Multi-objective Analysis Research Articles

Design and test of tangential and longitudinal-axial threshing and separating unit for wheat

To achieve low loss and efficient wheat harvest in the Huanghuaihai region of China, this paper designed a tangential and longitudinal-axial threshing and separating unit (TLTSU). Key components such as the tangential threshing rotor (TTR), longitudinal-axial threshing rotor (LTR), and tangential concave were designed. Dynamic balance simulation of the threshing rotors was done to obtain the reaction forces at supporting ends of the threshing rotors and the amount of unbalance, and the amount of unbalance of the LTR was corrected to meet the standard requirement. Response surface optimization experiments of wheat threshing were conducted using throughput, TTR speed, and LTR speed as influencing factors and entrainment loss rate and impurity rate as test indicators. The regression models for the threshing device's entrainment loss rate and impurity rates were established. Through multi-objective parameter optimization analysis, the optimal threshing parameter combination for TLTSU was determined to be throughput of 8 kg s−1, TTR speed of 557 r·min−1, and LTR speed of 900 r·min−1. A validation experiment was conducted on this parameter combination, and the results showed that the entrainment loss rate was 0.27 % and the impurity rate was 28.23 %. The relative errors between the evaluation indicators and the predicted values of their regression models were 3.85 % and 1.4 %, respectively, which were less than 5 %, which proved the correctness of the regression model and provided a reference for the design of threshing and separating unit of wheat combine harvester.

An integrated group decision making framework for business performance evaluation of performing arts enterprises with probabilistic hesitant fuzzy IDOCRIW-MOOSRA method

There is a lack of domestic and foreign research on the evaluation and improvement strategies of business performance of performing arts enterprises, especially in the context of the “restructuring” of cultural groups in China. Most of the existing studies are distributed in bulk, not only lacking in theoretical depth, but also lacking in systematization to a certain extent, which shows that the existing studies have not fully formed a mature and valuable theoretical system. The business performance evaluation of performing arts enterprises is a multiple attributes group decision making (MAGDM). This paper constructs a novel probabilistic hesitant fuzzy Multi-Objective Optimization Simple Ratio Analysis (PHF-MOOSRA) model based on the integrated determination of objective criteria weights (IDOCRIW) under the probabilistic hesitant fuzzy sets (PHFSs) for this issue. The PHFSs provides an evaluation circumstance containing more information which make the final decision-making results more accurately. Additionally, the IDOCRIW method separately and the MOOSRA method based on the MOORA method is proposed in PHFSs circumstance in this model. In the end, this model is then applied in a numerical case study for business performance evaluation of performing arts enterprises and compare this model with other existing methods.

Multi-objective analysis of solid-state joints: Effect of tool pin shoulder clearance on thick dissimilar aluminum plates

ABSTRACT Friction stir welding is a well-established technique developed extensively over the past two decades for various applications. This study investigates the effect of repositioning the tool pin shoulder (0.18 mm) above the surface of a 12 mm thick dissimilar aluminum alloy (AA6063-AA5083) on tensile strength, welding temperature, and micro-hardness. Trials were conducted according to the L9 Taguchi method and further simulated using ABAQUS software. The input parameters spindle speed, tool pin length, and traversing speed were each tested at three levels. The impact of these parameters on weld quality was analyzed using ANOVA. Optimization of the process to achieve the desired temperature and tensile strength was carried out using grey relational analysis for both experimental and simulated results. Mathematical models were generated using the multi-variable regression method and the response surface method (RSM). Predicted data from the RSM model were compared to experimental outcomes, revealing a maximum deviation of 8.69% for temperature and 5.17% for tensile strength. Furthermore, the study demonstrated that ABAQUS accurately simulates the friction stir welding process with an accuracy of up to 93.71%.

