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An integer multi-objective optimization model and an enhanced non-dominated sorting genetic algorithm for contraflow scheduling problem 认领 引用
1
作者 李沛恒 楼颖燕 《Journal of Central South University》 SCIE EI CAS CSCD 2015年第6期2399-2405,共7页
To determine the onset and duration of contraflow evacuation, a multi-objective optimization(MOO) model is proposed to explicitly consider both the total system evacuation time and the operation cost. A solution algor... To determine the onset and duration of contraflow evacuation, a multi-objective optimization(MOO) model is proposed to explicitly consider both the total system evacuation time and the operation cost. A solution algorithm that enhances the popular evolutionary algorithm NSGA-II is proposed to solve the model. The algorithm incorporates preliminary results as prior information and includes a meta-model as an alternative to evaluation by simulation. Numerical analysis of a case study suggests that the proposed formulation and solution algorithm are valid, and the enhanced NSGA-II outperforms the original algorithm in both convergence to the true Pareto-optimal set and solution diversity. 展开更多
关键词 hurricane evacuation contraflow scheduling multi-objective optimization NSGA-II
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Dynamic-model-based multi-objective optimization of valve plate for vibration reduction in axial piston pumps 认领 引用
2
作者 Shaogan YE Yutao WANG +4 位作者 Yue BAO Jing LUO Shoujun ZHAO Huixiang LIU Min YU 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第6期321-339,共19页
Axial piston pumps are widely used to supply the fluid power,but their significant vibration has become a growing concern.To address this issue,this paper presents a novel dynamic model for the axial piston pump and m... Axial piston pumps are widely used to supply the fluid power,but their significant vibration has become a growing concern.To address this issue,this paper presents a novel dynamic model for the axial piston pump and mitigates both flow fluctuation and mechanical vibration powers through a multi-objective optimization method.A Lumped-Parameter(LP)model is first introduced to describe dynamic behaviors of the entire pump assembly,and the Newmark-βmethod is adopted to calculate flow fluctuation and mechanical vibration powers.Experimental validation is conducted to ensure the accuracy of the proposed model.Using this validated model,a multiobjective optimization algorithm is employed to optimize the structural parameters of three representative valve plate types,aiming to simultaneously reduce flow fluctuation and mechanical vibrations.The optimization results demonstrate a significant reduction in the pump vibration power,as well as improvements in cavitation and pressure overshoot conditions.Among these optimized designs,the valve plate with hole-shaped damping grooves shows the lowest vibration power,while the valve plate with the triangular damping grooves achieves the lowest maximum piston chamber pressure.This study offers a promising approach for designing quieter axial piston pumps,which promotes the fluid power technology. 展开更多
关键词 Dynamic models Flow control Optimization Pumps Vibration control
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A hybrid modeling strategy based on deep learning surrogate models for accurate process multi-objective optimization of iso-octanol oxidation 认领 引用
3
作者 Xin Zhou Zhibo Zhang +3 位作者 Mengzhen Zhu Hui Zhao Hao Yan Chaohe Yang 《ENGINEERING Chemical Engineering》 SCIE EI CAS CSCD 2026年第2期45-56,共12页
Utilizing artificial intelligence to assist in the development of green processes for alcohol oxidation is a challenging and time-consuming task due to the lack of massive data and adequate optimization objectives.To ... Utilizing artificial intelligence to assist in the development of green processes for alcohol oxidation is a challenging and time-consuming task due to the lack of massive data and adequate optimization objectives.To solve these challenges,our work presents a hybrid surrogate model for iso-octanol oxidation to iso-octanal,integrating data-driven approaches with chemical equations grounded in mass transfer,heat transfer,momentum transfer,and reaction engineering,to enhance problem-solving efficiency.Specifically,a precise mechanistic model based on Aspen Plus generated database is developed to enhance the utility of experimental data,thereby overcoming the challenge of scarce oxidation experimental data caused by long operating cycles and hydrogen safety concerns.Based on this database,integrating machine learning techniques and intelligent optimization algorithms can quickly determine the optimal operating conditions for the iso-octanol oxidation reaction system.Compared to direct process simulation and multi-objective optimization methods,surrogate models exhibit higher efficiency,with computational speeds exceeding 400 times than those of traditional methods.The optimization results reveal significant reductions in both primary energy demand and greenhouse gas emissions,underscoring the effectiveness of the optimized solutions.Our work not only propels real-time optimization of alcohol oxidation production processes but also lays the groundwork for their widespread industrial application. 展开更多
