Multi-objective Genetic Algorithm Optimization Research Articles

Enhanced reaction extraction distillation via multi-objective optimization and 4E analysis for sustainable recovery of organic compounds from wastewater

Resource utilization and sustainable treatment of wastewater, which can achieve efficient energy utilization and environmental protection, have received hot social attention. Tetrahydrofuran and methanol are the two most common substances in pharmaceutical wastewater. It is very necessary to explore the wastewater treatment process containing these two substances. In this study, extractive distillation and reactive distillation were proposed to effectively separate the tetrahydrofuran-methanol-water component of pharmaceutical wastewater. To further reduce energy consumption, two strengthening processes, double-column reactive-extractive distillation and reactive-extractive dividing wall column distillation were designed. Taking the minimum operating and equipment cost as the objective function, the processes were optimized by using the multi-objective genetic optimization algorithm to obtain the best process parameters. Four processes were evaluated in terms of total annual cost, energy consumption, gas emissions and thermodynamic efficiency. Compared to the traditional three-column extractive distillation process, the above-mentioned four aspects of the reactive-extractive dividing wall column process was reduced by 40.63%, 43.13%, 43.14% and 17.71%, respectively. The reactive-extractive dividing wall column distillation process is of great significance for the efficient purification of valuable organic matter in wastewater and environmental protection.

Application of multi-objective optimization genetic algorithm to design terahertz metamaterials with fano resonances

In the prosperous development of terahertz (THz) metamaterials, Fano resonances have gained attention due to their potential applications in ultrasensitive systems. The performance of Fano resonance is directly influenced by the geometrical parameters of the element structure. However, the traditional design rules for Fano resonances in metamaterials rely on an empirical trial-and-error strategy, necessitating significant effort to achieve optimal results. To address this issue, we propose a design method in this study that utilizes the finite integration technique in time domain (FITD) along with a multi-objective optimization genetic algorithm for the intelligent design of metamaterial structures exhibiting the Fano resonance phenomenon. The FITD method is primarily used to calculate the Fano resonance with different metamaterial geometric structure parameters, while the genetic algorithm efficiently selects the optimal solution. Our method, characterized by high efficiency and complete independence from prior knowledge, could offer a new design technique for metamaterials with specific functions, thereby contributing to the development of THz applications.

Optimization of Elbow Draft Tubes for Variable Speed Propeller Turbine

The design of the elbow draft tubes is challenging due to the complexity of the flow. The whole turbine unit’s power output strongly depends on the draft tube function, especially for the low-head turbines. The article presents a novel approach to optimizing elbow draft tubes for a variable-speed propeller turbine designed for low-head applications. First, the study addresses the specifics of the propeller variable speed turbine by comparing the classical Kaplan turbine. Then, the grid scaling test is conducted to evaluate the uncertainty of the pressure regeneration. Further, a new approach to parameterising the elbow draft tube geometry is introduced. The study employs ANSYS CFX 2021 R1 software for numerical simulation to optimise the elbow draft tube geometry in the CAESES environment. After the sensitivity test and deselecting the non-sensitive parameters, we perform multi-objective genetic algorithm (MOGA) optimization. The optimization process results in a Pareto front of optimised elbow draft tube shapes with the best pressure regeneration for different draft tube construction heights, enabling the selection of suitable candidates for various locations. Minimal difference in the performance of the selected elbow draft tube shapes with the simple straight draft tube confirms a high-quality draft tube optimization achievement.

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.

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.

Optimal design and modelling of sustainable bio-catalytic enzyme for wastewater treatment using response surface methodology and artificial neural network