Multi-objective optimization analysis of combined heating and cooling transcritical CO2 system integrated with mechanical subcooling utilizing hydrocarbon mixture based on machine learning

To meet the simultaneous needs of high temperature disinfection and freezing in the field of food processing, a new concept of combined heating and cooling transcritical CO2 system integrated with dedicated mechanical subcooling utilizing hydrocarbon mixture is proposed. The system performance in terms of thermodynamics, economy and environment is studied and compared with the baseline combined heating and cooling transcritical CO2 system and four traditional combined heating and cooling solutions, considering the influence of temperature glide and the heat transfer deterioration. The new proposed system is further optimized by using the machine learning method of artificial neural network and non-dominated sorting genetic algorithm. Multi-objective optimization is conducted considering the objective function of energy efficiency, initial capital cost and life cycle carbon emissions of the new system, to obtain the optimum components and concentration ratio of the hydrocarbon mixture. The results indicate the thermodynamic performance and environmental benefits of subcooling subsystem with hydrocarbon mixture are better than those of the pure system. In contrast to that using pure R290 and R601, the coefficient of performance is enhanced by 8.20 % and 8.13 % and the life cycle carbon emission is reduced by 8.54 % and 9.31 %, respectively, when R290/R601 (50/50) is used. However, the initial capital cost is 9.25 % and 10.23 % higher than that of pure R290 and R601, respectively. Finally, the hydrocarbon mixture corresponding to the optimal design point is R1270/R601a (53/47), the corresponding discharge pressure is 12.86 MPa, and the subcooling degree is 37.50 °C. This study can provide a theoretical reference for the application of CO2 refrigeration and heat pump technology.

Piezoelectric impedance-based high-accuracy damage identification using sparsity conscious multi-objective optimization inverse analysis

Two elements are essential in structural health monitoring utilizing dynamic responses: response measurement with high-frequency contents, i.e., small characteristic wavelengths, that can adequately reflect damage features, and effective inverse identification analysis that is however oftentimes under-determined. The advancement of smart structure integration has led to active interrogation through frequency-sweeping piezoelectric impedance measurement at high frequency range. In this research we develop a multi-objective optimization formulation for the identification of damage location and severity utilizing piezoelectric impedance. While one optimization objective is to match the response measurement with finite element model prediction in the damage parametric space, the other is the number of locations of damage, i.e., the sparsity of damage index as the solution vector, since damage usually occurs within a small number of locations. This multi-objective formulation fits well the under-determined nature of damage identification, as it naturally provides multiple solutions as basis for further elucidation. The challenge remaining is how to find a small solution set that can include the actual damage scenario. Here we develop a novel inverse identification framework utilizing the intelligent swarm optimizer which possesses flexibility for enhancement. We first embed a sparsity enforcement process into the population generation of the optimizer, which yields a solution repository intrinsically possessing sparsity. We then apply reinforcement learning so the agents can adaptively opt for local strategies with the aim of enriching the searching patterns to diversify the solutions. Through the incorporation of a Q-table, searching toward more promising directions will be rewarded. Our case analyses employing experimental data indicate that this sparsity-conscious multi-objective particle swarm optimization technique can lead to a small solution set which generally encompasses the true damage scenario. This effectively solves the structural damage identification problem with piezoelectric impedance measurement.

Statistical modelling of depth milling in Ti-6AL4V using abrasive water jet machining

Multi-objective grey relational analysis optimization technique and multiple regression analysis were employed to determine the optimum values for depth of cut, surface roughness ( Ra), and kerf at entry and exit ([Formula: see text] and [Formula: see text]), for abrasive waterjet machining of Ti6AL4V materials. This method highlights a new process to extend the grey relational analysis technique for determining the optimum conditions for obtaining the best quality characteristics. The input parameters of the study were water pressure ( Wp), transverse speed ( Ts), abrasive mass flow rate ( Amf), abrasive orifice size ( Aos), nozzle/orifice diameter ratio ( N/Odia). The experiments were conducted as per the Taguchi-based L27 orthogonal array. The grey relational analysis technique found that Ts was the most significant parameter on the combined outputs. The regression models developed had an R2 of 81.58%, 79.79%%, 77.20%, and 74.39% for depth of cut, Ra, [Formula: see text] and [Formula: see text], respectively. Additionally, the analysis of variance showed that Wp and Aos had a significant influence on the output parameters. The predicted values were found to be reasonably close with the experimental values, and the maximum average deviation was 8.15% for [Formula: see text].