关键词 deep learning surrogate models hybrid models multi-objective optimization
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A Multi-objective Optimization Method for Resistance Performance of an Icebreaker Bow Based on Fully Parameterized Modeling 认领 引用
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作者 Long Zhu Yanzhuo Xue +3 位作者 Ruinan Guo Yingfei Zan Yang Lu Yicheng Zhang 《哈尔滨工程大学学报（英文版）》 CSCD 2026年第2期362-376,共15页
The form of an icebreaker bow is numerically optimized using a platform that relies on three methods ship geometry morphing under a fully parameterized modeling approach,a cyclic process of contact compression bending... The form of an icebreaker bow is numerically optimized using a platform that relies on three methods ship geometry morphing under a fully parameterized modeling approach,a cyclic process of contact compression bending failure to calculate the icebreaking loads,and a differential evolution algorithm for optimization.The main objectives of this study are to optimize the total resistance and the average pressure in the ice zone.Surface sensitivity analysis based on an adjoint solver is used to identify the most significant regions of the hull.The hull in these regions is then formed using a cubic nonuniform rational B-spline technique.The differential evolution algorithm is employed to optimize the objectives associated with the hull form and determine the corresponding optimized variables.The optimal values are obtained by comparing the Pareto optimal designs.The optimization results show that the acquired hull form reduces the total resistance by 4.2%and decreases the average pressure in the ice zone by 0.6%.The main modifications introduced by the optimization process are to increase the buttock angle and the waterline angle. 展开更多
关键词 Icebreaker Fully parameterized modeling Optimization Total resistance Average pressure in the ice zone
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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://gffzz188fe103f8f1460asbuw5coponu506pwn.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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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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Boundary Decision-Based Multi-Objective Robust Optimization for Microgrid Dispatching 认领 引用
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作者 Junjian Wu Jingliao Sun +2 位作者 Yejun Xiang Zhenyu Zhou Zhengchai Shi 《Energy Engineering》 EI 2026年第7期404-423,共20页
The inherent unpredictability of renewable energy generation poses significant challenges to the reliable and economic dispatch of grid-connected microgrids.In response,this paper proposes a novel robust optimization ... The inherent unpredictability of renewable energy generation poses significant challenges to the reliable and economic dispatch of grid-connected microgrids.In response,this paper proposes a novel robust optimization strategy grounded in uncertain boundary decision-making and enhanced through innovations in the multi-objective cross-entropy method.An uncertainty budget-aware environmental economic dispatch model is first established,integrating photovoltaic and wind power generation.By employing mathematical sophistication-particularly Lagrangian transformation-the proposed method effectively resolves embedded uncertainties,transforming the original model into a deterministic multi-objective optimization framework robust against renewable energy volatility.Furthermore,by incorporating the dynamic operational demands of microgrids,this paper culminates in a robust optimization approach that is both fundamentally based on and adaptively responsive to uncertainty boundaries.To address the critical challenges of convergence and diversity in multi-objective optimization,crossover operators and an adaptive parameter update mechanism are introduced,significantly refining the conventional multi-objective cross-entropy algorithm.Case studies demonstrate the rationality and effectiveness of the proposed dispatch strategy and corroborate the superior performance and applicability of the enhanced algorithm. 展开更多
关键词 Microgrid environmental economic dispatch uncertain boundary robust optimization multi-objective cross entropy algorithm
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Multi-objective trajectory optimization for spaceborne antennas with nonlinear coupling using hp-adaptive pseudospectral discretization 认领 引用
