Multi-objective Genetic Algorithm Research Articles

Parametric optimization of selected auxetic structures

AbstractAuxetic materials exhibit an interesting, counterintuitive behavior—when subjected to uniaxial tension, they stretch laterally, and when uniaxially compressed, they shrink laterally. In contrast to conventional materials, in auxetics, the value of Poisson’s ratio is negative. Behavior of auxetic materials is an effect of their internal structures. The auxetic effect depends mostly on the geometry of their internal unit cells and not on the properties of the bulk material. This paper presents the results of parametric optimization of selected two-dimensional auxetic unit cells with the aim to identify the geometrical parameters which exhibit the strongest influence on the value of Poisson’s ratio in each unit cell, and to identify geometries which exhibit the strongest auxetic effect. The optimization was conducted through numerical simulation with the use of the finite element method in commercial software. Response surface optimization and multi-objective genetic algorithm (MOGA) were applied. Obtained candidate geometries were verified via additional FEM analyses and confirmed to have improved auxetic effect and reduced equivalent stress. 5 × 5 structures composed of reference and optimized geometries of analyzed unit cells were subjected to similar analyses and it was confirmed that the optimization of singular unit cells caused an improvement of auxetic effect and reduction in equivalent stress in regular structures composed of multiple unit cells.

Study on the screening performance and parameter optimization of linear vibrating screen under the influence of sieve plate airflow

Screening is an important step in the preparation of machine made sand, and the screening performance of the screening machine is the key to improving the production efficiency and quality of machine made sand. This article first explores the influence of airflow generated by sieve plate vibration on particle motion on the sieve through numerical simulation, and verifies the reliability of the simulation model through experimental prototypes. Then, based on the CFD-DEM coupling model, the relationship between screening parameters and screening performance was studied. Finally, based on the screening results and the BP neural network optimized by the sparrow search algorithm, an evaluation model for screening parameters and screening performance was established, and a multi-objective genetic algorithm was used to optimize the high-performance screening parameter combination scheme. The comprehensive optimal performance parameters were obtained: vibration frequency of 15.5885 Hz, amplitude of 2.3789 mm, vibration direction angle of 50.5227, sieve obliquity of 16.6676, and feed rate of 20921 pieces/s.

Multi-objective design optimization of polymer spur gears using a hybrid approach

Polymer gears are used in applications requiring small to moderate loads to effectively transmit power and use the limited place available as possible. Various commercial standards have been provided designers with the rating criteria and acquaintance of different polymer material properties for the process of design. However, the result was unsatisfactory in terms of economy, time, and the optimality of the product. Thus, classic and stochastic algorithms have been embraced to reach the best design of polymer gears. Taking advantage of the former and latter algorithm’ methods, optimal design of gears could be attained with an increased gear life span and decreased failure modes. In this study, polyoxymethylene (POM) spur gear set has been optimized combining the mathematical model from plastic standards and hybrid optimization approach of multi-objective genetic algorithm (MOGA) and sequential quadratic programming (SQP). Weight and power loss were the objective functions. Five design variables were optimized with the satisfaction of bending and contact stresses, temperature, wear, and deformation as constraints. Solutions of the problem were formulated as Pareto optimal set. The results of multi-objective were compared with previously published single-objective optimization. The results favored multi-objective optimization (82.67%, 31.39% reduction in weight and power loss respectively) as it gave the best applicable solution fitting in real life situations. The results also went hand in hand with literature confirming the efficiency of the proposed algorithm. With the variation of operating parameters, various optimal designs could be obtained where the designers can choose the design that is suitable for a particular application.

