conflicting objectives in WSN environments,such as balancing energy consumption,ensuring routing reliability,distributing network load,and selecting the shortest path.Many optimization techniques have shown success in achieving one or two objectives but struggle to achieve the right balance between multiple conflicting objectives.To address this gap,this paper proposes an innovative approach that integrates Particle Swarm Optimization(PSO)with a fuzzy multi-objective framework.The proposed method uses fuzzy logic to effectively control multiple competing objectives to represent its major development beyond existing methods that only deal with one or two objectives.The search efficiency is improved by particle swarm optimization(PSO)which overcomes the large computational requirements that serve as a major drawback of existing methods.The PSO algorithm is adapted for WSNs to optimize routing paths based on fuzzy multi-objective fitness.The fuzzy logic framework uses predefined membership functions and rule-based reasoning to adjust routing decisions.These adjustments influence PSO’s velocity updates,ensuring continuous adaptation under varying network conditions.The proposed multi-objective PSO-fuzzy model is evaluated using NS-3 simulation.The results show that the proposed model is capable of improving the network lifetime by 15.2%–22.4%,increasing the stabilization time by 18.7%–25.5%,and increasing the residual energy by 8.9%–16.2% compared to the state-of-the-art techniques.The proposed model also achieves a 15%–24% reduction in load variance,demonstrating balanced routing and extended network lifetime.Furthermore,analysis using p-values obtained from multiple performance measures(p-values<0.05)showed that the proposed approach outperforms with a high level of confidence.The proposed multi-objective PSO-fuzzy model provides a robust and scalable solution to improve the performance of WSNs.It allows stable performance in networks with 100 to 300 nodes,under varying node densities,and across different base station placements.Computational complexity analysis has shown that the method fits well into large-scale WSNs and that the addition of fuzzy logic controls the power usage to make the system practical for real-world use. 展开更多
关键词 Wireless sensor networks particle swarm optimization fuzzy multi-objective framework routing stability
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A Multi-Objective Adaptive Car-Following Framework for Autonomous Connected Vehicles with Deep Reinforcement Learning 认领 引用
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作者 Abu Tayab Yanwen Li +5 位作者 Ahmad Syed Ghanshyam G.Tejani Doaa Sami Khafaga El-Sayed M.El-kenawy Amel Ali Alhussan Marwa M.Eid 《Computers, Materials & Continua》 SCIE EI 2026年第2期1311-1337,共27页
Autonomous connected vehicles(ACV)involve advanced control strategies to effectively balance safety,efficiency,energy consumption,and passenger comfort.This research introduces a deep reinforcement learning(DRL)-based... Autonomous connected vehicles(ACV)involve advanced control strategies to effectively balance safety,efficiency,energy consumption,and passenger comfort.This research introduces a deep reinforcement learning(DRL)-based car-following(CF)framework employing the Deep Deterministic Policy Gradient(DDPG)algorithm,which integrates a multi-objective reward function that balances the four goals while maintaining safe policy learning.Utilizing real-world driving data from the highD dataset,the proposed model learns adaptive speed control policies suitable for dynamic traffic scenarios.The performance of the DRL-based model is evaluated against a traditional model predictive control-adaptive cruise control(MPC-ACC)controller.Results show that theDRLmodel significantly enhances safety,achieving zero collisions and a higher average time-to-collision(TTC)of 8.45 s,compared to 5.67 s for MPC and 6.12 s for human drivers.For efficiency,the model demonstrates 89.2% headway compliance and maintains speed tracking errors below 1.2 m/s in 90% of cases.In terms of energy optimization,the proposed approach reduces fuel consumption by 5.4% relative to MPC.Additionally,it enhances passenger comfort by lowering jerk values by 65%,achieving 0.12 m/s3 vs.0.34 m/s3 for human drivers.A multi-objective reward function is integrated to ensure stable policy convergence while simultaneously balancing the four key performance metrics.Moreover,the findings underscore the potential of DRL in advancing autonomous vehicle control,offering a robust and sustainable solution for safer,more efficient,and more comfortable transportation systems. 展开更多
关键词 Car-following model DDPG multi-objective framework autonomous connected vehicles
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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 Optimisation Framework for Heterogeneous Federated Learning 认领 引用
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作者 Jamshid Tursunboev Vikas Palakonda +2 位作者 Il-Min Kim Sunghwan Moon Jae-Mo Kang 《CAAI Transactions on Intelligence Technology》 SCIE EI CSCD 2026年第1期1-14,共14页
