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Back analysis of rock mass parameters in mechanized twin tunnels based on coupled auto machine learning and multi-objective optimization algorithm 认领 引用 被引量：1
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作者 Chengwen Wang Xiaoli Liu +4 位作者 Jiubao Li Enzhi Wang Nan Hu Wenli Yao Zhihui He 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2025年第11期7038-7055,共18页
Accurate determination of rock mass parameters is essential for ensuring the accuracy of numericalsimulations. Displacement back-analysis is the most widely used method;however, the reliability of thecurrent approache... Accurate determination of rock mass parameters is essential for ensuring the accuracy of numericalsimulations. Displacement back-analysis is the most widely used method;however, the reliability of thecurrent approaches remains unsatisfactory. Therefore, in this paper, a multistage rock mass parameterback-analysis method, that considers the construction process and displacement losses is proposed andimplemented through the coupling of numerical simulation, auto-machine learning (AutoML), andmulti-objective optimization algorithms (MOOAs). First, a parametric modeling platform for mechanizedtwin tunnels is developed, generating a dataset through extensive numerical simulations. Next, theAutoML method is utilized to establish a surrogate model linking rock parameters and displacements.The tunnel construction process is divided into multiple stages, transforming the rock mass parameterback-analysis into a multi-objective optimization problem, for which multi-objective optimization algorithmsare introduced to obtain the rock mass parameters. The newly proposed rock mass parameterback-analysis method is validated in a mechanized twin tunnel project, and its accuracy and effectivenessare demonstrated. Compared with traditional single-stage back-analysis methods, the proposedmodel decreases the average absolute percentage error from 12.73% to 4.34%, significantly improving theaccuracy of the back-analysis. Moreover, although the accuracy of back analysis significantly increaseswith the number of construction stages considered, the back analysis time is acceptable. This studyprovides a new method for displacement back analysis that is efficient and accurate, thereby paving theway for precise parameter determination in numerical simulations. 展开更多
关键词 Back analysis of rock parameters Auto machine learning Multi-objective optimization algorithm Mechanized twin tunnels Parametric modeling
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A decoupled multi-objective optimization algorithm for cut order planning of multi-color garment 认领 引用
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作者 DONG Hui LYU Jinyang +3 位作者 LIN Wenjie WU Xiang WU Mincheng HUANG Guangpu 《High Technology Letters》 EI CAS 2025年第1期53-62,共10页
This work addresses the cut order planning(COP)problem for multi-color garment production,which is the first step in the clothing industry.First,a multi-objective optimization model of multicolor COP(MCOP)is establish... This work addresses the cut order planning(COP)problem for multi-color garment production,which is the first step in the clothing industry.First,a multi-objective optimization model of multicolor COP(MCOP)is established with production error and production cost as optimization objectives,combined with constraints such as the number of equipment and the number of layers.Second,a decoupled multi-objective optimization algorithm(DMOA)is proposed based on the linear programming decoupling strategy and non-dominated sorting in genetic algorithmsⅡ(NSGAII).The size-combination matrix and the fabric-layer matrix are decoupled to improve the accuracy of the algorithm.Meanwhile,an improved NSGAII algorithm is designed to obtain the optimal Pareto solution to the MCOP problem,thereby constructing a practical intelligent production optimization algorithm.Finally,the effectiveness and superiority of the proposed DMOA are verified through practical cases and comparative experiments,which can effectively optimize the production process for garment enterprises. 展开更多
关键词 multi-objective optimization non-dominated sorting in genetic algorithmsⅡ(NSGAII) cut order planning(COP) multi-color garment linear programming decoupling strategy
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Large-Scale Multi-Objective Optimization Algorithm Based on Weighted Overlapping Grouping of Decision Variables 认领 引用 被引量：1
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作者 Liang Chen Jingbo Zhang +2 位作者 Linjie Wu Xingjuan Cai Yubin Xu 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第7期363-383,共21页
