Large-scale electric vehicles(EVs) connected to the micro grid would cause many problems. In this paper, with the consideration of vehicle to grid(V2 G), two charging and discharging load modes of EVs were constructed...Large-scale electric vehicles(EVs) connected to the micro grid would cause many problems. In this paper, with the consideration of vehicle to grid(V2 G), two charging and discharging load modes of EVs were constructed. One was the disorderly charging and discharging mode based on travel habits, and the other was the orderly charging and discharging mode based on time-of-use(TOU) price;Monte Carlo method was used to verify the case. The scheme of the capacity optimization of photovoltaic charging station under two different charging and discharging modes with V2 G was proposed. The mathematical models of the objective function with the maximization of energy efficiency, the minimization of the investment and the operation cost of the charging system were established. The range of decision variables, constraints of the requirements of the power balance and the strategy of energy exchange were given. NSGA-Ⅱ and NSGA-SA algorithm were used to verify the cases, respectively. In both algorithms, by comparing with the simulation results of the two different modes, it shows that the orderly charging and discharging mode with V2 G is obviously better than the disorderly charging and discharging mode in the aspects of alleviating the pressure of power grid, reducing system investment and improving energy efficiency.展开更多
To address issues such as poor initial population diversity, low stability and local convergence accuracy, and easy local optima in the traditional Multi-Objective Artificial Hummingbird Algorithm (MOAHA), an Improved...To address issues such as poor initial population diversity, low stability and local convergence accuracy, and easy local optima in the traditional Multi-Objective Artificial Hummingbird Algorithm (MOAHA), an Improved MOAHA (IMOAHA) was proposed. The improvements involve Tent mapping based on random variables to initialize the population, a logarithmic decrease strategy for inertia weight to balance search capability, and the improved search operators in the territory foraging phase to enhance the ability to escape from local optima and increase convergence accuracy. The effectiveness of IMOAHA was verified through Matlab/Simulink. The results demonstrate that IMOAHA exhibits superior convergence, diversity, uniformity, and coverage of solutions across 6 test functions, outperforming 4 comparative algorithms. A Wilcoxon rank-sum test further confirmed its exceptional performance. To assess IMOAHA’s ability to solve engineering problems, an optimization model for a multi-track, multi-train urban rail traction power supply system with Supercapacitor Energy Storage Systems (SCESSs) was established, and IMOAHA was successfully applied to solving the capacity allocation problem of SCESSs, demonstrating that it is an effective tool for solving complex Multi-Objective Optimization Problems (MOOPs) in engineering domains.展开更多
Present of wind power is sporadically and cannot be utilized as the only fundamental load of energy sources.This paper proposes a wind-solar hybrid energy storage system(HESS)to ensure a stable supply grid for a longe...Present of wind power is sporadically and cannot be utilized as the only fundamental load of energy sources.This paper proposes a wind-solar hybrid energy storage system(HESS)to ensure a stable supply grid for a longer period.A multi-objective genetic algorithm(MOGA)and state of charge(SOC)region division for the batteries are introduced to solve the objective function and configuration of the system capacity,respectively.MATLAB/Simulink was used for simulation test.The optimization results show that for a 0.5 MW wind power and 0.5 MW photovoltaic system,with a combination of a 300 Ah lithium battery,a 200 Ah lead-acid battery,and a water storage tank,the proposed strategy reduces the system construction cost by approximately 18,000 yuan.Additionally,the cycle count of the electrochemical energy storage systemincreases from4515 to 4660,while the depth of discharge decreases from 55.37%to 53.65%,achieving shallow charging and discharging,thereby extending battery life and reducing grid voltage fluctuations significantly.The proposed strategy is a guide for stabilizing the grid connection of wind and solar power generation,capability allocation,and energy management of energy conservation systems.展开更多
Fatty acids are the main constituents of vegetable oils.To determine the fatty acid compositions of small trade vegetable oils and some less well studied beneficial vegetable oils,and investigate their relationships w...Fatty acids are the main constituents of vegetable oils.To determine the fatty acid compositions of small trade vegetable oils and some less well studied beneficial vegetable oils,and investigate their relationships with antioxidant activity and oxidative stability,gas chromatography-mass spectrometry was performed to characterize the associated fatty acid profiles.The antioxidant activity of vegetable oils,based on their DPPH-scavenging capacity(expressed as IC_(50) values),was used to assess their impact on human health,and their oxidative stability was characterized by performing lipid oxidation analysis to determine the oxidative induction time of fats and oils.In addition,correlation analyses were performed to examine associations between the fatty acid composition of the oils and DPPH-scavenging capacity and oxidative stability.The results revealed that among the assessed oils,coffee seed oil has the highest saturated fatty acid content(355.10 mg/g),whereas Garddenia jaminoides oil has the highest unsaturated fatty acid content(844.84 mg/g).Coffee seed oil was also found have the lowest DPPH IC_(50) value(2.30 mg/mL)and the longest oxidation induction time(17.09 h).Correlation analysis revealed a significant linear relationship(P<0.05)between oxidative stability and unsaturated fatty acid content,with lower contents tending to be associated with better oxidative stability.The findings of this study provide reference data for the screening of functional edible vegetable oils.展开更多
The multi-objective particle swarm optimization algorithm(MOPSO)is widely used to solve multi-objective optimization problems.In the article,amulti-objective particle swarm optimization algorithmbased on decomposition...The multi-objective particle swarm optimization algorithm(MOPSO)is widely used to solve multi-objective optimization problems.In the article,amulti-objective particle swarm optimization algorithmbased on decomposition and multi-selection strategy is proposed to improve the search efficiency.First,two update strategies based on decomposition are used to update the evolving population and external archive,respectively.Second,a multiselection strategy is designed.The first strategy is for the subspace without a non-dominated solution.Among the neighbor particles,the particle with the smallest penalty-based boundary intersection value is selected as the global optimal solution and the particle far away fromthe search particle and the global optimal solution is selected as the personal optimal solution to enhance global search.The second strategy is for the subspace with a non-dominated solution.In the neighbor particles,two particles are randomly selected,one as the global optimal solution and the other as the personal optimal solution,to enhance local search.The third strategy is for Pareto optimal front(PF)discontinuity,which is identified by the cumulative number of iterations of the subspace without non-dominated solutions.In the subsequent iteration,a new probability distribution is used to select from the remaining subspaces to search.Third,an adaptive inertia weight update strategy based on the dominated degree is designed to further improve the search efficiency.Finally,the proposed algorithmis compared with fivemulti-objective particle swarm optimization algorithms and five multi-objective evolutionary algorithms on 22 test problems.The results show that the proposed algorithm has better performance.展开更多
