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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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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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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 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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A hybrid modeling strategy based on deep learning surrogate models for accurate process multi-objective optimization of iso-octanol oxidation 认领 引用
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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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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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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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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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On-orbit servicing launch dynamics and multi-objective optimization of impulsive rendezvous 认领 引用
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作者 LI Weikang JIANG Ju +1 位作者 BIAN Yue HAN Yanhua 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2026年第2期697-711,共15页
The multi-body dynamics in the launch process of a space platform deploying a server,as well as the optimal double impulse rendezvous guidance law between the server and the target spacecraft,are studied.Firstly,the s... The multi-body dynamics in the launch process of a space platform deploying a server,as well as the optimal double impulse rendezvous guidance law between the server and the target spacecraft,are studied.Firstly,the space platform enters into orbit around the target,keeping its launch tube axis aiming at it.After receiving the launch command,the server shoots out from the launch tube,flying to the target.Due to body coupling,the platform’s attitude is disturbed,preventing the server from accurately aiming at the target during separation.The server uses its small rocket engine to apply two velocity pulses:the first one to adjust its trajectory for rendezvous,and the second near the target to reduce relative velocity to zero for soft docking.A two-body dynamics model is established using the Newton-Euler method,and a virtual prototype is developed in ADAMS for validation.To solve the multi-objective optimization subject to energy consumption and flight time for rendezvous,an improved non-dominated sorting genetic algorithm II(NSGA-II)algorithm is proposed.Simulation results show that launch-induced perturbations are non-negligible,and the proposed algorithm effectively derives the optimal guidance law that balances energy use and flight time. 展开更多
关键词 multi-body dynamics virtual prototype impulse rendezvous multi-objective optimization on-orbit servicing
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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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Network-Constrained Multi-Objective Optimization for Integrated Microgrids with Renewable and EV Integration:A Systematic Review 认领 引用
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作者 Theint Theint Maw Shuai Zhou Tek Tjing Lie 《Energy Engineering》 EI 2026年第9期46-79,共34页
The rapid deployment of distributed energy resources(DERs),including photovoltaic(PV)generation,wind turbines(WT),battery energy storage systems(BESS),and electric vehicles(EVs),is transforming modern distribution net... The rapid deployment of distributed energy resources(DERs),including photovoltaic(PV)generation,wind turbines(WT),battery energy storage systems(BESS),and electric vehicles(EVs),is transforming modern distribution networks by introducing bidirectional power flows,voltage variations,and increased operational complexity,thereby require enhanced system resilience.This paper presents a systematic review of multi-objective optimization approaches for interconnected multi-microgrid(MMG)systems with explicit consideration of resilience,following the PRISMA 2020 guidelines.A structured literature search and screening process was conducted across major databases,including IEEE Xplore,Scopus,and ScienceDirect,covering publications from 2015 to 2026.The selected studies are synthesised based on modelling frameworks,power flow formulations,resilience metrics,and optimization strategies.The review identifies key trends,including the growing adoption of distributed coordination schemes and advanced optimization techniques to address uncertainty and scalability.However,a critical gap is observed in the integration of resilience objectives with detailed network-constrained modelling,which limits practical applicability in real-world MMG systems.Finally,key research gaps are highlighted,and future research directions are proposed to support the development of unified,scalable,and resilient optimization frameworks for high-DER MMG systems. 展开更多
关键词 Distributed energy resources interconnected multi-microgrids power flow modelling resilience multi-objective optimization hybrid optimization distribution networks
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Numerical Analysis and Multi-Objective Optimization of Tesla Valve Cold Plates for Lithium-Ion Battery Thermal Management 认领 引用
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作者 Anjie Hu Rui Zhao +2 位作者 Liu Tang Jun Wang Dong Liu 《Fluid Dynamics & Materials Processing》 EI 2026年第5期72-96,共25页