The large-scale multi-objective optimization algorithm(LSMOA),based on the grouping of decision variables,is an advanced method for handling high-dimensional decision variables.However,in practical problems,the intera... The large-scale multi-objective optimization algorithm(LSMOA),based on the grouping of decision variables,is an advanced method for handling high-dimensional decision variables.However,in practical problems,the interaction among decision variables is intricate,leading to large group sizes and suboptimal optimization effects;hence a large-scale multi-objective optimization algorithm based on weighted overlapping grouping of decision variables(MOEAWOD)is proposed in this paper.Initially,the decision variables are perturbed and categorized into convergence and diversity variables;subsequently,the convergence variables are subdivided into groups based on the interactions among different decision variables.If the size of a group surpasses the set threshold,that group undergoes a process of weighting and overlapping grouping.Specifically,the interaction strength is evaluated based on the interaction frequency and number of objectives among various decision variables.The decision variable with the highest interaction in the group is identified and disregarded,and the remaining variables are then reclassified into subgroups.Finally,the decision variable with the strongest interaction is added to each subgroup.MOEAWOD minimizes the interactivity between different groups and maximizes the interactivity of decision variables within groups,which contributed to the optimized direction of convergence and diversity exploration with different groups.MOEAWOD was subjected to testing on 18 benchmark large-scale optimization problems,and the experimental results demonstrate the effectiveness of our methods.Compared with the other algorithms,our method is still at an advantage. 展开更多
关键词 Decision variable grouping large-scale multi-objective optimization algorithms weighted overlapping grouping direction-guided evolution
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An improved multi-objective optimization algorithm for solving flexible job shop scheduling problem with variable batches 认领 引用 被引量：5
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作者 WU Xiuli PENG Junjian +2 位作者 XIE Zirun ZHAO Ning WU Shaomin 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2021年第2期272-285,共14页
In order to solve the flexible job shop scheduling problem with variable batches,we propose an improved multiobjective optimization algorithm,which combines the idea of inverse scheduling.First,a flexible job shop pro... In order to solve the flexible job shop scheduling problem with variable batches,we propose an improved multiobjective optimization algorithm,which combines the idea of inverse scheduling.First,a flexible job shop problem with the variable batches scheduling model is formulated.Second,we propose a batch optimization algorithm with inverse scheduling in which the batch size is adjusted by the dynamic feedback batch adjusting method.Moreover,in order to increase the diversity of the population,two methods are developed.One is the threshold to control the neighborhood updating,and the other is the dynamic clustering algorithm to update the population.Finally,a group of experiments are carried out.The results show that the improved multi-objective optimization algorithm can ensure the diversity of Pareto solutions effectively,and has effective performance in solving the flexible job shop scheduling problem with variable batches. 展开更多
关键词 flexible job shop variable batch inverse scheduling multi-objective evolutionary algorithm based on decomposition a batch optimization algorithm with inverse scheduling
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Multi-Objective Optimization Algorithm for Grouping Decision Variables Based on Extreme Point Pareto Frontier 认领 引用 被引量：1
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作者 JunWang Linxi Zhang +4 位作者 Hao Zhang Funan Peng Mohammed A.El-Meligy Mohamed Sharaf Qiang Fu 《Computers, Materials & Continua》 SCIE EI 2024年第4期1281-1299,共19页
The existing algorithms for solving multi-objective optimization problems fall into three main categories:Decomposition-based,dominance-based,and indicator-based.Traditional multi-objective optimization problemsmainly... The existing algorithms for solving multi-objective optimization problems fall into three main categories:Decomposition-based,dominance-based,and indicator-based.Traditional multi-objective optimization problemsmainly focus on objectives,treating decision variables as a total variable to solve the problem without consideringthe critical role of decision variables in objective optimization.As seen,a variety of decision variable groupingalgorithms have been proposed.However,these algorithms are relatively broad for the changes of most decisionvariables in the evolution process and are time-consuming in the process of finding the Pareto frontier.To solvethese problems,a multi-objective optimization algorithm for grouping decision variables based on extreme pointPareto frontier(MOEA-DV/EPF)is proposed.This algorithm adopts a preprocessing rule to solve the Paretooptimal solution set of extreme points generated by simultaneous evolution in various target directions,obtainsthe basic Pareto front surface to determine the convergence effect,and analyzes the convergence and distributioneffects of decision variables.In the later stages of algorithm optimization,different mutation strategies are adoptedaccording to the nature of the decision variables to speed up the rate of evolution to obtain excellent individuals,thusenhancing the performance of the algorithm.Evaluation validation of the test functions shows that this algorithmcan solve the multi-objective optimization problem more efficiently. 展开更多