Optimizations of structure stiffnesses and sensitivities of Y-type six-axis force/torque sensor

Purpose The six-axis force/torque sensor based on a Y-type structure has the advantages of simple structure, small space volume, low cost and wide application prospects. To meet the overall structural stiffness requirements and sensor performance requirements in robot engineering applications, this paper aims to propose a Y-type six-axis force/torque sensor. Design/methodology/approach The performance indicators such as each component sensitivities and stiffnesses of the sensor were selected as optimization objectives. The multiobjective optimization equations were established. A multiple quadratic response surface in ANSYS Workbench was modeled by using the central composite design experimental method. The optimal manufacturing structural parameters were obtained by using multiobjective genetic algorithm. Findings The sensor was optimized and the simulation results show that the overload resistance of the sensor is 200%F.S., and the axial stiffness, radial stiffness, bending stiffness and torsional stiffness are 14.981 kN/mm, 16.855 kN/mm, 2.0939 kN m/rad and 6.4432 kN m/rad, respectively, which meet the design requirements, and the sensitivities of each component of the optimized sensor have been well increased to be 2.969, 2.762, 4.010, 2.762, 2.653 and 2.760 times as those of the sensor with initial structural parameters. The sensor prototype with optimized parameters was produced. According to the calibration experiment of the sensor, the maximum Class I and II errors and measurement uncertainty of each force/torque component of the sensor are 1.835%F.S., 1.018%F.S. and 1.606%F.S., respectively. All of them are below the required 2%F.S. Originality/value Hence, the conclusion can be drawn that the sensor has excellent comprehensive performance and meets the expected practical engineering requirements.

New empirical equations to assess energy efficiency of flow-dissipating vortex dropshaft

Vortex structures are generally considered the common energy-dissipating infrastructures in order to convey urban surface waters. During transferring wastewater in such urban networks, the major proportion of water energy is dissipated and additionally, the hydraulic performance of these infrastructures is evaluated on the basis of the dissipation of energy efficiency in the vortex dropshaft. Over the last decade, the literature review of vortex structures demonstrated that there is no reliable mathematical expression to predict energy efficiency although various experimental investigations were done to evaluate the hydraulics of vortex structures. In this research, robust numerical models based on conceptions of Artificial Intelligence (AI) methodologies (i.e., classification, non-parametric analysis, and evolutionary computing) were developed by using 144 experimental data extracted from a physical model of vortex dropshaft. Through analysis of experiments, three main factors, called flow Froude number (Fr), the ratio of sump height (Hs) to shaft diameter (D), and the ratio of drop total height (L) to shaft diameter (D) were determined to estimate the efficiency of flow energy dissipation. The numerical models were calibrated/trained by different algorithms such as multi/single-objective optimization strategies (e.g., Particle Swarm Optimization [PSO] and Multi-Objective Genetic Algorithm [MOGA]). Various statistical tests, known as quantitative assessments for training and testing stages of AI models, demonstrated that Evolutionary Polynomial Regression (EPR) had the best performance (Coefficient of Correlation [R] = 0.9736, Root Mean Square Error [RMSE] = 0.5923, Mean Absolute Percentage Error [MAPE] = 0.5035) and followed by MT (R = 0.9665, RMSE = 0.7039, MAPE = 0.6225), GEP (R = 0.9113, RMSE = 1.0803, MAPE = 0.9535), and MARS (R = 0.8337, RMSE = 1.6473, MAPE = 1.1010). Additionally, expressions given by AI models provided more accurate predictions of energy dissipation efficiency when compared with a regression-based equation (R = 0.9113, RMSE = 0.7039, MAPE = 0.6225) from the literature. Additionally, variations of three effective factors versus the predicted responses (values of energy dissipation efficiency given by AI models) were in coincidence with experimental observations. Overall, this study proved that the usability of mathematical expressions could provide new insights for experts to assess the hydraulic evaluation of vortex structures before their physical and prototype models are constructed.

Marine shaft optimization using surrogate models and multi-objective optimization

This paper aims to obtain an application software using Ansys software in which a static, dynamic, and modal analysis for the power transmission of ships is presented. The objective functions of this research are maximum static stress, maximum dynamic stress, shaft mass, and modal shaft frequency analysis. A complete, comprehensive, and generalizable model for metal shaft types is presented. Then, using the kriging response surface and multi-objective genetic algorithm (NSGA II), these goals are optimized simultaneously. In this research, the optimization parameters are geometric parameters of the shaft design, including the position of the supports and the inner and outer diameters of the shaft. As a result of this optimization, the shaft's maximum static and dynamic stresses are reduced by 36% and 42%, respectively. The shaft's mass, which is one of the most critical factors during the shaft and its vibrations, is reduced by about 9%. In this regard, the first natural frequency of the optimized shaft increased by about 28.5% compared to the initial shaft.