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作者 Feng GAO Guanghui SUN 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第2期517-530,共14页
Spaceborne antennas are essential for remote sensing,deep-space communication,and Earth observation,yet their trajectory planning is complicated by nonlinear base-manipulator coupling and antenna flexibility.To addres... Spaceborne antennas are essential for remote sensing,deep-space communication,and Earth observation,yet their trajectory planning is complicated by nonlinear base-manipulator coupling and antenna flexibility.To address these challenges,this paper proposes a multi-objective trajectory optimization framework.The system dynamics capture both nonlinear rigid-flexible coupling and antenna deformation through a reduced-order formulation.To enhance discretization efficiency,a predictive-terminal hp-adaptive pseudospectral method is employed,assigning collocation density based on task-phase characteristics:finer resolution is applied to dynamic segments requiring higher accuracy,especially near the terminal phase.This enables efficient transcription of the continuous-time problem into a Nonlinear Programming Problem(NLP).The resulting NLP is then solved using a multi-objective optimization strategy based on the nondominated sorting genetic algorithm II,which explores trade-offs among antenna pointing accuracy,energy consumption,and structural vibration.Numerical results demonstrate that the proposed method achieves a reduction of approximately 14.0% in control energy and 41.8%in peak actuation compared to a GPOPS-II baseline,while significantly enhancing vibration suppression.The resulting Pareto front reveals structured trade-offs and clustered solutions,offering robust and diverse options for precision,low-disturbance mission planning. 展开更多
关键词 hp-adaptive pseudospectral method Multi-objective trajectory optimization Nonlinear dynamics Rigid-flexible coupling Spaceborne antenna Structural vibration suppression
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Knee point-guided heterogeneous surrogate-assisted optimization for multi-objective coal gasification system 认领 引用
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作者 Wenlu Li Nan Guo +5 位作者 Tiewei Shang Yueyang Sun Dapeng Li Xiaolong Gao Wei Xiong Junfei Qiao 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2026年第2期168-180,共13页
Coal gasificationtechnology plays a pivotal role in chemical production as a key process for efficiently converting coal into liquid fuels and chemical feedstocks.During gasification,high-temperature reactions generat... Coal gasificationtechnology plays a pivotal role in chemical production as a key process for efficiently converting coal into liquid fuels and chemical feedstocks.During gasification,high-temperature reactions generate syngas,and optimizing its operational parameters is essential for improving syngas quality,carbon efficiencyand liquid fuel yield.However,the intricate chemical reactions and heat transfer mechanisms in gasificationnecessitate costly simulations or experimental testing,making it an expensive multi-objective optimization problem.To address this challenge,this paper proposes a Knee Point-guided Heterogeneous Surrogate-assisted Evolutionary Algorithm(KG-HSEA)that integrates Kriging and Feedforward Neural Networks(FNN)to construct a heterogeneous surrogate model,leveraging their complementary strengths to reduce computational costs while maintaining predictive accuracy.By incorporating a knee point-guided search mechanism,the method prioritizes solutions that embody critical trade-offs among conflictingobjectives.Moreover,an adaptive sampling strategy combined with dual-archive management is employed to dynamically update the surrogate model,ensuring it adapts to unstable operating conditions while maintaining robust convergence-diversity balance in coal gasificationprocesses.Experimental results show that KG-HSEA achieved a 71.9% superiority rate with 23 optimal solutions out of 32 benchmark problems,highlighting its potential for efficientand feasible coal gasificationoptimization. 展开更多
关键词 Coal gasification Expensive multi-objective optimization Kriging Feedforward neural network(FNN) Knee point Model management
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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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A MOGWO-based multi-objective optimal tuning strategy for model predictive direct thrust control architecture in gas turbine engine 认领 引用
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作者 Genchang WANG Xiaofeng LIU 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2026年第3期384-409,共26页