ABSTRACT The accessibility of various wastewater treatment systems has become increasingly prominent in current times, coinciding with a rising concern regarding the substantial content of food waste that poses a significant challenge in waste management. In this context, ecoenzymes have emerged as a promising technique for water treatment due to their remarkable efficiency in pollutant removal and cost-effectiveness. Nevertheless, many experts encounter difficulties in determining the optimal combination of ecoenzyme variables to maximize pollutant removal. The objective of this study is to apply a comprehensive approach utilizing Response Surface Methodology (RSM), Artificial Neural Networks (ANN), and Multi-objective Genetic Algorithm (MOGA) methods for the modeling and optimization of ecoenzyme-based water treatment processes. The research outcomes demonstrate a noteworthy alignment between the actual removal rates of Total Suspended Solids (TSS), Volatile Suspended Solids (VSS), and Total Ammonia Nitrogen (TAN) with the values predicted by both RSM and ANN models, validating the robustness of the regression analysis (R2 >0.95). From the optimization results, it is elucidated that to attain the maximum removal of TSS, VSS, and TAN, the treatment process should be conducted over a period of 3.5 days, employing specific enzyme concentrations of 0.07 units/ml for protease, 0.027 units/ml for amylase, and 0.36 units/ml for lipase. This study employed machine learning approaches and MOGA optimization to predict the optimum ecoenzyme properties for maximum wastewater removal utilizing the concept of a low-cost, sustainable purification technology.

Identification of important gene signatures in schizophrenia through feature fusion and genetic algorithm.

Schizophrenia is a debilitating psychiatric disorder that can significantly affect a patient's quality of life and lead to permanent brain damage. Although medical research has identified certain genetic risk factors, the specific pathogenesis of the disorder remains unclear. Despite the prevalence of research employing magnetic resonance imaging, few studies have focused on the gene level and gene expression profile involving a large number of screened genes. However, the high dimensionality of genetic data presents a great challenge to accurately modeling the data. To tackle the current challenges, this study presents a novel feature selection strategy that utilizes heuristic feature fusion and a multi-objective optimization genetic algorithm. The goal is to improve classification performance and identify the key gene subset for schizophrenia diagnostics. Traditional gene screening techniques are inadequate for accurately determining the precise number of key genes associated with schizophrenia. Our innovative approach integrates a filter-based feature selection method to reduce data dimensionality and a multi-objective optimization genetic algorithm for improved classification tasks. By combining the filtering and wrapper methods, our strategy leverages their respective strengths in a deliberate manner, leading to superior classification accuracy and a more efficient selection of relevant genes. This approach has demonstrated significant improvements in classification results across 11 out of 14 relevant datasets. The performance on the remaining three datasets is comparable to the existing methods. Furthermore, visual and enrichment analyses have confirmed the practicality of our proposed method as a promising tool for the early detection of schizophrenia.

Artificial neural network prediction and multi-objective genetic algorithm optimization of the microchannel heat sink with trapezoidal ribs

Trapezoidal rib microchannel heat sinks (TR-MCHS) are widely used for efficient heat dissipation in electronic integrated devices. To enhance the heat transfer and hydraulic performance of TR-MCHS, an optimization method using neural network prediction combined with multi-objective genetic algorithm NSGA-II is proposed. First, the effects of trapezoidal rib geometry parameters and inlet velocity (V) on the Nusselt number (Nu¯) and pressure drop (ΔP) of TR-MCHS are investigated using numerical simulation and Sobol sensitivity analysis. The results show that the trapezoidal rib height (ht) and inlet V have the most significant effect on the TR-MCHS, and the interaction effect between the parameters can be neglected. Then, optimization is performed with Nu¯ and ΔP as objectives. On the obtained Pareto solution set, Nu¯ can be improved by 7.3 % compared to the unoptimized TR-MCHS without consuming additional pumping power. In addition, if the same Nu¯ is obtained as the unoptimized TR-MCHS, the ΔP can be reduced to 223 Pa. On this basis, the TOPSIS method based on entropy weight is developed to solve the optimal compromise solution. The best compromise solution has the Nu¯ of 50.9 and the ΔP of only 184.6 Pa, which is 87.9 Pa lower than that of the unoptimized TR-MCHS. These results have important implications for the design of TR-MCHS for electronics cooling.

Optimizing Placement and Scheduling for VNF by a Multi-objective Optimization Genetic Algorithm

Virtual network functions (VNFs) have gradually replaced the implementation of traditional network functions. Through efficient placement, the VNF placement technology strives to operate VNFs consistently to the greatest extent possible within restricted resources. Thus, VNF mapping and scheduling tasks can be framed as an optimization problem. Existing research efforts focus only on optimizing the VNFs scheduling or mapping. Besides, the existing methods focus only on one or two objectives. In this work, we proposed addressing the problem of VNFs scheduling and mapping. This work proposed framing the problem of VNFs scheduling and mapping as a multi-objective optimization problem on three objectives, namely (1) minimizing line latency of network link, (2) reducing the processing capacity of each virtual machine, and (3) reducing the processing latency of virtual machines. Then, the proposed VNF-NSGA-III algorithm, an adapted variation of the NSGA-III algorithm, was used to solve this multi-objective problem. Our proposed algorithm has been thoroughly evaluated through a series of experiments on homogeneous and heterogeneous data center environments. The proposed method was compared to several heuristic and recent meta-heuristic methods. The results reveal that the VNF-NSGA-III outperformed the comparison methods.