Background Oxidative stress significantly impacts growth performance and liver function in piglets.Ferulic acid(FA)works as an antioxidant,however,the role and mechanism of FA in the regulation of diquat-induced oxida...Background Oxidative stress significantly impacts growth performance and liver function in piglets.Ferulic acid(FA)works as an antioxidant,however,the role and mechanism of FA in the regulation of diquat-induced oxidative stress in piglets are less known.This study was designed to investigate the effects of FA on growth performance and antioxi-dant capacity in piglets with diquat challenge.Methods Thirty-two healthy DLY(Duroc×Landrace×Yorkshire)piglets(13.24±0.19 kg)were randomly divided into one of two diets including 0 or 4 g/kg FA for 14 d.On d 15,all pigs were intraperitoneally injected diquat or sterile saline.Results Dietary supplementation with ferulic acid(FA)significantly improved the average daily gain(ADG)and decreased feed-gain ratio(F/G)of piglets.Here,dietary FA supplementation reduced serum aspartate aminotrans-ferase(AST),alanine aminotransferase(ALT)activities in diquat challenged piglets.Furthermore,diquat infusion increased reactive oxygen radicals(ROS)level in liver,decreased the activities of total superoxide dismutase(T-SOD)and glutathione peroxidase(GSH-Px),total antioxidant capacity(T-AOC)and increased malondialdehyde(MDA)con-tent in the liver and serum.Supplementation with FA significantly increased T-AOC and T-SOD activities and decreased MDA and ROS levels.FA down-regulated gene and protein expression of Keap1,and up-regulated protein expression of Nrf2 and HO-1 in the liver of piglets with diquat challenge.Importantly,diquat challenge increased the ratio of late apoptosis,increased serum levels of IL-1β,IL-18 and lactate dehydrogenase(LDH),and up-regulated pyroptosis-related genes in the liver.FA supplementation reduced the ratio of late apoptosis and down-regulated mRNA expression of Caspase-1.Accordingly,FA addition reduced concentration of IL-1β,IL-18,and LDH under diquat challenge.Conclusions Diquat-induced oxidative stress reduced growth performance and impaired liver function in piglets.Dietary FA supplementation enhanced the antioxidant capacity and reduced the degree of hepatocyte pyroptosis,thereby alleviating the oxidative damage in the liver and mitigating the impact of diquat on growth performance of piglets.展开更多
This paper introduces the Surrogate-assisted Multi-objective Grey Wolf Optimizer(SMOGWO)as a novel methodology for addressing the complex problem of empty-heavy train allocation,with a focus on line utilization balanc...This paper introduces the Surrogate-assisted Multi-objective Grey Wolf Optimizer(SMOGWO)as a novel methodology for addressing the complex problem of empty-heavy train allocation,with a focus on line utilization balance.By integrating surrogate models to approximate the objective functions,SMOGWO significantly improves the efficiency and accuracy of the optimization process.The effectiveness of this approach is evaluated using the CEC2009 multi-objective test function suite,where SMOGWO achieves a superiority rate of 76.67%compared to other leading multi-objective algorithms.Furthermore,the practical applicability of SMOGWO is demonstrated through a case study on empty and heavy train allocation,which validates its ability to balance line capacity,minimize transportation costs,and optimize the technical combination of heavy trains.The research highlights SMOGWO's potential as a robust solution for optimization challenges in railway transportation,offering valuable contributions toward enhancing operational efficiency and promoting sustainable development in the sector.展开更多
The increased demand for personalized customization calls for new production modes to enhance collaborations among a wide range of manufacturing practitioners who unnecessarily trust each other.In this article,a block...The increased demand for personalized customization calls for new production modes to enhance collaborations among a wide range of manufacturing practitioners who unnecessarily trust each other.In this article,a blockchain-enabled manufacturing collaboration framework is proposed,with a focus on the production capacity matching problem for blockchainbased peer-to-peer(P2P)collaboration.First,a digital model of production capacity description is built for trustworthy and transparent sharing over the blockchain.Second,an optimization problem is formulated for P2P production capacity matching with objectives to maximize both social welfare and individual benefits of all participants.Third,a feasible solution based on an iterative double auction mechanism is designed to determine the optimal price and quantity for production capacity matching with a lack of personal information.It facilitates automation of the matching process while protecting users'privacy via blockchainbased smart contracts.Finally,simulation results from the Hyperledger Fabric-based prototype show that the proposed approach increases social welfare by 1.4%compared to the Bayesian game-based approach,makes all participants profitable,and achieves 90%fairness of enterprises.展开更多
In the independent electro-hydrogen system(IEHS)with hybrid energy storage(HESS),achieving optimal scheduling is crucial.Still,it presents a challenge due to the significant deviations in values ofmultiple optimizatio...In the independent electro-hydrogen system(IEHS)with hybrid energy storage(HESS),achieving optimal scheduling is crucial.Still,it presents a challenge due to the significant deviations in values ofmultiple optimization objective functions caused by their physical dimensions.These deviations seriously affect the scheduling process.A novel standardization fusion method has been established to address this issue by analyzing the variation process of each objective function’s values.The optimal scheduling results of IEHS with HESS indicate that the economy and overall energy loss can be improved 2–3 times under different optimization methods.The proposed method better balances all optimization objective functions and reduces the impact of their dimensionality.When the cost of BESS decreases by approximately 30%,its participation deepens by about 1 time.Moreover,if the price of the electrolyzer is less than 15¥/kWh or if the cost of the fuel cell drops below 4¥/kWh,their participation will increase substantially.This study aims to provide a more reasonable approach to solving multi-objective optimization problems.展开更多
This study investigates the bond performance at the interfacial region shared by Ultra-High Performance Concrete(UHPC)and steel tubes through push-out tests.This study examines how changes in steel fiber volumetric ra...This study investigates the bond performance at the interfacial region shared by Ultra-High Performance Concrete(UHPC)and steel tubes through push-out tests.This study examines how changes in steel fiber volumetric ratio and thickness of steel tube influence the bond strength characteristics.The results show that as the enhancement of the steel tube wall thickness,the ultimate bond strength at the interface improves significantly,whereas the initial bond strength exhibits only slight variations.The influence of steel fiber volumetric ratio presents a nonlinear trend,with initial bond strength decreasing at low fiber content and increasing significantly as fiber content rises.Additionally,finite element(FE)simulations were applied to replicate the experimental conditions,and the outcomes showed strong correlation with the experimental data,confirming the exactitude of the FE model in predicting the bond behavior at the UHPC-Steel interface.These findings provide valuable insights for optimizing the design of UHPC-Filled steel tubes in high-performance structure.展开更多
In the context of the“dual carbon”goals,to address issues such as high energy consumption,high costs,and low power quality in the rapid development of electrified railways,this study focused on the China Railways Hi...In the context of the“dual carbon”goals,to address issues such as high energy consumption,high costs,and low power quality in the rapid development of electrified railways,this study focused on the China Railways High-Speed 5 Electric Multiple Unit and proposed a mathematical model and capacity optimization method for an onboard energy storage system using lithium batteries and supercapacitors as storage media.Firstly,considering the electrical characteristics,weight,and volume of the storage media,a mathematical model of the energy storage system was established.Secondly,to tackle problems related to energy consumption and power quality,an energy management strategy was proposed that comprehensively considers peak shaving and valley filling and power quality by controlling the charge/discharge thresholds of the storage system.Thecapacity optimization adopted a bilevel programming model,with the series/parallel number of storage modules as variables,considering constraints imposed by the Direct Current to Direct Current converter,train load,and space.An improved Particle Swarm Optimization algorithm and linear programming solver were used to solve specific cases.The results show that the proposed onboard energy storage system can effectively achieve energy savings,reduce consumption,and improve power qualitywhile meeting the load and space limitations of the train.展开更多