Multi-objective analysis of the Sand Hypoplasticity model calibration

Multi-objective analysis of the Sand Hypoplasticity model calibration

Multi-objective optimization of liquid cold plate with v-shaped ribs for lithium-ion batterys

The performance of lithium-ion batteries is affected by the operational temperature significantly for the NEVs, and should be below 338 K and 5 K, respectively in the actual project. An efficient thermal management system is essential for the battery, as it would ensure the safe operation and increase the battery life. In this study, the LCP with V-shaped ribs is applied to improve the heat transfer characteristics for guaranteeing the safe operational temperature of the battery. Based on the battery thermal models, the accuracy of numerical simulation through classical experimental correlation is verified, and is adopted to investigate the effects of different design factors on the heat dissipation of the battery, including the ribbed shaped, the distance between adjacent ribs and the inlet velocity of the coolant. The maximum temperature and the temperature difference of the battery and the pressure drop of the channel are taken as the design objectives. An orthogonal test and an entropy weighted-TOPSIS method are used to optimize the results with multi-objective analysis, then the optimal case of design parameters is obtained. The optimal case for the LCP is the ribbed shape of Model 2, the distance between the adjacent ribs of 30 mm and the inlet velocity of 0.3 m/s. A good balance is achieved between the heat dissipation of the battery pack and the pressure drop of the channel. The optimal case can reduce the maximum temperature and the temperature difference of the battery by 7.41K and 4.94K compared with the unoptimized cases, meanwhile the pressure drop is also effectively controlled.

Goal Setting and Instructional Design in Music Education Based on Bloom’s Cognitive Hierarchy Theory

Abstract This paper first introduces Bloom’s cognitive hierarchy theory of music education goal classification and revises it to construct a new version of Bloom’s educational goal-setting system. After that, it adopts multi-objective hierarchical analysis (AHP) to calculate the weights of the music education objectives assessed by experts and optimizes the convergence of AHP based on Bergh’s improved particle swarm (PSO) optimization algorithm. New music education objectives and teaching contents were created based on the experimental results. The CR values of the six cognitive subprocesses of the second-level evaluation indexes were 0.0001, 0.0011, 0.0014, 0.0014, 0.0023, and 0.0029, respectively. The results of the consistency test of the first-level and second-level indexes were all CR<0.05, indicating that their consistency and trustworthiness were extremely high. The top three indicators in terms of the weights of the secondary indicators are inference, recall and differentiation, whose weights are 0.5723, 0.5276 and 0.5192, respectively; the three indicators with the lowest proportion are interpretation (0.2422), normalization (0.2661) and creation (0.3201). The purpose of this paper is to create a music course instructional design system with a 4-dimensional and 18-indicator system and evaluate the importance of each indicator. It fully embodies the basic spirit of quality education and reflects the common requirements of the new curriculum system of colleges and universities that combines epochal, basic, and selective. The teaching design system proposed in this paper is expected to serve as a reference for the current music teaching reform.