关键词 Multi-objective evolutionary optimization algorithm decision variables grouping extreme point pareto frontier
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Cooperative task allocation for heterogeneous multi-UAV using multi-objective optimization algorithm 认领 引用 被引量：41
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作者 WANG Jian-feng JIA Gao-wei +1 位作者 LIN Jun-can HOU Zhong-xi 《Journal of Central South University》 SCIE EI CAS CSCD 2020年第2期432-448,共17页
The application of multiple UAVs in complicated tasks has been widely explored in recent years.Due to the advantages of flexibility,cheapness and consistence,the performance of heterogeneous multi-UAVs with proper coo... The application of multiple UAVs in complicated tasks has been widely explored in recent years.Due to the advantages of flexibility,cheapness and consistence,the performance of heterogeneous multi-UAVs with proper cooperative task allocation is superior to over the single UAV.Accordingly,several constraints should be satisfied to realize the efficient cooperation,such as special time-window,variant equipment,specified execution sequence.Hence,a proper task allocation in UAVs is the crucial point for the final success.The task allocation problem of the heterogeneous UAVs can be formulated as a multi-objective optimization problem coupled with the UAV dynamics.To this end,a multi-layer encoding strategy and a constraint scheduling method are designed to handle the critical logical and physical constraints.In addition,four optimization objectives:completion time,target reward,UAV damage,and total range,are introduced to evaluate various allocation plans.Subsequently,to efficiently solve the multi-objective optimization problem,an improved multi-objective quantum-behaved particle swarm optimization(IMOQPSO)algorithm is proposed.During this algorithm,a modified solution evaluation method is designed to guide algorithmic evolution;both the convergence and distribution of particles are considered comprehensively;and boundary solutions which may produce some special allocation plans are preserved.Moreover,adaptive parameter control and mixed update mechanism are also introduced in this algorithm.Finally,both the proposed model and algorithm are verified by simulation experiments. 展开更多
关键词 unmanned aerial vehicles cooperative task allocation heterogeneous constraint multi-objective optimization solution evaluation method
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Interactive Evolutionary Multi-Objective Optimization Algorithm Using Cone Dominance 认领 引用
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作者 Dalaijargal Purevsuren Saif ur Rehman +2 位作者 Gang Cui Jianmin Bao Nwe Nwe Htay Win 《Journal of Harbin Institute of Technology(New Series)》 EI CAS 2015年第6期76-84,共9页
As the number of objectives increases,the performance of the Pareto dominance-based Evolutionary Multi-objective Optimization( EMO) algorithms such as NSGA-II,SPEA2 severely deteriorates due to the drastic increase in... As the number of objectives increases,the performance of the Pareto dominance-based Evolutionary Multi-objective Optimization( EMO) algorithms such as NSGA-II,SPEA2 severely deteriorates due to the drastic increase in the Pareto-incomparable solutions. We propose a sorting method which classifies these incomparable solutions into several ordered classes by using the decision maker's( DM) preference information.This is accomplished by designing an interactive evolutionary algorithm and constructing convex cones. This method allows the DMs to drive the search process toward a preferred region of the Pareto optimal front. The performance of the proposed algorithm is assessed for two,three,and four-objective knapsack problems. The results demonstrate the algorithm ' s ability to converge to the most preferred point. The evaluation and comparison of the results indicate that the proposed approach gives better solutions than that of NSGA-II. In addition,the approach is more efficient compared to NSGA-II in terms of the number of generations required to reach the preferred point. 展开更多
关键词 multi-objective optimization evolutionary optimization preference information pareto dominance cone dominance
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A performance comparison of multi-objective optimization algorithms for solving nearly-zero-energy-building design problems 认领 引用 被引量：9
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作者 Mohamed Hamdy Anh-Tuan Nguyen +1 位作者 Jan L.M. Hensen 侯恩哲 《建筑节能》 2016年第6期4-4,共1页