Lithium-ion batteries are widely deployed in electric vehicles,yet their performance and safety are strongly constrained by elevated operating temperatures,which may accelerate degradation and,in extreme cases,trigger... Lithium-ion batteries are widely deployed in electric vehicles,yet their performance and safety are strongly constrained by elevated operating temperatures,which may accelerate degradation and,in extreme cases,trigger thermal runaway.This study numerically investigates the thermal performance of a Tesla valve-based cold plate for battery thermal management,with the aim of enhancing heat dissipation efficiency through multi-parameter collaborative optimization.An initial screening is conducted using orthogonal experimental design to evaluate the effects of shunt angle(30°–50°),number of unit pairs(3–7),channel asymmetry ratio(0–0.5),and branch channel width(2–4 mm)on maximum temperature difference and pressure drop.The results indicate that the number of unit pairs and the asymmetry ratio are the dominant factors governing thermal and hydraulic performance.To further quantify these relationships,an optimal Latin hypercube sampling strategy is combined with Kriging surrogate modeling to construct response surfaces linking design variables to system performance.Subsequently,a multi-objective optimization based on the Non-dominated Sorting Genetic Algorithm II(NSGA-II)genetic algorithm is performed to simultaneously minimize temperature non-uniformity and pressure drop,yielding a Pareto-optimal solution set.The optimal configuration corresponds to a shunt angle of 30°,7 unit pairs,a zero asymmetry ratio,and a branch channel width of 4 mm.Compared with the baseline design,this configuration reduces the maximum temperature difference by 9.13%and the average temperature difference by 15.03%,while also decreasing pressure drop by 0.36%. 展开更多
关键词 Lithium-ion batteries thermal management tesla valve multi-objective optimization kriging model
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Dynamic-model-based multi-objective optimization of valve plate for vibration reduction in axial piston pumps 认领 引用
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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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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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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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An Improved Variant of Multi-Population Cooperative Constrained Multi-Objective Optimization(MCCMO)for Multi-Objective Optimization Problem 认领 引用
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作者 Muhammad Waqar Khan Adnan Ahmed Siddiqui Syed Sajjad Hussain Rizvi 《Computers, Materials & Continua》 SCIE EI 2026年第2期1874-1888,共15页
The multi-objective optimization problems,especially in constrained environments such as power distribution planning,demand robust strategies for discovering effective solutions.This work presents the improved variant... The multi-objective optimization problems,especially in constrained environments such as power distribution planning,demand robust strategies for discovering effective solutions.This work presents the improved variant of the Multi-population Cooperative Constrained Multi-Objective Optimization(MCCMO)Algorithm,termed Adaptive Diversity Preservation(ADP).This enhancement is primarily focused on the improvement of constraint handling strategies,local search integration,hybrid selection approaches,and adaptive parameter control.Theimproved variant was experimented on with the RWMOP50 power distribution systemplanning benchmark.As per the findings,the improved variant outperformed the original MCCMO across the eleven performance metrics,particularly in terms of convergence speed,constraint handling efficiency,and solution diversity.The results also establish that MCCMOADP consistently delivers substantial performance gains over the baseline MCCMO,demonstrating its effectiveness across performancemetrics.The new variant also excels atmaintaining the balanced trade-off between exploration and exploitation throughout the search process,making it especially suitable for complex optimization problems in multiconstrained power systems.These enhancements make MCCMO-ADP a valuable and promising candidate for handling problems such as renewable energy scheduling,logistics planning,and power system optimization.Future work will benchmark the MCCMO-ADP against widely recognized algorithms such as NSGA-Ⅱ,NSGA-Ⅲ,and MOEA/D and will also extend its validation to large-scale real-world optimization domains to further consolidate its generalizability. 展开更多
关键词 MCCMO algorithms adaptive diversity preservation RWMOP50 power distribution system multi-modal multi objective optimization evolutionary algorithm multi objective problem
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Kinetic modeling and multi-objective optimization of an industrial hydrocracking process with an improved SPEA2-PE algorithm 认领 引用
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作者 Chen Fan Xindong Wang +1 位作者 Gaochao Li Jian Long 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2025年第4期130-146,共17页
Hydrocracking is one of the most important petroleum refining processes that converts heavy oils into gases,naphtha,diesel,and other products through cracking reactions.Multi-objective optimization algorithms can help... Hydrocracking is one of the most important petroleum refining processes that converts heavy oils into gases,naphtha,diesel,and other products through cracking reactions.Multi-objective optimization algorithms can help refining enterprises determine the optimal operating parameters to maximize product quality while ensuring product yield,or to increase product yield while reducing energy consumption.This paper presents a multi-objective optimization scheme for hydrocracking based on an improved SPEA2-PE algorithm,which combines path evolution operator and adaptive step strategy to accelerate the convergence speed and improve the computational accuracy of the algorithm.The reactor model used in this article is simulated based on a twenty-five lumped kinetic model.Through model and test function verification,the proposed optimization scheme exhibits significant advantages in the multiobjective optimization process of hydrocracking. 展开更多
关键词 Hydrocracking Multi-objective optimization Improved SPEA2 Kinetic modeling
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Multi-objective optimization workflow for CO2 water-alternating-gas injection assisted by single-objective pre-search 认领 引用
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作者 Ren-Feng Yang Wei Zhang +2 位作者 Shuai-Chen Liu Bin Yuan Wen-Dong Wang 《Petroleum Science》 SCIE EI CAS CSCD 2025年第7期2967-2976,共10页