Research on Vehicle Frame Optimization Methods Based on the Combination of Size Optimization and Topology Optimization

The efficient development of electric vehicles is essential to drive society towards sustainable development. Designing a lightweight frame is a key strategy to improve the economy and environment, increase energy efficiency, and reduce carbon emissions. Taking an automatic loading and unloading mixer truck as the research object, a force analysis of its frame was conducted under six typical working conditions. A size optimization method based on a hybrid model of the Kriging model and the analytic hierarchy process (AHP) is proposed. An approximate model of the mass and maximum stress of the frame was established using the Kriging model, and the Kriging model was optimized by using the multi-objective genetic optimization algorithm and the AHP method. Meanwhile, topology optimization was introduced to improve the structural performance of the frame and reduce its weight. The optimization results show that the overall weight of the frame is reduced by 11.96% compared to the pre-optimization period, though it still meets the material performance specifications. By comparing the iterative curves of the single Kriging model with those of the AHP model, it can be seen that the initial optimization efficiency of the hybrid model is about twice as much as that of the AHP model, and the final optimization result is improved by about 3.6% compared with the Kriging model. This validates the hybrid model as an effective tool for the multi-objective optimization of electric vehicle frames, providing more efficient and accurate optimization results for frame design.

Hybrid modeling and optimization of fiber laser hole cutting of austenitic stainless-steel sheets using response surface

In this study, an efficient approach was proposed to systematically model and optimize the laser small hole cutting process parameters using a hybrid approach for the design of experiment and multi-objective genetic algorithm optimization. The central composite design and response surface methodology were used to effectively model the impact of four main factors: cutting speed, laser power, gas pressure and focal distance on the responses. The responses considered were hole diameter circularity tolerance, spattering and cut kerf width, which were used to evaluate the quality of the laser hole cutting. The regression equations were used to model the effect of process parameters and their interactions on the responses. These regression models were then used as objective functions for optimization. The results show that the focal distance and laser power have had a significant influence on the hole diameter circularity tolerance and the variation in size of the cut kerf. In particular, the melted material spattering rate increased threefold when the focal distance increased from 0.4 to 0.8 mm. The optimization results highlighted that the best outcomes in terms of minimum deviation, spatter, and the cut-kerf width were achieved at low power (between 605 and 685 W) and low speeds (in the range of 11.1–12.7 m min−1). The optimal focal distance for all solutions was found to be 0 mm for the gas pressure (between 6.5 and 8 bars) to minimize the objective functions.

A competitive new multi-objective optimization genetic algorithm based on apparent front ranking

Evolutionary algorithms built on Pareto-dominance suffer from a loss of selection pressure as the number of objectives increases and the probability of finding non-dominated solutions in the population decreases. Furthermore, Pareto-dominance is computationally expensive because of the pairwise comparisons necessary to rank the individuals of a population. The paper introduces a new genetic algorithm for multi-objective optimization based on Apparent Front Ranking and crowding distance, called Controlled Apparent Front Zones Genetic Algorithm (CAFZGA). To avoid pairwise comparisons, CAFZGA first generates the main Apparent Front Boundary (AFB) with the help of a small set of support vectors and creates secondary AFBs as shifted versions of the main AFB. The space of objectives is divided into zones by the AFBs. The zones play a similar role in CAFZGA as the non-dominated fronts do in the Controlled Non-dominated Sorting Genetic Algorithm CNSGA-II. The zone ranking becomes the main criterion in differentiating individuals, with crowding distance being the tiebreak criterion. The method is shown to be extremely flexible because the AFBs are adjusted for each generation. Computationally, CAFZGA is more efficient than GAs using Pareto-dominance because the set of support vectors for generating the AFBs is significantly smaller than the population. CAFZGA is applied to the problem of optimizing the configuration of a Grid ALU Processor (GAP) and is shown to be competitive and sometimes even better than well-established GAs like CNSGA-II and Fuzzy-Dominance-Driven Genetic Algorithm FDD-GA.