Integrated building design is inherently a multi-objective optimization problem where two or more conflicting objectives must be minimized and/or maximized concurrently.Many multi-objective optimization algorithms hav... Integrated building design is inherently a multi-objective optimization problem where two or more conflicting objectives must be minimized and/or maximized concurrently.Many multi-objective optimization algorithms have been developed;however few of them are tested in solving building design problems.This paper compares performance of seven commonly-used multi-objective evolutionary optimization algorithms in solving the design problem of a nearly zero energy building(n ZEB) where more than 1.610 solutions would be possible.The compared algorithms include a controlled non-dominated sorting genetic algorithm witha passive archive(p NSGA-II),a multi-objective particle swarm optimization(MOPSO),a two-phase optimization using the genetic algorithm(PR_GA),an elitist non-dominated sorting evolution strategy(ENSES),a multi-objective evolutionary algorithm based on the concept of epsilon dominance(ev MOGA),a multi-objective differential evolution algorithm(sp MODE-II),and a multi-objective dragonfly algorithm(MODA).Several criteria was used to compare performance of these algorithms.In most cases,the quality of the obtained solutions was improved when the number of generations was increased.The optimization results of running each algorithm20 times with gradually increasing number of evaluations indicated that the PR_GA algorithm had a high repeatability to explore a large area of the solution-space and achieved close-to-optimal solutions with a good diversity,followed by the p NSGA-II,ev MOGA and sp MODE-II.Uncompetitive results were achieved by the ENSES,MOPSO and MODA in most running cases.The study also found that 1400-1800 were minimum required number of evaluations to stabilize optimization results of the building energy model. 展开更多
关键词 Multi-objective optimization Algorithms Experimentation Building simulation Comparison
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MGOKA:A Multi-Objective Optimization Algorithm for Controller Placement Problem Combining Network Partition with Cluster Fusion in Software Defined Network 认领 引用
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作者 CHEN Jue XIAO Changwei +1 位作者 QIU Xihe LÜ Wenjing 《Wuhan University Journal of Natural Sciences》 CAS CSCD 2024年第6期589-599,共11页
Software Defined Network(SDN)has been developed rapidly in technology and popularized in application due to its efficiency and flexibility in network management.In multi-controller SDN architecture,the Controller Plac... Software Defined Network(SDN)has been developed rapidly in technology and popularized in application due to its efficiency and flexibility in network management.In multi-controller SDN architecture,the Controller Placement Problem(CPP)must be solved carefully as it directly affects the whole network performance.This paper proposes a Multi-objective Greedy Optimized K-means Algorithm(MGOKA)to solve this problem to optimize worst-case and average delay between switches and controllers as well as synchronization delay and load balance among controllers for Wide Area Networks(WAN).MGOKA combines the process of network partition based on the K-means algorithm with cluster fusion based on the greedy algorithm and designs a normalization strategy to convert a multi-objective into a single-objective optimization problem.The simulation results depict that in different network scales with different numbers of controllers,the relative optimization rate of our proposed algorithm compared with K-means,K-means++,and GOKA can reach up to 101.5%,109.9%,and 79.8%,respectively.Moreover,the error rate between MGOKA and the global optimal solution is always less than 4%. 展开更多
关键词 Software Defined Network Controller Placement Problem propagation delay load balance multi-objective optimization
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Quantum-Inspired Optimization Algorithm for 3D Multi-Objective Base-Station Deployment in Next-Generation 5G/6G Wireless Network 认领 引用
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作者 Yao-Hsin Chou Cheng-Yen Hua +1 位作者 Ru-Wei Tseng Shu-Yu Kuo 《Computers, Materials & Continua》 SCIE EI 2026年第5期981-996,共16页
The rapid growth of mobile and Internet of Things(IoT)applications in dense urban environments places stringent demands on future Beyond 5G(B5G)or Beyond 6G(B6G)networks,which must ensure high Quality of Service(QoS)w... The rapid growth of mobile and Internet of Things(IoT)applications in dense urban environments places stringent demands on future Beyond 5G(B5G)or Beyond 6G(B6G)networks,which must ensure high Quality of Service(QoS)while maintaining cost-efficiency and sustainable deployment.Traditional strategies struggle with complex 3D propagation,building penetration loss,and the balance between coverage and infrastructure cost.To address this challenge,this study presents the first application of a Global-best Guided Quantum-inspired Tabu Search with Quantum-Not Gate(GQTS-QNG)framework for 3D base-station deployment optimization.The problem is formulated as a multi-objective model that simultaneously maximizes coverage and minimizes deployment cost.A binary-to-decimal encodingmechanism is designed to represent discrete placement coordinates and base station types,leveraging a quantum-inspired method to efficiently search and refine solutions within challenging combinatorial environments.Global-best guidance and tabu memory are integrated to strengthen convergence stability and avoid revisiting previously explored solutions.Simulation results across user densities ranging from 1000 to 10,000 show that GQTS-QNG consistently finds deployment configurations achieving full coverage while reducing deployment cost compared with the state-of-the-art algorithms under equal iteration times.Additionally,our method generates welldistributed and structured Pareto fronts,offering diverse planning options that allow operators to flexibly balance cost and performance requirements.These findings demonstrate that GQTS-QNG is a scalable and efficient algorithm for sustainable 3D cellular network deployment in B5G/6G urban scenarios. 展开更多