Federated learning is a distributed framework that trains a centralised model using data from multiple clients without transferring that data to a central server.Despite rapid progress,federated learning still faces s... Federated learning is a distributed framework that trains a centralised model using data from multiple clients without transferring that data to a central server.Despite rapid progress,federated learning still faces several unsolved challenges.Specifically,communication costs and system heterogeneity,such as nonidentical data distribution,hinder federated learning's progress.Several approaches have recently emerged for federated learning involving heterogeneous clients with varying computational capabilities(namely,heterogeneous federated learning).However,heterogeneous federated learning faces two key challenges:optimising model size and determining client selection ratios.Moreover,efficiently aggregating local models from clients with diverse capabilities is crucial for addressing system heterogeneity and communication efficiency.This paper proposes an evolutionary multiobjective optimisation framework for heterogeneous federated learning(MOHFL)to address these issues.Our approach elegantly formulates and solves a biobjective optimisation problem that minimises communication cost and model error rate.The decision variables in this framework comprise model sizes and client selection ratios for each Q client cluster,yielding a total of 2×Q optimisation parameters to be tuned.We develop a partition-based strategy for MOHFL that segregates clients into clusters based on their communication and computation capabilities.Additionally,we implement an adaptive model sizing mechanism that dynamically assigns appropriate subnetwork architectures to clients based on their computational constraints.We also propose a unified aggregation framework to combine models of varying sizes from heterogeneous clients effectively.Extensive experiments on multiple datasets demonstrate the effectiveness and superiority of our proposed method compared to existing approaches. 展开更多
关键词 deep learning learning(artificial intelligence) learning models multi-objective optimisation
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Multi-Objective Evolutionary Framework for High-Precision Community Detection in Complex Networks 认领 引用
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作者 Asal Jameel Khudhair Amenah Dahim Abbood 《Computers, Materials & Continua》 SCIE EI 2026年第1期1453-1483,共31页
Community detection is one of the most fundamental applications in understanding the structure of complicated networks.Furthermore,it is an important approach to identifying closely linked clusters of nodes that may r... Community detection is one of the most fundamental applications in understanding the structure of complicated networks.Furthermore,it is an important approach to identifying closely linked clusters of nodes that may represent underlying patterns and relationships.Networking structures are highly sensitive in social networks,requiring advanced techniques to accurately identify the structure of these communities.Most conventional algorithms for detecting communities perform inadequately with complicated networks.In addition,they miss out on accurately identifying clusters.Since single-objective optimization cannot always generate accurate and comprehensive results,as multi-objective optimization can.Therefore,we utilized two objective functions that enable strong connections between communities and weak connections between them.In this study,we utilized the intra function,which has proven effective in state-of-the-art research studies.We proposed a new inter-function that has demonstrated its effectiveness by making the objective of detecting external connections between communities is to make them more distinct and sparse.Furthermore,we proposed a Multi-Objective community strength enhancement algorithm(MOCSE).The proposed algorithm is based on the framework of the Multi-Objective Evolutionary Algorithm with Decomposition(MOEA/D),integrated with a new heuristic mutation strategy,community strength enhancement(CSE).The results demonstrate that the model is effective in accurately identifying community structures while also being computationally efficient.The performance measures used to evaluate the MOEA/D algorithm in our work are normalized mutual information(NMI)and modularity(Q).It was tested using five state-of-the-art algorithms on social networks,comprising real datasets(Zachary,Dolphin,Football,Krebs,SFI,Jazz,and Netscience),as well as twenty synthetic datasets.These results provide the robustness and practical value of the proposed algorithm in multi-objective community identification. 展开更多
关键词 Multi-objective optimization evolutionary algorithms community detection heuristic metaheuristic hybrid social network models
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Dynamic decision-making of UAV swarm based on constrained multi-objective optimization under incomplete interference information 认领 引用 被引量：1