Machine learning for multi-dimensional performance optimization and predictive modelling of nanopowder-mixed electric discharge machining (EDM)

AbstractAluminium 6061 (Al6061) is a widely used material for various industrial applications due to low density and high strength. Nevertheless, the conventional machining operations are not the best choice for the machining purposes. Therefore, amongst all the non-conventional machining operations, electric discharge machining (EDM) is opted to carry out the research due to its wide ability to cut the materials. But the high electrode wear rate (EWR) and high dimensional inaccuracy or overcut (OC) of EDM limit its usage. Consequently, nanopowder is added to the dielectric medium to address the abovementioned issues. Nanopowder mixed EDM (NPMEDM) process is a complex process in terms of performance predictability for different materials. Similarly, the interactions between the process parameters such as peak current (Ip), spark voltage (Sv), pulse on time (Pon) and powder concentration (Cp) in dielectric enhance the parametric sensitivity. In addition, the cryogenic treatment (CT) of electrodes makes the process complex limiting conventional simulation approaches for modelling inter-relationships. An alternative approach requires experimental exploration and systematic investigation to model EWR and overcutting problems of EDM. Thus, artificial neural networks (ANNs) are used for predictive modelling of the process which are integrated with multi-objective genetic algorithm (MOGA) for parametric optimization. The approach uses experimental data based on response surface methodology (RSM) design of experiments. Moreover, the process physics is thoroughly discussed with parametric effect analysis supported with evidence of microscopic images, scanning electron microscopy (SEM) and 3D surface topographic images. Based on multi-dimensional optimization results, the NT brass electrode showed an improvement of 65.02% in EWR and 59.73% in OC using deionized water. However, CT brass electrode showed 78.41% reduction in EWR and 67.79% improved dimensional accuracy in deionized water. In addition to that, CT brass electrode gave 27.69% less EWR and 81.40% improved OC in deionized water compared to kerosene oil.

Theoretical and Experimental Study on Parameter Optimization of Active Sources in Seismic Tomography

Abstract Given the high cost of active seismic tomography and the large influence on mining production, as well as the uncontrollable inversion period and accuracy of the target area during the passive seismic tomography, an optimization algorithm based on adding a certain number of artificial microseism (active sources) was proposed through the distribution characteristic of microseismic events (passive sources). The active source parameter optimization method realizes the combination of active source and passive source and improves the accuracy of inversion at the same time. The method uses a multiobjective optimization genetic algorithm to fuse the accuracy index and stability index that preevaluates the inversion effect and solves the best combination of active sources to improve the inversion effect. At the same time, experimental studies were carried out. After the active source was added, the inversion accuracy of high-stress areas and the ability to resist data errors were greatly improved. The inversion results were more in line with the law of stress transfer and stress distribution, which enhanced the connectivity of the low-velocity area of the fault zone or weakened zone.

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.

Optimization study for RF cavity and beam dynamics simulation in the electron linear accelerator using the multi-objective genetic algorithm

This work presents an optimization study on the RF cavity geometry design and beam dynamics of an electron injector linac for the Korea Fourth-Generation Storage Ring (Korea-4GSR). It is challenging to achieve an optimal design for a linac system due to the nonlinear nature of accelerator system and the associated beam dynamics with numerous knobs to control. To address this challenge, we employed the Multi-Objective Genetic Algorithm (MOGA) for efficient optimization. Through the utilization of MOGA, we effectively optimized the electron linac for the Korea-4GSR. The optimized design yielded a horizontal normalized rms emittance of approximately 3.79 mm-mrad and an energy spread of about 307.37 keV, which met design requirements of the Korea-4GSR. This approach facilitated the design of an electron linac tailored for the Korea-4GSR with enhanced efficiency and effectiveness.