The intermittency and volatility of wind and photovoltaic power generation exacerbate issues such as wind and solar curtailment,hindering the efficient utilization of renewable energy and the low-carbon development of...The intermittency and volatility of wind and photovoltaic power generation exacerbate issues such as wind and solar curtailment,hindering the efficient utilization of renewable energy and the low-carbon development of energy systems.To enhance the consumption capacity of green power,the green power system consumption optimization scheduling model(GPS-COSM)is proposed,which comprehensively integrates green power system,electric boiler,combined heat and power unit,thermal energy storage,and electrical energy storage.The optimization objectives are to minimize operating cost,minimize carbon emission,and maximize the consumption of wind and solar curtailment.The multi-objective particle swarm optimization algorithm is employed to solve the model,and a fuzzy membership function is introduced to evaluate the satisfaction level of the Pareto optimal solution set,thereby selecting the optimal compromise solution to achieve a dynamic balance among economic efficiency,environmental friendliness,and energy utilization efficiency.Three typical operating modes are designed for comparative analysis.The results demonstrate that the mode involving the coordinated operation of electric boiler,thermal energy storage,and electrical energy storage performs the best in terms of economic efficiency,environmental friendliness,and renewable energy utilization efficiency,achieving the wind and solar curtailment consumption rate of 99.58%.The application of electric boiler significantly enhances the direct accommodation capacity of the green power system.Thermal energy storage optimizes intertemporal regulation,while electrical energy storage strengthens the system’s dynamic regulation capability.The coordinated optimization of multiple devices significantly reduces reliance on fossil fuels.展开更多
Since the discovery of carbon dots(CDs)in 2004,the unique photoluminescence phenomenon of CDs has attracted widespread attention.However,the molecular weight of CDs has not been adequately quantified at present,due to...Since the discovery of carbon dots(CDs)in 2004,the unique photoluminescence phenomenon of CDs has attracted widespread attention.However,the molecular weight of CDs has not been adequately quantified at present,due to CDs are atomically imprecise and their molecular weight distribution is broad.In this paper,a series of Pluronic-modified CDs were prepared and the structure of the CDs was briefly analyzed.Subsequently,a molecular weight measurement method based on colligative properties was developed,and the correction coefficient in the algorithm was briefly analyzed.The calculated molecular weight was applied to the determination of surface adsorption capacity.This work provided a method for averaging the molecular weight of atomically imprecise particulate materials,which is expected to provide new opportunities in related fields.展开更多
Multi-instance image generation remains a challenging task in the field of computer vision.While existing diffusionmodels demonstrate impressive fidelity in image generation,they often struggle with precisely controll...Multi-instance image generation remains a challenging task in the field of computer vision.While existing diffusionmodels demonstrate impressive fidelity in image generation,they often struggle with precisely controlling each object’s shape,pose,and size.Methods like layout-to-image and mask-to-image provide spatial guidance but frequently suffer from object shape distortion,overlaps,and poor consistency,particularly in complex scenes with multiple objects.To address these issues,we introduce PolyDiffusion,a contour-based diffusion framework that encodes each object’s contour as a boundary-coordinate sequence,decoupling object shapes and positions.This approach allows for better control over object geometry and spatial positioning,which is critical for achieving high-quality multiinstance generation.We formulate the training process as a multi-objective optimization problem,balancing three key objectives:a denoising diffusion loss to maintain overall image fidelity,a cross-attention contour alignment loss to ensure precise shape adherence,and a reward-guided denoising objective that minimizes the Fréchet distance to real images.In addition,the Object Space-Aware Attention module fuses contour tokens with visual features,while a prior-guided fusion mechanism utilizes inter-object spatial relationships and class semantics to enhance consistency across multiple objects.Experimental results on benchmark datasets such as COCO-Stuff and VOC-2012 demonstrate that PolyDiffusion significantly outperforms existing layout-to-image and mask-to-image methods,achieving notable improvements in both image quality and instance-level segmentation accuracy.The implementation of Poly Diffusion is available at https://github.com/YYYYYJS/PolyDiffusion(accessed on 06 August 2025).展开更多
To investigate the evolution of load-bearing characteristics of pre-stressed beams throughout their service life and to provide a basis for accurately assessing the actual working state of damaged pre-stressed concret...To investigate the evolution of load-bearing characteristics of pre-stressed beams throughout their service life and to provide a basis for accurately assessing the actual working state of damaged pre-stressed concrete T-beams,destructive tests were conducted on full-scale pre-stressed concrete beams.Based on the measurement and ana-lysis of beam deflection,strain,and crack development under various loading levels during the research tests,combined with the verification coefficient indicators specified in the codes,the verification coefficients of bridges at different stages of damage can be examined.The results indicate that the T-beams experience complete,incom-plete linear,and non-linear stages during the destructive test process.In the complete linear elastic stage,both the deflection and bottom strain verification coefficients comply with the specifications,indicating a good structural load-bearing capacity no longer adheres to the code’s requirements.In the non-linear stage,both coefficients exhi-bit a sharp increase,resulting in a further decrease in the structure’s load-bearing capacity.According to the pro-visions of the current code,the beam can be in the incomplete linear stage when both values fall within the code’s specified range.The strain verification coefficient sourced from the compression zone at the bottom of theflange is not recommended for assessing the bridge’s load-bearing capacity.展开更多
In the last decade,space solar power satellites(SSPSs)have been conceived to support net-zero carbon emissions and have attracted considerable attention.Electric energy is transmitted to the ground via a microwave pow...In the last decade,space solar power satellites(SSPSs)have been conceived to support net-zero carbon emissions and have attracted considerable attention.Electric energy is transmitted to the ground via a microwave power beam,a technology known as microwave power transmission(MPT).Due to the vast transmission distance of tens of thousands of kilometers,the power transmitting antenna array must span up to 1 kilometer in diameter.At the same time,the size of the rectifying array on the ground should extend over a few kilometers.This makes the MPT system of SSPSs significantly larger than the existing aerospace engineering system.To design and operate a rational MPT system,comprehensive optimization is required.Taking the space MPT system engineering into consideration,a novel multi-objective optimization function is proposed and further analyzed.The multi-objective optimization problem is modeled mathematically.Beam collection efficiency(BCE)is the primary factor,followed by the thermal management capability.Some tapers,designed to solve the conflict between BCE and the thermal problem,are reviewed.In addition to these two factors,rectenna design complexity is included as a functional factor in the optimization objective.Weight coefficients are assigned to these factors to prioritize them.Radiating planar arrays with different aperture illumination fields are studied,and their performances are compared using the multi-objective optimization function.Transmitting array size,rectifying array size,transmission distance,and transmitted power remaine constant in various cases,ensuring fair comparisons.The analysis results show that the proposed optimization function is effective in optimizing and selecting the MPT system architecture.It is also noted that the multi-objective optimization function can be expanded to include other factors in the future.展开更多