Multi-Objective Optimization of a Multi-Cavity, Significant Wall Thickness Difference Extrusion Profile Mold Design for New Energy Vehicles

With the rapid development of the new energy vehicle market, the demand for extruded profiles for battery trays, mainly characterized by significant wall thickness differences in multiple chambers, is increasing, posing new challenges to production and quality control. This study examines the multi-objective optimization problem in the design process of aluminum profile dies with multi-cavity profiles and significant wall thickness differences. Using QFORM-extrusion professional aluminum extrusion finite element analysis software and the response surface analysis method, the standard deviation of the velocity (SDV), standard deviation of the pressure (SDP), and thick wall hydrostatic pressure (TWHP) on the profile section at the die exit are optimized. By analyzing the functional relationship between the key die structure parameters (the height of the baffle plates, the length of the bearing, and the height of the false mandrel) and the optimization objective, the optimal combination scheme of die structure parameters was obtained using the NSGA2 (non-dominated sorting genetic algorithm-2) multi-objective genetic optimization algorithm. The results show that, compared with the initial design scheme, the standard deviation of profile section velocity was reduced by 5.33%, the standard deviation of pressure was reduced by 11.16%, and the thick wall hydrostatic pressure was increased by 26.47%. The die designed and manufactured using this scheme successfully completed the hot extrusion production task, and the profile quality met the predetermined requirements, thus verifying the effectiveness of this study in optimizing the design of a multi-cavity aluminum profile die with significant differences in wall thickness for complex structures.

Experimental study and multi-objective optimisation of CNC turning parameters of AL6061 materials

ABSTRACT This paper investigates the effectiveness of feed rate, depth of cut, and cutting speed on material removal rate (MRR) and surface roughness (SR) of CNC lathe machines on AL6061 Materials. A combined use of Taguchi, Response Surface Methodology (RSM), and Genetic Algorithms (GA) is proposed to study and optimise turning processes. Firstly, the RSM with a fractional factorial design is implemented to indicate the significance and influence of process parameters on RSM and MRR. With the achieved data, regression models are formulated to present the relationship between control factors and the responses and are proven reliable in predicting through statistical analysis and validating experiment results. Further, a multi-objective genetic algorithm (GA) was used to obtain a Pareto solution set with multiple combinations of factors for optimal MRR and SR. Then, the best trade-off between process parameters is analysed to achieve desired quality outcomes. The results indicate that the proposed approach provides an effective solution for CNC Turning machines and can extend to similar problems in various fields.

Geometrical Optimization of Segmented Thermoelectric Generators (TEGs) Based on Neural Network and Multi-Objective Genetic Algorithm

In this study, a neural network and a multi-objective genetic algorithm were used to optimize the geometric parameters of segmented thermoelectric generators (TEGs) with trapezoidal legs, including the cold end width of thermoelectric (TE) legs (Wc), the ratios of cold-segmented length to the total lengths of the n- and p-legs (Sn,c and Sp,c), and the width ratios of the TE legs between the hot end and the cold end of the n- and p-legs (Kn and Kp). First, a neural network with high prediction accuracy was trained based on 5000 sets of parameters and the corresponding output power values of the TEGs obtained from finite element simulations. Then, based on the trained neural network, the multi-objective genetic algorithm was applied to optimize the geometric parameters of the segmented TEGs with the objectives of maximizing the output power (P) and minimizing the semiconductor volume (V). The optimal geometric parameters for different semiconductor volumes were obtained, and their variations were analyzed. The results indicated that the optimal Sn,c, Sp,c, Kn, and Kp remained almost unchanged when V increased from 52.8 to 216.2 mm3 for different semiconductor volumes. This work provides practical guidance for the design of segmented TEGs with trapezoidal legs.