关键词 3D network deployment quantum-inspired optimization B5G/6G multi-objective optimization coverage deployment cost urban wireless 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://gffzz188fe103f8f1460askcwqxbkf59xx6f5o.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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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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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 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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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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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 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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Constraint Intensity-Driven Evolutionary Multitasking for Constrained Multi-Objective Optimization 认领 引用
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作者 Leyu Zheng Mingming Xiao +2 位作者 Yi Ren Ke Li Chang Sun 《Computers, Materials & Continua》 SCIE EI 2026年第3期1241-1261,共21页
In a wide range of engineering applications,complex constrained multi-objective optimization problems(CMOPs)present significant challenges,as the complexity of constraints often hampers algorithmic convergence and red... In a wide range of engineering applications,complex constrained multi-objective optimization problems(CMOPs)present significant challenges,as the complexity of constraints often hampers algorithmic convergence and reduces population diversity.To address these challenges,we propose a novel algorithm named Constraint IntensityDriven Evolutionary Multitasking(CIDEMT),which employs a two-stage,tri-task framework to dynamically integrates problem structure and knowledge transfer.In the first stage,three cooperative tasks are designed to explore the Constrained Pareto Front(CPF),the Unconstrained Pareto Front(UPF),and theε-relaxed constraint boundary,respectively.A CPF-UPF relationship classifier is employed to construct a problem-type-aware evolutionary strategy pool.At the end of the first stage,each task selects strategies from this strategy pool based on the specific type of problem,thereby guiding the subsequent evolutionary process.In the second stage,while each task continues to evolve,aτ-driven knowledge transfer mechanism is introduced to selectively incorporate effective solutions across tasks.enhancing the convergence and feasibility of the main task.Extensive experiments conducted on 32 benchmark problems from three test suites(LIRCMOP,DASCMOP,and DOC)demonstrate that CIDEMT achieves the best Inverted Generational Distance(IGD)values on 24 problems and the best Hypervolume values(HV)on 22 problems.Furthermore,CIDEMT significantly outperforms six state-of-the-art constrained multi-objective evolutionary algorithms(CMOEAs).These results confirm CIDEMT’s superiority in promoting convergence,diversity,and robustness in solving complex CMOPs. 展开更多
关键词 Constrained multi-objective optimization evolutionary algorithm evolutionary multitasking knowledge transfer
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Multi-objective optimization and biomechanical injury analysis of rear-seat occupant restraint systems in small overlap collision 认领 引用
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作者 Jia Yu Jingyu Xu +5 位作者 Tao Xiong Meng Xu Hao Deng Zhiyong Yin Xiuju Yang Shengxiong Liu 《Theoretical & Applied Mechanics Letters》 EI CAS CSCD 2026年第1期118-126,共9页
In small overlap collision,rear-seat occupants face elevated injury risks.This study conducts multi-objective opti-mization of the rear-seat occupant restraint system to reduce these injury risks and enhance overall v... In small overlap collision,rear-seat occupants face elevated injury risks.This study conducts multi-objective opti-mization of the rear-seat occupant restraint system to reduce these injury risks and enhance overall vehicle safety performance.Based on real-world traffic accident data,a full-vehicle crash simulation model was established and validated with the actual injury data.Weighted injury criteria(WIC)and neck injury metrics(Nij)were selected as optimization objectives.The rear-seat restraint system was optimized using NSGA-Ⅱ and TOPSIS algorithms to determine the optimal parameter configurations.The optimized parameters were subsequently reintegrated into the simulation model for validation.The results demonstrate a significant reduction in occupant injuries,with WIC and Nij reduced by 30.3%and 20.7%,respectively. 展开更多
关键词 Small overlap collision Rear-seat occupant injury Biomechanics analysis Multi-objective algorithm
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