To exchange experiences and progress in standardization capacity building at home and abroad,the sub-forum on capacity building and creating leadership in standardization was held on July 9.The sub-forum was hosted by...To exchange experiences and progress in standardization capacity building at home and abroad,the sub-forum on capacity building and creating leadership in standardization was held on July 9.The sub-forum was hosted by the Qingdao Municipal People’s Government and co-organized by the International Standardization Training Base(Qingdao),Qingdao University,International Standardization Outstanding Contribution Foundation,and Shandong National Standards Center of Technical Evaluation,which was expected to inject new vitality into the industrial development and provide new ideas for improving the talent cultivation system.展开更多
Impinging jet arrays are extensively used in numerous industrial operations,including the cooling of electronics,turbine blades,and other high-heat flux systems because of their superior heat transfer capabilities.Opt...Impinging jet arrays are extensively used in numerous industrial operations,including the cooling of electronics,turbine blades,and other high-heat flux systems because of their superior heat transfer capabilities.Optimizing the design and operating parameters of such systems is essential to enhance cooling efficiency and achieve uniform pressure distribution,which can lead to improved system performance and energy savings.This paper presents two multi-objective optimization methodologies for a turbulent air jet impingement cooling system.The governing equations are resolved employing the commercial computational fluid dynamics(CFD)software ANSYS Fluent v17.The study focuses on four controlling parameters:Reynolds number(Re),swirl number(S),jet-to-jet separation distance(Z/D),and impingement height(H/D).The effects of these parameters on heat transfer and impingement pressure distribution are investigated.Non-dominated Sorting Genetic Algorithm(NSGA-II)and Weighted Sum Method(WSM)are employed to optimize the controlling parameters for maximum cooling performance.The aim is to identify optimal design parameters and system configurations that enhance heat transfer efficiency while achieving a uniform impingement pressure distribution.These findings have practical implications for applications requiring efficient cooling.The optimized design achieved a 12.28%increase in convective heat transfer efficiency with a local Nusselt number of 113.05 compared to 100.69 in the reference design.Enhanced convective cooling and heat flux were observed in the optimized configuration,particularly in areas of direct jet impingement.Additionally,the optimized design maintained lower wall temperatures,demonstrating more effective thermal dissipation.展开更多
With the development of renewable energy technologies such as photovoltaics and wind power,it has become a research hotspot to improve the consumption rate of new energy and reduce energy costs through algorithm impro...With the development of renewable energy technologies such as photovoltaics and wind power,it has become a research hotspot to improve the consumption rate of new energy and reduce energy costs through algorithm improvement.To reduce the operational costs of micro-grid systems and the energy abandonment rate of renewable energy,while simultaneously enhancing user satisfaction on the demand side,this paper introduces an improvedmultiobjective Grey Wolf Optimizer based on Cauchy variation.The proposed approach incorporates a Cauchy variation strategy during the optimizer’s search phase to expand its exploration range and minimize the likelihood of becoming trapped in local optima.At the same time,adoptingmultiple energy storage methods to improve the consumption rate of renewable energy.Subsequently,under different energy balance orders,themulti-objective particle swarmalgorithm,multi-objective grey wolf optimizer,and Cauchy’s variant of the improvedmulti-objective grey wolf optimizer are used for example simulation,solving the Pareto solution set of the model and comparing.The analysis of the results reveals that,compared to the original optimizer,the improved optimizer decreases the daily cost by approximately 100 yuan,and reduces the energy abandonment rate to zero.Meanwhile,it enhances user satisfaction and ensures the stable operation of the micro-grid.展开更多
This study proposes a multi-objective optimization framework for electric winches in fiber-reinforced plastic(FRP)fishing vessels to address critical limitations of conventional designs,including excessive weight,mate...This study proposes a multi-objective optimization framework for electric winches in fiber-reinforced plastic(FRP)fishing vessels to address critical limitations of conventional designs,including excessive weight,material inefficiency,and performance redundancy.By integrating surrogate modeling techniques with a multi-objective genetic algorithm(MOGA),we have developed a systematic approach that encompasses parametric modeling,finite element analysis under extreme operational conditions,and multi-fidelity performance evaluation.Through a 10-t electric winch case study,the methodology’s effectiveness is demonstrated via parametric characterization of structural integrity,stiffness behavior,and mass distribution.The comparative analysis identified optimal surrogate models for predicting key performance metrics,which enabled the construction of a robust multi-objective optimization model.The MOGA-derived Pareto solutions produced a design configuration achieving 7.86%mass reduction,2.01%safety factor improvement,and 23.97%deformation mitigation.Verification analysis confirmed the optimization scheme’s reliability in balancing conflicting design requirements.This research establishes a generalized framework for marine deck machinery modernization,particularly addressing the structural compatibility challenges in FRP vessel retrofitting.The proposed methodology demonstrates significant potential for facilitating sustainable upgrades of fishing vessel equipment through systematic performance optimization.展开更多
基金Project(3502Z20179026)supported by Xiamen Science and Technology Project,China。
文摘Large-scale electric vehicles(EVs) connected to the micro grid would cause many problems. In this paper, with the consideration of vehicle to grid(V2 G), two charging and discharging load modes of EVs were constructed. One was the disorderly charging and discharging mode based on travel habits, and the other was the orderly charging and discharging mode based on time-of-use(TOU) price;Monte Carlo method was used to verify the case. The scheme of the capacity optimization of photovoltaic charging station under two different charging and discharging modes with V2 G was proposed. The mathematical models of the objective function with the maximization of energy efficiency, the minimization of the investment and the operation cost of the charging system were established. The range of decision variables, constraints of the requirements of the power balance and the strategy of energy exchange were given. NSGA-Ⅱ and NSGA-SA algorithm were used to verify the cases, respectively. In both algorithms, by comparing with the simulation results of the two different modes, it shows that the orderly charging and discharging mode with V2 G is obviously better than the disorderly charging and discharging mode in the aspects of alleviating the pressure of power grid, reducing system investment and improving energy efficiency.