CO2 Water-Alternating-Gas(CO2-WAG)injection is not only a method to enhance oil recovery but also a feasible way to achieve CO2 sequestration.However,inappropriate injection strategies would prevent the attai... CO2 Water-Alternating-Gas(CO2-WAG)injection is not only a method to enhance oil recovery but also a feasible way to achieve CO2 sequestration.However,inappropriate injection strategies would prevent the attainment of maximum oil recovery and cumulative CO2 storage.Furthermore,the optimization of CO2-WAG is computationally expensive as it needs to frequently call the compositional simulation model that involves various CO2 storage mechanisms.Therefore,the surrogate-assisted evolutionary optimization is necessary,which replaces the compositional simulator with surrogate models.In this paper,a surrogate-based multi-objective optimization algorithm assisted by the single-objective pre-search method is proposed.The results of single-objective optimization will be used to initialize the solutions of multi-objective optimization,which accelerates the exploration of the entire Pareto front.In addition,a convergence criterion is also proposed for the single-objective optimization during pre-search,and the gradient of surrogate models is adopted as the convergence criterion.Finally,the method proposed in this work is applied to two benchmark reservoir models to prove its efficiency and correctness.The results show that the proposed algorithm achieves a better performance than the conventional ones for the multi-objective optimization of CO2-WAG. 展开更多
关键词 CO2-WAG CO2storage Multi-objective optimization Convergence criterion
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Evolutionary Multitasking With Multiple Knowledge Representations and Elite Vector Guidance for Solving Large-Scale Multi-Objective Optimization Problems 认领 引用
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作者 Weijie Mai Zhifan Tang +2 位作者 Weili Liu Jinghui Zhong Hu Jin 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2025年第12期2553-2571,共19页
Evolutionary multitasking optimization(EMTO) can obtain beneficial knowledge for the target task from the auxiliary task to improve its performance, which has received extensive attention in scientific research and en... Evolutionary multitasking optimization(EMTO) can obtain beneficial knowledge for the target task from the auxiliary task to improve its performance, which has received extensive attention in scientific research and engineering problems. Nevertheless, faced with the widespread large-scale multi-objective optimization problems(LSMOPs), the existing EMTO literature barely involves the research of LSMOPs. More importantly, these EMTO algorithms often get trapped in local optima when dealing with LSMOPs, resulting in a slow convergence speed, which is worthy of our attention. To this end, this paper proposes an EMTO algorithm dedicated to solving LSMOPs. On the one hand, given the intricate nature of LSMOPs, we propose a knowledge domination-based knowledge transfer mechanism that can flexibly transfer knowledge from multiple knowledge representations, i.e., the information distribution and distribution distance of the task population. On the other hand, we design an elite vector-guided search strategy. Specifically, the generative adversarial network(GAN) model should first be trained within the divided populations. Then, the well-trained model is used to generate a high-quality individual for the target individual. After that, the high-quality individual is combined with the top-performing individual in the current population to find the elite vector corresponding to the target individual. Finally, the elite vector is applied to guide the target individual to accelerate convergence towards the global optimum in the high-dimensional decision space. We conduct comprehensive experimental investigations on two artificial LSMOPs suites and six real-world LSMOPs to validate the efficiency and robustness of the proposed algorithm,through comparative analysis with state-of-the-art peer algorithms. 展开更多
关键词 Elite vector evolutionary multitasking optimization(EMTO) large-scale multi-objective optimization multiple knowledge representations
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Multi-objective optimization framework in the modeling of belief rule-based systems with interpretability-accuracy trade-off 认领 引用
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作者 YOU Yaqian SUN Jianbin +1 位作者 TAN Yuejin JIANG Jiang 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2025年第2期423-435,共13页
The belief rule-based(BRB)system has been popular in complexity system modeling due to its good interpretability.However,the current mainstream optimization methods of the BRB systems only focus on modeling accuracy b... The belief rule-based(BRB)system has been popular in complexity system modeling due to its good interpretability.However,the current mainstream optimization methods of the BRB systems only focus on modeling accuracy but ignore the interpretability.The single-objective optimization strategy has been applied in the interpretability-accuracy trade-off by inte-grating accuracy and interpretability into an optimization objec-tive.But the integration has a greater impact on optimization results with strong subjectivity.Thus,a multi-objective optimiza-tion framework in the modeling of BRB systems with inter-pretability-accuracy trade-off is proposed in this paper.Firstly,complexity and accuracy are taken as two independent opti-mization goals,and uniformity as a constraint to give the mathe-matical description.Secondly,a classical multi-objective opti-mization algorithm,nondominated sorting genetic algorithm II(NSGA-II),is utilized as an optimization tool to give a set of BRB systems with different accuracy and complexity.Finally,a pipeline leakage detection case is studied to verify the feasibility and effectiveness of the developed multi-objective optimization.The comparison illustrates that the proposed multi-objective optimization framework can effectively avoid the subjectivity of single-objective optimization,and has capability of joint optimiz-ing the structure and parameters of BRB systems with inter-pretability-accuracy trade-off. 展开更多
关键词 belief rule-based(BRB)systems interpretability multi-objective optimization nondominated sorting genetic algo-rithm II(NSGA-II) pipeline leakage detection.
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