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作者 Kuixian LI Jinjie LIU +5 位作者 Xin GU Yandie YANG Cheng CHANG Haipeng CHEN Liangtian WAN Yun LIN 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第7期64-77,共14页
The decision-making and resource allocation of UAV swarms play a crucial role in dynamic,uncertain environments.In such complex scenarios,UAV swarms need to effectively collaborate and communicate in frequently changi... The decision-making and resource allocation of UAV swarms play a crucial role in dynamic,uncertain environments.In such complex scenarios,UAV swarms need to effectively collaborate and communicate in frequently changing interference conditions.However,existing resource allocation methods typically assume complete interference information or are suitable only for static environments,leading to significant performance degradation in the face of external uncertainties and incomplete information.To address these challenges,this paper employs fuzzy set theory to dynamically model the uncertainty of external interference and defuzzify its impact on the available frequency bands during iterative diagnostics.Additionally,a dynamic constrained multi-objective optimization model is developed,and a novel Dynamic Constrained MultiObjective Evolutionary Algorithm based on Transfer Search(TrS-DCMOEA)is proposed.By integrating transfer learning and dynamic adjustment strategies,the algorithm quickly adapts to environmental changes,ensuring communication performance while maintaining the security of UAV swarm communications.Simulation results show that the proposed algorithm achieves superior decision-making and resource allocation efficiency in most time slots,with TrS-DCMOEA particularly excelling in tracking the Pareto front in dynamic environments. 展开更多
关键词 UAV swarm Resource allocation Dynamic constraints Incomplete information Dynamic decision-making Multi-objective optimization
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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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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://gffzz188fe103f8f1460as6nb5kouqpuun6wkq.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 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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Toward efficient multi-objective seismic design optimization of self-centering bridges using machine learning 认领 引用
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作者 Xueqi ZHONG Lintao TANG +1 位作者 Xiangnan LI Liuyang LI 《ENGINEERING Structure and Civil Engineering》 SCIE EI CAS CSCD 2026年第3期461-481,共21页
Self-centering rocking bridge piers,characterized by their minimal residual deformation and rapid postseismic recovery,have emerged as a promising solution for enhancing the seismic resilience of bridge systems.Howeve... Self-centering rocking bridge piers,characterized by their minimal residual deformation and rapid postseismic recovery,have emerged as a promising solution for enhancing the seismic resilience of bridge systems.However,their inherently nonlinear behavior and pronounced sensitivity to multiple interdependent design parameters make it challenging to achieve balanced seismic performance among all piers within an integrated bridge system.This work develops a system-oriented optimization framework for self-centering rocking bridges to address this issue.The proposed framework integrates machine learning-based surrogate modeling to markedly accelerate the optimization process.A detailed case study of a four-span self-centering rocking bridge is conducted to demonstrate the framework’s applicability and effectiveness.Results show that substituting traditional finite element model with an XGBoost-based surrogate model reduces computational time by 92%while preserving high predictive accuracy.Furthermore,the optimized design significantly enhances system-level performance uniformity,achieving a 52.3%reduction in inter-pier shear force variability and a 19.0%decrease in displacement disparity compared with the baseline configuration. 展开更多
关键词 self-centering rocking bridge machine learning seismic design multi-objective optimization nonlinear dynamics multi-criteria decision-making
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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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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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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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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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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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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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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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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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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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