In modern gas turbine engine control,Direct Thrust Control(DTC) is an effective method for achieving the desired thrust.Model Predictive Control(MPC) has the characteristics of handling constraints while accomplishing... In modern gas turbine engine control,Direct Thrust Control(DTC) is an effective method for achieving the desired thrust.Model Predictive Control(MPC) has the characteristics of handling constraints while accomplishing command tracking,making it a promising approach for implementing DTC.However,since the performance of DTC is highly sensitive to MPC's tuning parameters,developing an efficient optimization strategy for these parameters becomes imperative.Therefore,a Model Predictive Direct Thrust Control(MP-DTC) architecture is designed,and its asymptotic stability is proven.Additionally,the influence of the tuning parameters on control performance is analyzed.Then,a tuning strategy for MP-DTC architecture is proposed.This strategy combines the objectives of DTC to design a Multi-Objective Optimization(MOO) index function,and uses Multi-Objective Grey Wolf Optimizer(MOGWO) to solve its Pareto front and obtain the tuning parameters.In the Hardware-in-Loop(HIL) experiments,the proposed MPDTC architecture achieves the shortest settling time and smallest overshoot compared to the latest DTC scheme.Its MOGWO-based tuning strategy provides more Pareto-optimal solutions,ensuring optimal selection based on rapidity and stability,and maintains precise DTC even under component degradation and various operating conditions,thereby providing robustness,optimality,and generalizability. 展开更多
关键词 Direct thrust control Model predictive control Multi-objective grey wolf optimizer Multi-objective optimization Tuning parameters
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Multi-Objective Optimization of Defective Multi-Inventory Mother-Plate Cutting 认领 引用
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作者 Changtian Zhang Qi Zhang +3 位作者 Shujin Qin Xiwang Guo Bin Hu Wenjie Luo 《Computers, Materials & Continua》 SCIE EI 2026年第7期1640-1670,共31页
The increasing complexity of steel manufacturing and the rising demand for customized high-grade plates have intensified the need for efficient and defect-aware cutting optimization.In practical production,mother plat... The increasing complexity of steel manufacturing and the rising demand for customized high-grade plates have intensified the need for efficient and defect-aware cutting optimization.In practical production,mother plates frequently contain multiple surface defects,and the cutting process is further constrained by delay-sensitive operations such as tool-change sequences and defect-tolerance requirements.To address these challenges,this study formulates the Defective Multi-Inventory Mother-Plate Two-Dimensional Cutting Stock Problem(DMMP-2CSP)as a multi-objective model that simultaneously maximizes cutting profit and minimizes tool changes under strict geometric and defect-avoidance constraints.We develop an Improved Multi-Objective Grey Wolf Optimizer(IMOGWO)featuring continuous random-keys encoding with hierarchical decoding to handle multi-plate,multi-defect layouts;a Large-Language-Model-guided Fourth-Leader Boost mechanism that adaptively mitigates stagnation through domain-informed auxiliary-leader generation;and an NSGA-II fusion module incorporating non-dominated sorting,crowding-distance control,and stochastic variation to balance exploration and exploitation throughout the search.Extensive experiments on industrial-scale datasets demonstrate that IMOGWO consistently produces well-distributed Pareto-optimal solutions,significantly improves cutting profit,reduces tool-change frequency,and achieves superior overall performance compared with classical Multiobjective Grey Wolf Optimizer,Multiobjective Particle Swarm Optimization,Multi-Objective Cuckoo Search,and Multi-Objective Snake Optimizer baselines. 展开更多
关键词 Defective plate cutting multi-objective optimization metaheuristic optimization Large-Language-Model(LLM)-assisted decision support Pareto-based search
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MDMOSA:Multi-Objective-Oriented Dwarf Mongoose Optimization for Cloud Task Scheduling 认领 引用
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作者 Olanrewaju Lawrence Abraham Md Asri Ngadi +1 位作者 Johan Bin Mohamad Sharif Mohd Kufaisal Mohd Sidik 《Computers, Materials & Continua》 SCIE EI 2026年第3期2062-2096,共35页