Optimization of Enterprise Economic Management Model and Risk Control Based on Marginal Analysis Approach

Abstract As an effective economic decision-making tool in enterprise economic management, Marginal Analysis is crucial to reflect the small change relationship between costs and benefits. This paper aims to develop a multi-objective marginal analysis optimization algorithm (MMAOA) that utilizes marginal analysis to optimize the enterprise economic management model and risk control. This algorithm's improvement of the population initialization method and constraint handling enhances the quality of understanding and search efficiency. In this study, an improved population initialization method is designed to ensure that the population is within the upper and lower limits of the output power. Then the constraints are reasonably handled by the penalty function method and the correction method to enhance the feasibility and robustness of the algorithm. Guided by the concept of Pareto optimality, MMAOA is successfully applied to multi-objective optimization problems, and the algorithm can efficiently generate a set of Pareto optimal solutions under the consideration of equilibrium constraints and inequality constraints, in which the objective function values of certain optimization instances are improved by more than 10%. Following marginal analysis, this study presents the disparity in ROI between the East and West and the three stages of agricultural economic efficiency. This article provides a new optimization tool for enterprise financial management and a quantitative analysis method for risk assessment.

Health and comfort oriented automatic generative design and optimization of residence space layout: An integrated data-driven and knowledge-based approach

During the initial design phase, space layout design plays a critical role in ensuring the ultimate performance of a residence. However, physical performance like energy consumption is usually prioritized, while health and comfort performance of residence is less explored. This study proposes an integrated data-driven and knowledge-based approach for automatic generative design of residence space layout focusing on health and comfort performance. First, explicit knowledge about key parameters of the layout design is obtained through quantitative analysis of large-scale health data. Second, with health and comfort as performance oriented, adjacency preference and noise score are introduced as objective functions and automatic generative design is carried out to obtain multiple layout alternatives. Finally, multi-objective deviation analysis is conducted to determine the optimal layout scheme. This study offers a practical and efficient tool through the whole design process, including pre-parameters analysis, during-process generation, and post-scheme evaluation, which contributes to improve decision-making.

Multi-objective analysis of performance in mileage based on the minimization of several vehicle polluting features

Multi-objective analysis of performance in mileage based on the minimization of several vehicle polluting features

Analysis on data center power supply system based on multiple renewable power configurations and multi-objective optimization

With rapid development of data center industry, achieving low energy consumption and costs become important. How to provide an optimal configuration on renewable distributed energy systems combining data centers is challenging. This work discussed possible hybrid configurations by combining diesel, photovoltaic, wind, and battery capacity in data center. For each mode, renewable penetration (RP) and the Levelized Cost of Electricity (LCOE) were used to evaluate the performance of system. A multi-objective optimization analysis of the case is performed based on the entropy weighted-TOPSIS method, genetic algorithm, and NSGA-II aiming to obtain both a high RP and a low LCOE. Then, comparative research on optimal power configuration with conventional or renewable energy was conducted. The results show that it is recommended to take a high-rated PV power. When wind power is taken, it is only meaningful when combined with PV power system. For the hybrid renewable power system without an energy storage unit, it's easy to realize a lower LCOE compared to Diesel mode, and its realizable maximum RP is 28.31 %. When battery energy storage is taken in the hybrid system, six optimal configurations are recommended which can achieve the lowest cost of 0.21 $/kWh and the highest RP of 44.5 %.

Multi-obective performance analysis of a wind power plant equipped with a PAT system

In conventional wind power plants, the wind turbine drives an electrical generator equipped with an AC/DC converter for battery charging purposes and with an inverter inverter to supply power to the grid / AC load. This approach exhibits some drawbacks like the energy losses associated to the power electronics and the cost and the waste management of the battery storage systems.To overcome such drawbacks, this work proposes a new plant scheme, where the battery system has been removed (or strongly reduced) and the storage task is accomplished by a pumping system equipped with a reversible hydraulic machinery which can operate both as centrifugal pump and a hydraulic turbine (called Pump as Turbine). In this scenario, when the wind velocity is quite high, the reversible hydraulic machinery, working as centrifugal pump, will store energy in the form of potential energy pumping fluid to an elevated water reservoir. On the contrary, in low wind conditions, the Pump as Turbine will operate as a hydraulic turbine, helping the wind turbine to overcome the external mechanical load. This solution results in a number of advantages: lower plant costs, longer Pump as Turbine life with lesser maintenance and reduced waste costs.The present work focuses on a multi-objective performance analysis of the proposed wind system power scheme. Specifically, this multi-objective sensitivity analysis will be addressed to the net potential energy stored by the reversible hydraulic machinery, the extracted wind energy and the torque supplied by the Pump as Turbine in hydraulic turbine mode. Such an analysis will considered as design variables the Pump as Turbine (centrifugal pump mode) specific speed (pump geometry), the gear box transmission ratio between the centrifugal pump and the wind turbine and the pump head. The aim of this analysis is to evaluate the design variables range which could lead to determine the set of the multi-objective wind systems optimal design.