基金by National Natural Science Foundation of China(62373142,62033014)Natural Science Foundation of Hunan Province(2025JJ70017,2022JJ50074).
文摘To address issues such as poor initial population diversity, low stability and local convergence accuracy, and easy local optima in the traditional Multi-Objective Artificial Hummingbird Algorithm (MOAHA), an Improved MOAHA (IMOAHA) was proposed. The improvements involve Tent mapping based on random variables to initialize the population, a logarithmic decrease strategy for inertia weight to balance search capability, and the improved search operators in the territory foraging phase to enhance the ability to escape from local optima and increase convergence accuracy. The effectiveness of IMOAHA was verified through Matlab/Simulink. The results demonstrate that IMOAHA exhibits superior convergence, diversity, uniformity, and coverage of solutions across 6 test functions, outperforming 4 comparative algorithms. A Wilcoxon rank-sum test further confirmed its exceptional performance. To assess IMOAHA’s ability to solve engineering problems, an optimization model for a multi-track, multi-train urban rail traction power supply system with Supercapacitor Energy Storage Systems (SCESSs) was established, and IMOAHA was successfully applied to solving the capacity allocation problem of SCESSs, demonstrating that it is an effective tool for solving complex Multi-Objective Optimization Problems (MOOPs) in engineering domains.
基金supported by a Horizontal Project on the Development of a Hybrid Energy Storage Simulation Model for Wind Power Based on an RT-LAB Simulation System(PH2023000190)the Inner Mongolia Natural Science Foundation Project and the Optimization of Exergy Efficiency of a Hybrid Energy Storage System with Crossover Control for Wind Power(2023JQ04).
文摘Present of wind power is sporadically and cannot be utilized as the only fundamental load of energy sources.This paper proposes a wind-solar hybrid energy storage system(HESS)to ensure a stable supply grid for a longer period.A multi-objective genetic algorithm(MOGA)and state of charge(SOC)region division for the batteries are introduced to solve the objective function and configuration of the system capacity,respectively.MATLAB/Simulink was used for simulation test.The optimization results show that for a 0.5 MW wind power and 0.5 MW photovoltaic system,with a combination of a 300 Ah lithium battery,a 200 Ah lead-acid battery,and a water storage tank,the proposed strategy reduces the system construction cost by approximately 18,000 yuan.Additionally,the cycle count of the electrochemical energy storage systemincreases from4515 to 4660,while the depth of discharge decreases from 55.37%to 53.65%,achieving shallow charging and discharging,thereby extending battery life and reducing grid voltage fluctuations significantly.The proposed strategy is a guide for stabilizing the grid connection of wind and solar power generation,capability allocation,and energy management of energy conservation systems.
文摘Fatty acids are the main constituents of vegetable oils.To determine the fatty acid compositions of small trade vegetable oils and some less well studied beneficial vegetable oils,and investigate their relationships with antioxidant activity and oxidative stability,gas chromatography-mass spectrometry was performed to characterize the associated fatty acid profiles.The antioxidant activity of vegetable oils,based on their DPPH-scavenging capacity(expressed as IC_(50) values),was used to assess their impact on human health,and their oxidative stability was characterized by performing lipid oxidation analysis to determine the oxidative induction time of fats and oils.In addition,correlation analyses were performed to examine associations between the fatty acid composition of the oils and DPPH-scavenging capacity and oxidative stability.The results revealed that among the assessed oils,coffee seed oil has the highest saturated fatty acid content(355.10 mg/g),whereas Garddenia jaminoides oil has the highest unsaturated fatty acid content(844.84 mg/g).Coffee seed oil was also found have the lowest DPPH IC_(50) value(2.30 mg/mL)and the longest oxidation induction time(17.09 h).Correlation analysis revealed a significant linear relationship(P<0.05)between oxidative stability and unsaturated fatty acid content,with lower contents tending to be associated with better oxidative stability.The findings of this study provide reference data for the screening of functional edible vegetable oils.
基金supported by National Natural Science Foundations of China(nos.12271326,62102304,61806120,61502290,61672334,61673251)China Postdoctoral Science Foundation(no.2015M582606)+2 种基金Industrial Research Project of Science and Technology in Shaanxi Province(nos.2015GY016,2017JQ6063)Fundamental Research Fund for the Central Universities(no.GK202003071)Natural Science Basic Research Plan in Shaanxi Province of China(no.2022JM-354).