Optimization of wear parameters for ECAP-processed ZK30 alloy using response surface and machine learning approaches: a comparative study

The present research applies different statistical analysis and machine learning (ML) approaches to predict and optimize the processing parameters on the wear behavior of ZK30 alloy processed through equal channel angular pressing (ECAP) technique. Firstly, The ECAPed ZK30 billets have been examined at as-annealed (AA), 1-pass, and 4-passes of route Bc (4Bc). Then, the wear output responses in terms of volume loss (VL) and coefficient of friction (COF) have been experimentally investigated by varying load pressure (P) and speed (V) using design of experiments (DOE). In the second step, statistical analysis of variance (ANOVA), 3D response surface plots, and ML have been employed to predict the output responses. Subsequently, genetic algorithm (GA), hybrid DOE–GA, and multi-objective genetic algorithm techniques have been used to optimize the input variables. The experimental results of ECAP process reveal a significant reduction in the average grain size by 92.7% as it processed through 4Bc compared to AA counterpart. Furthermore, 4Bc exhibited a significant improvement in the VL by 99.8% compared to AA counterpart. Both regression and ML prediction models establish a significant correlation between the projected and the actual data, indicating that the experimental and predicted values agreed exceptionally well. The minimal VL at different ECAP passes was obtained at the highest condition of the wear test. Also, the minimal COF for all ECAP passes was obtained at maximum wear load. However, the optimal speed in the wear process decreased with the number of billets passes for minimum COF. The validation of predicted ML models and VL regression under different wear conditions have an accuracy range of 70–99.7%, respectively.

Energy modeling, calibration, and validation of a small-scale greenhouse using TRNSYS

Greenhouse energy modeling is a prevalent tool for optimizing greenhouse energy consumption. However, for a model to serve its intended use, it is imperative to have a high level of confidence in the precision of its predictions. In this paper, a validated greenhouse energy model for a typical small-scale greenhouse in a cold climate is developed. The model is created using TRNSYS, a building performance simulation tool, with detailed energy modeling components and a user-defined crop model. The model is calibrated to fix uncertain parameters. A sensitivity analysis is used first to identify sensible uncertain parameters, followed by a multi-stage automated calibration. The automated calibration method uses a multi-objective genetic algorithm to adjust the uncertain parameters, calibrating the model for the measured indoor air temperature and relative humidity. The model performed well during the free-floating and ventilated stages (56 days) with a combined root mean square error (RMSE) of 1.6 °C for indoor air temperature and 8.3 % for the air relative humidity. The validation process involved assessing the applicability of the calibrated model using two additional datasets. For all the cases, comparing the simulation results with indoor environment measurements resulted in an RMSE of less than 2 °C for air temperature and less than 10 % for air relative humidity; these values compare favorably to the literature. The model achieved a 3.7 % mean relative error (MRE) in estimating monthly energy consumption for a minimally heated greenhouse. Given these results, the model is deemed sufficiently accurate and applicable for future investigations.

Multi-objective genetic algorithm embedded with reinforcement learning for petrochemical melt-flow-index production scheduling

In the petrochemical industry, product type conversion can cause capacity loss and produce transition products, leading to waste. Our study aims to minimize these transition products and tardiness in meeting customer due dates. We introduce the Engineering Experience Heuristic (EEH), which utilizes scheduling reference sheets to identify key production traits and constraints, thereby generating efficient schedules. Further building on the EEH, we propose a non-dominated sorting genetic algorithm II enhanced with reinforcement learning (NSGAeRL) to address the multi-objective scheduling optimization problem. The incorporation of reinforcement learning (RL), particularly through a model-based Markov Decision Process (MDP), allows for the dynamic adjustment of genetic algorithm (GA) parameters, including crossover and mutation mechanisms, based on the optimal policy derived from RL. We validate our proposed methodologies through empirical studies and numerical experiments. The results demonstrate that NSGAeRL outperforms standard benchmarking algorithms, offering superior performance in minimizing transition products and scheduling tardiness.