Task scheduling in cloud computing is a multi-objective optimization problem,often involving conflicting objectives such as minimizing execution time,reducing operational cost,and maximizing resource utilization.Howev... Task scheduling in cloud computing is a multi-objective optimization problem,often involving conflicting objectives such as minimizing execution time,reducing operational cost,and maximizing resource utilization.However,traditional approaches frequently rely on single-objective optimization methods which are insufficient for capturing the complexity of such problems.To address this limitation,we introduce MDMOSA(Multi-objective Dwarf Mongoose Optimization with Simulated Annealing),a hybrid that integrates multi-objective optimization for efficient task scheduling in Infrastructure-as-a-Service(IaaS)cloud environments.MDMOSA harmonizes the exploration capabilities of the biologically inspired Dwarf Mongoose Optimization(DMO)with the exploitation strengths of Simulated Annealing(SA),achieving a balanced search process.The algorithm aims to optimize task allocation by reducing makespan and financial cost while improving system resource utilization.We evaluate MDMOSA through extensive simulations using the real-world Google Cloud Jobs(GoCJ)dataset within the CloudSim environment.Comparative analysis against benchmarked algorithms such as SMOACO,MOTSGWO,and MFPAGWO reveals that MDMOSA consistently achieves superior performance in terms of scheduling efficiency,cost-effectiveness,and scalability.These results confirm the potential of MDMOSA as a robust and adaptable solution for resource scheduling in dynamic and heterogeneous cloud computing infrastructures. 展开更多
关键词 Cloud computing multi-objective task scheduling dwarf mongoose optimization metaheuristic
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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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Low-Noise,High-Gain 28 GHz LNA Design Using Multi-Objective Optimization with NSGA-Ⅱ and MOPSO 认领 引用
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作者 Spandana Saggurthi Anand Nayyar +1 位作者 Sk Hasane Ahammad Sumendra Yogarayan 《Computers, Materials & Continua》 SCIE EI 2026年第9期691-709,共19页
This work presents a multi-objective optimization framework for systematic design-space exploration of a 28 GHz single-stage cascode LNA(Low noise amplifier)in 22 nm FDSOI technology using NSGA-Ⅱ and MOPSO algorithms... This work presents a multi-objective optimization framework for systematic design-space exploration of a 28 GHz single-stage cascode LNA(Low noise amplifier)in 22 nm FDSOI technology using NSGA-Ⅱ and MOPSO algorithms.The objectives of the paper include simultaneous minimization of noise figure(NF)and power consumption while maximizing gain under matching and stability constraints.Using device parameters and circuit models that were developed for a 22 nm FDSOI process technology,an optimization framework was created in Python,with the passive components LG,LS,LD,LOUT,and COUT chosen to be the variables optimized.The NSGA-Ⅱ optimized design achieves 1.7 dB NF,17 dB gain,and 4.7 mW DC power,while MOPSO achieves 1.8 dB NF,17.1 dB gain,and 5.0 mW power.NSGA-Ⅱ provides improved Pareto diversity and slightly better output matching,whereas MOPSO reduces computational time by 24%with comparable RF performance.The results demonstrate effective multi-objective design-space exploration and controlled algorithm benchmarking at the schematic-level for mm-wave LNA design. 展开更多
关键词 LNA mm-wave multi-objective optimization NSGA-Ⅱ MOPSO internet of things(IoT) S-parameters gain and noise figure
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Scaling the StrategyWall:Efficient Jailbreaking of LLMs via Component-Based Multi-Objective Optimization 认领 引用
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作者 Jialing Tao Song Huang Changyou Zheng 《Computers, Materials & Continua》 SCIE EI 2026年第9期1054-1074,共21页
Background:Jailbreak attacks,which use crafted prompts to bypass safety alignments of Large Language Models(LLMs)and generate harmful content,pose a significant security threat.Existing methods often optimize for a si... Background:Jailbreak attacks,which use crafted prompts to bypass safety alignments of Large Language Models(LLMs)and generate harmful content,pose a significant security threat.Existing methods often optimize for a single objective(e.g.,attack success rate),neglecting critical factors like query efficiency,which limits their practicality and generalization.Methods:We propose a Componentized Multi-Objective Optimization Framework(CMOOF),which introduces a paradigm shift:it searches for generalizable and query-efficient attack strategy templates within a structured,component-based strategy space.CMOOF leverages the NSGA-Ⅱ algorithm to explicitly co-optimize two first-class objectives:Attack Success Rate(ASR)and Query Efficiency,thereby discovering their Pareto-optimal tradeoff frontier.Results:Experiments on benchmark datasets show significant improvements,with the highest jailbreak success rate reaching 98.75%on models like Llama3,and query efficiency surpassing baselines.Conclusions:CMOOF redefines jailbreak optimization from instance-level prompt crafting to strategy-level template discovery.The work provides an efficient,scalable,and generalizable jailbreak solution,and the framework offers broader insights for automated red teaming and LLM security defense. 展开更多
关键词 Jailbreak attacks LLMs multi-objective optimization NSGA-Ⅱ
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