文摘The multi-objective particle swarm optimization algorithm(MOPSO)is widely used to solve multi-objective optimization problems.In the article,amulti-objective particle swarm optimization algorithmbased on decomposition and multi-selection strategy is proposed to improve the search efficiency.First,two update strategies based on decomposition are used to update the evolving population and external archive,respectively.Second,a multiselection strategy is designed.The first strategy is for the subspace without a non-dominated solution.Among the neighbor particles,the particle with the smallest penalty-based boundary intersection value is selected as the global optimal solution and the particle far away fromthe search particle and the global optimal solution is selected as the personal optimal solution to enhance global search.The second strategy is for the subspace with a non-dominated solution.In the neighbor particles,two particles are randomly selected,one as the global optimal solution and the other as the personal optimal solution,to enhance local search.The third strategy is for Pareto optimal front(PF)discontinuity,which is identified by the cumulative number of iterations of the subspace without non-dominated solutions.In the subsequent iteration,a new probability distribution is used to select from the remaining subspaces to search.Third,an adaptive inertia weight update strategy based on the dominated degree is designed to further improve the search efficiency.Finally,the proposed algorithmis compared with fivemulti-objective particle swarm optimization algorithms and five multi-objective evolutionary algorithms on 22 test problems.The results show that the proposed algorithm has better performance.
基金Sichuan Science and Technology Program(No.2021ZDZX0009).
文摘Background Oxidative stress significantly impacts growth performance and liver function in piglets.Ferulic acid(FA)works as an antioxidant,however,the role and mechanism of FA in the regulation of diquat-induced oxidative stress in piglets are less known.This study was designed to investigate the effects of FA on growth performance and antioxi-dant capacity in piglets with diquat challenge.Methods Thirty-two healthy DLY(Duroc×Landrace×Yorkshire)piglets(13.24±0.19 kg)were randomly divided into one of two diets including 0 or 4 g/kg FA for 14 d.On d 15,all pigs were intraperitoneally injected diquat or sterile saline.Results Dietary supplementation with ferulic acid(FA)significantly improved the average daily gain(ADG)and decreased feed-gain ratio(F/G)of piglets.Here,dietary FA supplementation reduced serum aspartate aminotrans-ferase(AST),alanine aminotransferase(ALT)activities in diquat challenged piglets.Furthermore,diquat infusion increased reactive oxygen radicals(ROS)level in liver,decreased the activities of total superoxide dismutase(T-SOD)and glutathione peroxidase(GSH-Px),total antioxidant capacity(T-AOC)and increased malondialdehyde(MDA)con-tent in the liver and serum.Supplementation with FA significantly increased T-AOC and T-SOD activities and decreased MDA and ROS levels.FA down-regulated gene and protein expression of Keap1,and up-regulated protein expression of Nrf2 and HO-1 in the liver of piglets with diquat challenge.Importantly,diquat challenge increased the ratio of late apoptosis,increased serum levels of IL-1β,IL-18 and lactate dehydrogenase(LDH),and up-regulated pyroptosis-related genes in the liver.FA supplementation reduced the ratio of late apoptosis and down-regulated mRNA expression of Caspase-1.Accordingly,FA addition reduced concentration of IL-1β,IL-18,and LDH under diquat challenge.Conclusions Diquat-induced oxidative stress reduced growth performance and impaired liver function in piglets.Dietary FA supplementation enhanced the antioxidant capacity and reduced the degree of hepatocyte pyroptosis,thereby alleviating the oxidative damage in the liver and mitigating the impact of diquat on growth performance of piglets.
基金supported by the National Natural Science Foundation of China(Project No.5217232152102391)+2 种基金Sichuan Province Science and Technology Innovation Talent Project(2024JDRC0020)China Shenhua Energy Company Limited Technology Project(GJNY-22-7/2300-K1220053)Key science and technology projects in the transportation industry of the Ministry of Transport(2022-ZD7-132).
文摘This paper introduces the Surrogate-assisted Multi-objective Grey Wolf Optimizer(SMOGWO)as a novel methodology for addressing the complex problem of empty-heavy train allocation,with a focus on line utilization balance.By integrating surrogate models to approximate the objective functions,SMOGWO significantly improves the efficiency and accuracy of the optimization process.The effectiveness of this approach is evaluated using the CEC2009 multi-objective test function suite,where SMOGWO achieves a superiority rate of 76.67%compared to other leading multi-objective algorithms.Furthermore,the practical applicability of SMOGWO is demonstrated through a case study on empty and heavy train allocation,which validates its ability to balance line capacity,minimize transportation costs,and optimize the technical combination of heavy trains.The research highlights SMOGWO's potential as a robust solution for optimization challenges in railway transportation,offering valuable contributions toward enhancing operational efficiency and promoting sustainable development in the sector.
基金supported in part by the National Natural Science Foundation of China(62273310)the Natural Science Foundation of Zhejiang Province of China(LY22F030006,LZ24F030009)
文摘The increased demand for personalized customization calls for new production modes to enhance collaborations among a wide range of manufacturing practitioners who unnecessarily trust each other.In this article,a blockchain-enabled manufacturing collaboration framework is proposed,with a focus on the production capacity matching problem for blockchainbased peer-to-peer(P2P)collaboration.First,a digital model of production capacity description is built for trustworthy and transparent sharing over the blockchain.Second,an optimization problem is formulated for P2P production capacity matching with objectives to maximize both social welfare and individual benefits of all participants.Third,a feasible solution based on an iterative double auction mechanism is designed to determine the optimal price and quantity for production capacity matching with a lack of personal information.It facilitates automation of the matching process while protecting users'privacy via blockchainbased smart contracts.Finally,simulation results from the Hyperledger Fabric-based prototype show that the proposed approach increases social welfare by 1.4%compared to the Bayesian game-based approach,makes all participants profitable,and achieves 90%fairness of enterprises.
基金sponsored by R&D Program of Beijing Municipal Education Commission(KM202410009013).
文摘In the independent electro-hydrogen system(IEHS)with hybrid energy storage(HESS),achieving optimal scheduling is crucial.Still,it presents a challenge due to the significant deviations in values ofmultiple optimization objective functions caused by their physical dimensions.These deviations seriously affect the scheduling process.A novel standardization fusion method has been established to address this issue by analyzing the variation process of each objective function’s values.The optimal scheduling results of IEHS with HESS indicate that the economy and overall energy loss can be improved 2–3 times under different optimization methods.The proposed method better balances all optimization objective functions and reduces the impact of their dimensionality.When the cost of BESS decreases by approximately 30%,its participation deepens by about 1 time.Moreover,if the price of the electrolyzer is less than 15¥/kWh or if the cost of the fuel cell drops below 4¥/kWh,their participation will increase substantially.This study aims to provide a more reasonable approach to solving multi-objective optimization problems.
基金supported by grants from the Natural Science Foundation of Fujian Province(2021J011062)Minjiang Scholars Funding(GY-633Z21067).