Performance comparison of linear and nonlinear compliant liquid dampers-inerter in controlling across-wind response of benchmark tall building

The potential of compliant liquid dampers-inerter (CLDI) for response control of tall buildings, capitalizing on the mass amplification property of the inerter, has been explored. However, the influence of the compliance connection on the damper displacement, energy dissipation, and inerter force is never addressed. The present work introduces a nonlinear compliant liquid damper-inerter (NCLDI), in which the compliance connection is achieved by joining the tank with the building through nonlinear spring and linear dashpot elements. The nonlinear mechanism (restoring force) is generated by connecting two linear springs in series. The effectiveness of NCLDI in reducing the across-wind response of tall buildings is examined and compared with that of a CLDI. For numerical demonstration, the 76-storey benchmark tall building is considered and subjected to across-wind loading. With the building's available mass, damping, and stiffness matrices, the uncontrolled and controlled structural responses are obtained using the Newmark–Beta method in the MATLAB environment. Iterations are executed at each time step to address the nonlinear stiffness term incorporating the Newton-Raphson method. Optimization is performed to estimate the optimum damper parameters utilizing MATLAB's multi-objective genetic algorithm solver, ‘gamultiobj’. The four selected objective functions are the peak and root-mean-square values of the roof displacement and acceleration. The performance of both the dampers is investigated for different inerter topologies with variable inertance ratios. The CLDI is found to be efficient when the inertance ratio and topology are less. However, an enhanced mitigation effect of the NCLDI compared to the CLDI is observed for higher inertance with a large topology. In addition, the stroke length of the damper is reduced significantly in case of NCLDI, which is a notable advantage for any passive damping device.

Research on Key Technologies for Optimization of Forming Parameters of Aluminum Alloy Hood Outer Panel

At present, computer technology and finite element software analysis technology have been widely used in the stamping and forming process of aluminum alloy hood outer plate to predict the possible quality defects in the stamping process, and effectively improve the quality and production efficiency of parts. The stamping and forming of the outer plate of the automobile aluminum alloy hood is a highly nonlinear process, and its forming quality involves many forming factors, if the variable design is not reasonable, it may lead to quality defects such as wrinkles or cracks of the molded parts. Due to the relatively complex shape and structure of the aluminum alloy hood outer plate and its high requirements for the quality of forming, it is difficult to obtain the optimal process parameters in a short time if the finite element software simulation optimization is carried out manually. The machine learning algorithm is applied to the research on the stamping and forming of the outer plate of the aluminum alloy hood, and the multi-objective genetic algorithm is used to optimize the stamping process parameters of the covers, which can meet the maximum thinning rate of the formed parts and control the rebound of the formed parts to a certain extent. How to combine machine learning algorithms with finite element simulations to reasonably adjust the process parameters in the stamping and forming process by using machine learning algorithms to meet the forming quality requirements of aluminum alloy hood outer plate has always been a research hotspot and difficulty in the field of aluminum alloy hood outer plate stamping. 

Structural Optimization and Process Scheme of Large-span Gantry Crossbeam based on Response Surface

A comprehensive design method for large-span gantry beams, combining process techniques and structural optimization, is proposed to address significant deflection deformation issues caused by their own gravity and the gravity of front attachments. Selecting the beam section type, finite element static analysis is performed using ANSYS Workbench for both cast and welded gantry beams. The maximum static deformation values of the beams for both process schemes are compared to determine the process scheme for the large-span beam. Subsequently, multiple structural dimensions of the welded gantry beam are parameterized, and sensitivity analysis is conducted to determine the degree of influence of each parameter on the optimization objective. Parameters with relatively small effects on the corresponding objectives are eliminated. A response surface multi-objective genetic algorithm optimization based on Box-Behnken design experimental design method is employed to minimize deflection deformation and reduce weight. The response surface model is fitted with expressions, and variance and significance analyses are conducted to validate the model's reliability. The optimal Pareto solutions are determined based on the response surface model using a multi-objective genetic algorithm. A comparison between the beams before and after optimization shows a reduction of 0.052 in maximum static deformation of the welded beam, representing an 8.3% decrease. Additionally, the mass decreases by 1710 kg, indicating a 14.63% reduction, demonstrating significant optimization effects.