Optimizing biochar-based geopolymer composites for enhanced water resistance in asphalt mixes: an experimental, microstructural, and multi-objective analysis

Due to increased traffic and environmental concerns, this study addresses challenges in conventional asphalt concrete. Our focus is on enhancing the water resistance of asphalt mixes through the optimization of both the asphalt binder and the biochar-based geopolymer composite. We employ experiments and response surface methodology to assess their impact on volume, Marshall parameters, and water resistance. Asphalt binders were evaluated within the range of 4–6%, while biochar-based geopolymer composite levels varied from 0 to 4%. According to the findings, the incorporation of the biochar-based geopolymer composite improves asphalt properties, stiffness, and temperature sensitivity. Response surface methodology (RSM) was utilized to construct robust mathematical models with high R2 values (90%) and low p-values. Multi-objective optimization indicated that optimal content levels were 4.56% for the binder and 2.71% for the biochar-based geopolymer composite. Model accuracy was confirmed with less than a 5% error in validation tests. The research also identified structural changes in the asphalt binder caused by the BGC Si–O phase. Additionally, the leaching value for both BGC and BGC-MAB asphalt concrete was found to be substantially below the regulatory limit, demonstrating the environmental safety of incorporating BGC into the asphalt sector.

A novel framework for FMEA using evidential BWM and SMAA-MARCOS method

This paper presents a novel failure mode and effect analysis (FMEA) framework as a formal design method to ensure safety and reliability. FMEA is used to identify potential failure modes (FMs), and it is crucial to determine the weights of risk factors and prioritize FMs. In this work, we propose a comprehensive framework that integrates the Dempster-Shafer theory, best-worst method (BWM), stochastic multi-objective acceptability analysis (SMAA), and measurement of alternatives and ranking according to compromise solution (MARCOS) to address this problem. To capture the uncertainty caused by the loss of information, the Dempster-Shafer theory is applied for dealing with the uncertainty about risk factors and FMs in terms of linguistic information. Based on the comprehensive evidential preference interval vectors of risk factors constructed by Dempster-Shafer theory, an evidential BWM combined with SMAA is proposed to determine the optimal set of risk factor weights. Meanwhile, based on the constructed interval belief interval decision matrix of FMs to risk factors constructed by Dempster-Shafer theory, an evidential SMAA-MARCOS method is proposed for determining the risk priority of FMs. Further, we conduct a case study to evaluate the risk of equipment in an automobile manufacturing enterprise. A sensitivity and comparative analysis are also conducted to demonstrate the effectiveness and superiority of the proposed framework.

Multi-Objective Optimization Analysis of Auxiliary Flux Modulator Magnetic Gear with Unequal Magnetic Poles

Multi-Objective Optimization Analysis of Auxiliary Flux Modulator Magnetic Gear with Unequal Magnetic Poles

Analysis of the Cultivation Mechanism of Innovation and Entrepreneurship Ability of College Students in the Context of Information Age