文摘This study investigates the bond performance at the interfacial region shared by Ultra-High Performance Concrete(UHPC)and steel tubes through push-out tests.This study examines how changes in steel fiber volumetric ratio and thickness of steel tube influence the bond strength characteristics.The results show that as the enhancement of the steel tube wall thickness,the ultimate bond strength at the interface improves significantly,whereas the initial bond strength exhibits only slight variations.The influence of steel fiber volumetric ratio presents a nonlinear trend,with initial bond strength decreasing at low fiber content and increasing significantly as fiber content rises.Additionally,finite element(FE)simulations were applied to replicate the experimental conditions,and the outcomes showed strong correlation with the experimental data,confirming the exactitude of the FE model in predicting the bond behavior at the UHPC-Steel interface.These findings provide valuable insights for optimizing the design of UHPC-Filled steel tubes in high-performance structure.
基金funded by the National Natural Science Foundation of China(52167013)the Key Program of Natural Science Foundation of Gansu Province(24JRRA225)Natural Science Foundation of Gansu Province(23JRRA891).
文摘In the context of the“dual carbon”goals,to address issues such as high energy consumption,high costs,and low power quality in the rapid development of electrified railways,this study focused on the China Railways High-Speed 5 Electric Multiple Unit and proposed a mathematical model and capacity optimization method for an onboard energy storage system using lithium batteries and supercapacitors as storage media.Firstly,considering the electrical characteristics,weight,and volume of the storage media,a mathematical model of the energy storage system was established.Secondly,to tackle problems related to energy consumption and power quality,an energy management strategy was proposed that comprehensively considers peak shaving and valley filling and power quality by controlling the charge/discharge thresholds of the storage system.Thecapacity optimization adopted a bilevel programming model,with the series/parallel number of storage modules as variables,considering constraints imposed by the Direct Current to Direct Current converter,train load,and space.An improved Particle Swarm Optimization algorithm and linear programming solver were used to solve specific cases.The results show that the proposed onboard energy storage system can effectively achieve energy savings,reduce consumption,and improve power qualitywhile meeting the load and space limitations of the train.
基金funded by the National Key Research and Development Program of China(2024YFE0106800)Natural Science Foundation of Shandong Province(ZR2021ME199).
文摘The intermittency and volatility of wind and photovoltaic power generation exacerbate issues such as wind and solar curtailment,hindering the efficient utilization of renewable energy and the low-carbon development of energy systems.To enhance the consumption capacity of green power,the green power system consumption optimization scheduling model(GPS-COSM)is proposed,which comprehensively integrates green power system,electric boiler,combined heat and power unit,thermal energy storage,and electrical energy storage.The optimization objectives are to minimize operating cost,minimize carbon emission,and maximize the consumption of wind and solar curtailment.The multi-objective particle swarm optimization algorithm is employed to solve the model,and a fuzzy membership function is introduced to evaluate the satisfaction level of the Pareto optimal solution set,thereby selecting the optimal compromise solution to achieve a dynamic balance among economic efficiency,environmental friendliness,and energy utilization efficiency.Three typical operating modes are designed for comparative analysis.The results demonstrate that the mode involving the coordinated operation of electric boiler,thermal energy storage,and electrical energy storage performs the best in terms of economic efficiency,environmental friendliness,and renewable energy utilization efficiency,achieving the wind and solar curtailment consumption rate of 99.58%.The application of electric boiler significantly enhances the direct accommodation capacity of the green power system.Thermal energy storage optimizes intertemporal regulation,while electrical energy storage strengthens the system’s dynamic regulation capability.The coordinated optimization of multiple devices significantly reduces reliance on fossil fuels.
文摘Since the discovery of carbon dots(CDs)in 2004,the unique photoluminescence phenomenon of CDs has attracted widespread attention.However,the molecular weight of CDs has not been adequately quantified at present,due to CDs are atomically imprecise and their molecular weight distribution is broad.In this paper,a series of Pluronic-modified CDs were prepared and the structure of the CDs was briefly analyzed.Subsequently,a molecular weight measurement method based on colligative properties was developed,and the correction coefficient in the algorithm was briefly analyzed.The calculated molecular weight was applied to the determination of surface adsorption capacity.This work provided a method for averaging the molecular weight of atomically imprecise particulate materials,which is expected to provide new opportunities in related fields.
基金supported in part by the Scientific Research Fund of National Natural Science Foundation of China(Grant No.62372168)the Hunan Provincial Natural Science Foundation of China(Grant No.2023JJ30266)+2 种基金the Research Project on teaching reform in Hunan province(No.HNJG-2022-0791)the Hunan University of Science and Technology(No.2022-44-8)the National Social Science Funds of China(19BZX044).
文摘Multi-instance image generation remains a challenging task in the field of computer vision.While existing diffusionmodels demonstrate impressive fidelity in image generation,they often struggle with precisely controlling each object’s shape,pose,and size.Methods like layout-to-image and mask-to-image provide spatial guidance but frequently suffer from object shape distortion,overlaps,and poor consistency,particularly in complex scenes with multiple objects.To address these issues,we introduce PolyDiffusion,a contour-based diffusion framework that encodes each object’s contour as a boundary-coordinate sequence,decoupling object shapes and positions.This approach allows for better control over object geometry and spatial positioning,which is critical for achieving high-quality multiinstance generation.We formulate the training process as a multi-objective optimization problem,balancing three key objectives:a denoising diffusion loss to maintain overall image fidelity,a cross-attention contour alignment loss to ensure precise shape adherence,and a reward-guided denoising objective that minimizes the Fréchet distance to real images.In addition,the Object Space-Aware Attention module fuses contour tokens with visual features,while a prior-guided fusion mechanism utilizes inter-object spatial relationships and class semantics to enhance consistency across multiple objects.Experimental results on benchmark datasets such as COCO-Stuff and VOC-2012 demonstrate that PolyDiffusion significantly outperforms existing layout-to-image and mask-to-image methods,achieving notable improvements in both image quality and instance-level segmentation accuracy.The implementation of Poly Diffusion is available at https://github.com/YYYYYJS/PolyDiffusion(accessed on 06 August 2025).