Conceptual design and multi-objective optimization of a hybrid system based on direct ammonia protonic ceramic fuel cell and alkali metal thermal electric converter

As a power generation device, direct ammonia protonic ceramic fuel cells (NH3–PCFCs) also produce a significant amount waste heat, which not only results in energy wastage but also abnormal operation if the waste heat is not removed. To address these concerns and enhance generation electricity capacity, a novel waste heat recovery system based upon NH3-PCFC and alkali metal thermal electric converter (AMTEC) is first proposed, wherein the AMTEC converts the high-grade exhaust heat produced by the NH3-PCFC to extra electricity. Considering ammonia decomposition sluggish kinetics, various irreversible overpotential losses of NH3-PCFC as well as thermodynamic losses within the integrated system, the energetic/exergetic performance parameters evaluating the NH3-PCFC, AMTEC and the hybrid system are formulated. The general performance characteristics of the proposed system are revealed, along with a sensitive analysis conducted under specific operational conditions or structural parameters. Numerical calculation exhibited that the maximum attainable power density of the hybrid system has been promoted 20.5 % than that of stand-alone NH3-PCFC system. The multi-objective genetic algorithm is utilized to optimize the hybrid system performance for trade-off the power output and efficiency. Optimized results show the power density and corresponding efficiency of proposed system can respectively reach 6762.3 W m−2 and 49.8 % in the corresponding optimization conditions.

Prediction and optimization of the bearing capacity of strip footing resting on soft soil improved with stone columns using RSM, ANN, and multi-objective GA

Prediction and optimization of the bearing capacity of strip footing resting on soft soil improved with stone columns using RSM, ANN, and multi-objective GA

Proximate analysis on WEDM performances for titanium matrix composite using a novel desirable multi-objective genetic algorithm

Proximate analysis on WEDM performances for titanium matrix composite using a novel desirable multi-objective genetic algorithm

Real-time task parameter selection method of accounting system based on multi-objective optimization and genetic algorithm.

The progress of the digital economy has promoted the enterprise accounting system. To accelerate the update and evolution of accounting systems, we propose a parameter selection method based on multi-objective optimization and genetic algorithm. Firstly, this article proposes an accounting feature extraction method based on multimodal information embedding. The dual-branch structure and feature pyramid network are used to realize the feature extraction of the information involved in accounting. Then, this article proposes a multi-objective parameter selection method based on a parallel genetic algorithm. By embedding a genetic algorithm in the process of dual-branch model training, the model's ability to sense accounting information is improved. Finally, using the above two methods, an accounting system evaluation method upon recurrent Transformer is proposed to improve the financial situation of enterprises. Our experiments have proven that our approach attains a remarkable performance with an 87.6% F-value, 83.5% mAP value, and 83.4% accuracy. These results position our method at an advanced level globally, showcasing its capability to enhance accounting systems.

Compressive sensing-based construction of high-resolution mode shapes for updating bridge boundary constraints

AbstractIn this study, a method of finite element model updating is proposed to quantitatively identify bridge boundary constraints using the high-resolution mode shapes of a bridge. The high-resolution mode shapes are first identified from the responses measured by few randomly distributed sensors using the compressive sensing theory, which is innovatively implemented in the spatial domain with a proposed basis matrix. To speed up finite element updating, the frequency and modal assurance criterion Kriging models are then established to approximate the implicit relation between boundary constraints and bridge modal parameters including frequencies and mode shapes, serving as surrogate models for the bridge finite element model. By adopting the surrogate models in finite element updating, the objective functions of frequencies and mode shape indicators are optimized by a multi-objective genetic algorithm. The numerical examples as well as an actual laboratory experiment have shown that the mode shapes and boundary constraints of a bridge can be identified precisely and efficiently by the proposed method, even for a continuous and variable cross-sectional bridge.