Analysis of Taxiing Performance of Single Strut Landing Gear Based on ISD

In this paper, based on the “spring-damper” landing gear (SDLG), the inerter is introduced to form the “inerter-spring-damper” landing gear (ISDLG), and the taxiing dynamic models of both are established. Taking fuselage acceleration, buffer stroke and dynamic tire load as performance indexes, the parameters of ISDLG are optimized by using multi-objective genetic optimization algorithm. In MATLAB/Simulink environment, SDLG and ISDLG are simulated in time-domain and frequency-domain. The results show that compared with SDLG, the values of performance indexes of ISDLG are all reduced, especially at low frequency, which can effectively suppress the low frequency vibration of the fuselage and improve the ride comfort and handling stability of the aircraft when it is taxiing on the ground. In addition, through comparison, it is found that speed and road grade have different influences on aircraft taxiing performance, and road grade has the greatest influence, followed by speed.

Machine Learning Assisted Microchannel Geometric Optimization—A Case Study of Channel Designs

At present, microchannel heat exchangers are widely applied in the fields of air-conditioning and heat pumping applications given their high heat transfer performance, compact size, and low material cost. However, designing and optimizing the channel geometries remain challenging, as they require balancing multiple competing objectives to achieve the optimal performance. This study investigates various parameters, including the channel count, wetted perimeter, cross-sectional area, and mass flow rate for each channel, to achieve the optimal efficiency. The optimization objectives include maximizing the heat transfer rate, minimizing the refrigerant convective thermal resistance, maximizing the refrigerant heat transfer coefficient, and minimizing the pressure drop. A multi-objective genetic optimization algorithm, in conjunction with artificial neural network (ANN)-based machine learning models, was used to predict the heat transfer rate to speed up the calculation process during the optimization. We identified that a gradient reduction in the wetted perimeter from the air inlet along the airflow direction could enhance the heat transfer rate. Additionally, the results indicate that an increase in the number of channels leads to an enhanced heat transfer efficiency rate. However, with the increase in the number of channels, the cross-sectional area of each channel is correspondingly reduced to maintain a consistent overall cross-sectional area. This reduction increases the fluid resistance, leading to an increased pressure drop across the system. This observation is critical for a microchannel design optimization, highlighting the importance of attaining a balance between achieving a higher heat transfer efficiency and maintaining a favorable fluid dynamic performance.

Sustainable and efficient process design for wastewater recovery of cyclohexane/isopropyl alcohol azeotrope by extractive distillation based on multi-objective genetic algorithm optimization

Cyclohexane and isopropyl alcohol are widely used in the chemical and pharmaceutical industries. There is a strong industrial need for the refinement of cyclohexane and isopropyl alcohol. In this study, the recovery of cyclohexane and isopropyl alcohol from wastewater was carried out sustainably and efficiently by extractive distillation. The suitability of ethylene glycol as entrainer was determined by relative volatility analysis. The process was optimized via multi-objective optimization, and the extractive distillation process obtained optimal parameters and process scheme. Finally, a comprehensive analysis was conducted to evaluate the economic, environmental and exergy efficiency of different process schemes. The results show that the extractive distillation coupled pervaporation technology has a 14.07% lower total annual cost than extractive distillation, and the pervaporation technology reduces gas emissions by 15.65% compared to the extractive distillation. This work explores the recovery of cyclohexane and isopropyl alcohol from wastewater by different process schemes, which has certain guiding significance for the sustainable and efficient separation of ternary azeotrope.

Optimisation of direct contact membrane distillation to enhance brackish water desalination and study polyvinylidene fluoride (PVDF) membrane performance

This study explores the optimisation of operational parameters in direct contact membrane distillation (DCMD) for brackish water desalination using polyvinylidene fluoride (PVDF) membranes. Central composite design (CCD) identified optimal conditions, and quadratic regression models demonstrated a strong correlation (R2 values: 0.98 for salt rejection, 0.99 for permeate flux) between operational factors and membrane performance. Artificial neural network (ANN) models surpassed Response Surface Methodology (RSM) in predictability for permeate flux and salt rejection. Multi-objective Genetic Algorithm (MOGA) optimisation yielded a pareto optimal solution, revealing two sets of conditions: one maximising permeate flux (21.40 L/m2 h) with 44.75 LPH feed flowrate and the other achieving 100 % salt rejection with 52.48 LPH feed flowrate. Optimising DCMD for flexible water desalination, this study positions it as a promising, sustainable technique, providing practical large-scale implementation guidance. Addressing limitations is key to widespread adoption as a cost-effective freshwater production method.