文摘To investigate the evolution of load-bearing characteristics of pre-stressed beams throughout their service life and to provide a basis for accurately assessing the actual working state of damaged pre-stressed concrete T-beams,destructive tests were conducted on full-scale pre-stressed concrete beams.Based on the measurement and ana-lysis of beam deflection,strain,and crack development under various loading levels during the research tests,combined with the verification coefficient indicators specified in the codes,the verification coefficients of bridges at different stages of damage can be examined.The results indicate that the T-beams experience complete,incom-plete linear,and non-linear stages during the destructive test process.In the complete linear elastic stage,both the deflection and bottom strain verification coefficients comply with the specifications,indicating a good structural load-bearing capacity no longer adheres to the code’s requirements.In the non-linear stage,both coefficients exhi-bit a sharp increase,resulting in a further decrease in the structure’s load-bearing capacity.According to the pro-visions of the current code,the beam can be in the incomplete linear stage when both values fall within the code’s specified range.The strain verification coefficient sourced from the compression zone at the bottom of theflange is not recommended for assessing the bridge’s load-bearing capacity.
文摘In the last decade,space solar power satellites(SSPSs)have been conceived to support net-zero carbon emissions and have attracted considerable attention.Electric energy is transmitted to the ground via a microwave power beam,a technology known as microwave power transmission(MPT).Due to the vast transmission distance of tens of thousands of kilometers,the power transmitting antenna array must span up to 1 kilometer in diameter.At the same time,the size of the rectifying array on the ground should extend over a few kilometers.This makes the MPT system of SSPSs significantly larger than the existing aerospace engineering system.To design and operate a rational MPT system,comprehensive optimization is required.Taking the space MPT system engineering into consideration,a novel multi-objective optimization function is proposed and further analyzed.The multi-objective optimization problem is modeled mathematically.Beam collection efficiency(BCE)is the primary factor,followed by the thermal management capability.Some tapers,designed to solve the conflict between BCE and the thermal problem,are reviewed.In addition to these two factors,rectenna design complexity is included as a functional factor in the optimization objective.Weight coefficients are assigned to these factors to prioritize them.Radiating planar arrays with different aperture illumination fields are studied,and their performances are compared using the multi-objective optimization function.Transmitting array size,rectifying array size,transmission distance,and transmitted power remaine constant in various cases,ensuring fair comparisons.The analysis results show that the proposed optimization function is effective in optimizing and selecting the MPT system architecture.It is also noted that the multi-objective optimization function can be expanded to include other factors in the future.
文摘To exchange experiences and progress in standardization capacity building at home and abroad,the sub-forum on capacity building and creating leadership in standardization was held on July 9.The sub-forum was hosted by the Qingdao Municipal People’s Government and co-organized by the International Standardization Training Base(Qingdao),Qingdao University,International Standardization Outstanding Contribution Foundation,and Shandong National Standards Center of Technical Evaluation,which was expected to inject new vitality into the industrial development and provide new ideas for improving the talent cultivation system.
文摘Impinging jet arrays are extensively used in numerous industrial operations,including the cooling of electronics,turbine blades,and other high-heat flux systems because of their superior heat transfer capabilities.Optimizing the design and operating parameters of such systems is essential to enhance cooling efficiency and achieve uniform pressure distribution,which can lead to improved system performance and energy savings.This paper presents two multi-objective optimization methodologies for a turbulent air jet impingement cooling system.The governing equations are resolved employing the commercial computational fluid dynamics(CFD)software ANSYS Fluent v17.The study focuses on four controlling parameters:Reynolds number(Re),swirl number(S),jet-to-jet separation distance(Z/D),and impingement height(H/D).The effects of these parameters on heat transfer and impingement pressure distribution are investigated.Non-dominated Sorting Genetic Algorithm(NSGA-II)and Weighted Sum Method(WSM)are employed to optimize the controlling parameters for maximum cooling performance.The aim is to identify optimal design parameters and system configurations that enhance heat transfer efficiency while achieving a uniform impingement pressure distribution.These findings have practical implications for applications requiring efficient cooling.The optimized design achieved a 12.28%increase in convective heat transfer efficiency with a local Nusselt number of 113.05 compared to 100.69 in the reference design.Enhanced convective cooling and heat flux were observed in the optimized configuration,particularly in areas of direct jet impingement.Additionally,the optimized design maintained lower wall temperatures,demonstrating more effective thermal dissipation.
基金supported by the Open Fund of Guangxi Key Laboratory of Building New Energy and Energy Conservation(Project Number:Guike Energy 17-J-21-3).
文摘With the development of renewable energy technologies such as photovoltaics and wind power,it has become a research hotspot to improve the consumption rate of new energy and reduce energy costs through algorithm improvement.To reduce the operational costs of micro-grid systems and the energy abandonment rate of renewable energy,while simultaneously enhancing user satisfaction on the demand side,this paper introduces an improvedmultiobjective Grey Wolf Optimizer based on Cauchy variation.The proposed approach incorporates a Cauchy variation strategy during the optimizer’s search phase to expand its exploration range and minimize the likelihood of becoming trapped in local optima.At the same time,adoptingmultiple energy storage methods to improve the consumption rate of renewable energy.Subsequently,under different energy balance orders,themulti-objective particle swarmalgorithm,multi-objective grey wolf optimizer,and Cauchy’s variant of the improvedmulti-objective grey wolf optimizer are used for example simulation,solving the Pareto solution set of the model and comparing.The analysis of the results reveals that,compared to the original optimizer,the improved optimizer decreases the daily cost by approximately 100 yuan,and reduces the energy abandonment rate to zero.Meanwhile,it enhances user satisfaction and ensures the stable operation of the micro-grid.
基金supported by the Basic Public Welfare Research Program of Zhejiang Province(No.LGN22E050005).
文摘This study proposes a multi-objective optimization framework for electric winches in fiber-reinforced plastic(FRP)fishing vessels to address critical limitations of conventional designs,including excessive weight,material inefficiency,and performance redundancy.By integrating surrogate modeling techniques with a multi-objective genetic algorithm(MOGA),we have developed a systematic approach that encompasses parametric modeling,finite element analysis under extreme operational conditions,and multi-fidelity performance evaluation.Through a 10-t electric winch case study,the methodology’s effectiveness is demonstrated via parametric characterization of structural integrity,stiffness behavior,and mass distribution.The comparative analysis identified optimal surrogate models for predicting key performance metrics,which enabled the construction of a robust multi-objective optimization model.The MOGA-derived Pareto solutions produced a design configuration achieving 7.86%mass reduction,2.01%safety factor improvement,and 23.97%deformation mitigation.Verification analysis confirmed the optimization scheme’s reliability in balancing conflicting design requirements.This research establishes a generalized framework for marine deck machinery modernization,particularly addressing the structural compatibility challenges in FRP vessel retrofitting.The proposed methodology demonstrates significant potential for facilitating sustainable upgrades of fishing vessel equipment through systematic performance optimization.