Abstract In this paper, under the background of the information era, according to the control theory of education of Innovation and Creation in institutions of higher education, the evaluation index system of education of Innovation and Creation resources is constructed. In order to optimize the allocation of education of Innovation and Creation resources in institutions of higher education, multi-objective optimization analysis of education of Innovation and Creation resources in institutions of higher education is carried out and combined with the objective function and indicator weights as well as the MOPSO algorithm, which in turn completes the multi-objective optimization analysis model. We establish the research variables, analyze and research scheme, and carry out an empirical analysis of the education of Innovation and Development in higher education institutions against the backdrop of information technology. These form the basis of the study’s methodology for teaching Creative Thinking and Creation mechanisms in higher education institutions. The findings indicate that prior to the experiment, the model’s resource allocation efficiency for Innovation and Creation education varied significantly amongst schools, ranging from a minimum of 0.122 to a maximum of 0.186; after optimization, the allocation efficiency was 0.188, with a mean increase of 20.88%, thereby realizing the optimization of Innovation and Creation education resource allocation.Regarding the optimization of education for innovation and the creation of resource allocation. On education of Innovation and Creation, the interaction term of new entrepreneurship education and entrepreneurship includes the regression coefficient of education of Innovation and Creation*entrepreneurship is 0.038 (Model16: β = 0.038, p<0.001), and it is significant at the level of 0.001, and there is always a positive interaction effect between education of Innovation and Creation and entrepreneurship in the influence on intention. This study can provide a better platform for entrepreneurial practice for students, which is of great importance in enhancing their innovation consciousness and practical abilities in college.

Multi-objective optimization of an endoreversible closed Atkinson cycle

Abstract Based on finite-time-thermodynamic theory and the model established in previous literature, the multi-objective optimization analysis for an endoreversible closed Atkinson cycle is conducted through using the NSGA-II algorithm. With the final state point temperature (T 2) of cycle compression process as the optimization variable and the thermal efficiency (η), the dimensionless efficient power ( E ̄ P ${\bar{E}}_{P}$ ), the dimensionless ecological function ( E ̄ $\bar{E}$ ) and the dimensionless power ( P ̄ $\bar{P}$ ) as the optimization objectives, the influences of T 2 on the four optimization objectives are analyzed, multi-objective optimization analyses of single-, two-, three- and four-objective are conducted, and the optimal cycle optimization objective combination is chosen by using three decision-making methods which include LINMAP, TOPSIS, and Shannon Entropy. The result shows that when four-objective optimization is conducted, with the ascent of T 2, P ̄ $\bar{P}$ descends, η ascends, both E ̄ $\bar{E}$ and E ̄ P ${\bar{E}}_{P}$ firstly ascend and then descend. In this situation, the deviation index is the smallest and equals to 0.2657 under the decision-making method of Shannon Entropy, so its optimization result is the optimal. The multi-objective optimization results are able to provide certain guidelines for the design of practical closed Atkinson cycle heat engine.

Life-cycle-based multi-objective optimal design and analysis of distributed multi-energy systems for data centers

The widespread deployment of data centers has contributed to a dramatic increase in energy consumption and carbon emissions. To solve this problem, this study proposes a distributed multi-energy system (DMES) driven by solar, wind, geothermal and natural gas and its life-cycle-based multi-objective optimization model considered energy, economy, and environment. Moreover, a novel operation strategy based on load characteristics is presented for energy flow allocation and a multi-objective grasshopper optimization algorithm is improved for model solving. Case studies are conducted on a practical data center in Qinghai, China. Based on the DMES capacity configuration and scheduling results, the cooling, heating and power supply can satisfy the demand of the data center while generating over 30% of the power from renewable sources. In addition, carbon tax is considered in the optimization model, which accounts for 4% of the life-cycle cost. Furthermore, the sensitivity of carbon tax, equipment lifespan and predicted mean vote is analyzed, obtaining that these factors have some influence on the performance of the system. Finally, the uncertainty impact on system planning is studied, concluding that the designed system exhibits high robustness levels when renewable energy forecast error is less than 20% or load forecast error is less than 10%.