Multi-objective Optimization Process Research Articles

Multiobjective optimization of carbon fiber–reinforced epoxy composite properties using nonparametric modeling

Carbon fiber–reinforced epoxy composites are widely used in the marine and aviation sectors due to their superior mechanical properties. As research into these materials progresses, the demand for materials with even more complex and superior properties has increased. Traditional trial-and-error methods are limited to optimizing single factors and are plagued by heavy workloads and prohibitive costs when applied to multifactor composite designs. Addressing these problems, this study introduces a nonparametric multiobjective optimization process for composite materials. This approach employs a Gaussian process (GP) to establish a multiobjective regression model and uses nondominated sorting genetic algorithm II (NSGA II) to determine the Pareto front. Pareto solutions were ranked in accordance with their similarity to ideal solutions and on the basis of predetermined preference weights. The results demonstrate variations in optimal outcomes contingent on the weight values assigned. The differences between the optimized results and experimental validations reached 12.9%, with the minimum deviation being 0.6%. The effectiveness of the proposed method was demonstrated. The strategy combines GP and NSGA II to extend design methods for small samples and materials with multiple attributes. This method enables the simultaneous optimization of multiple conflicting objectives, offering greater efficiency than addressing them individually.

A Probability-based Fuzzy Multi-objective Optimization for Material Selection

Abstract: In the present paper, a rational fuzzy multi-objective optimization for material selection is developed in respect of probabilistic method for multi-objective optimization, which agrees with the viewpoint of system theory for the whole optimization of a system and is the novelty of this work. The basic ideas and algorithms of fuzzy theory together with probability theory are taken as the cornerstone to perform the formulation. In the treatment, the intersection of the membership function of fuzzy numbers of alternative material performance and the membership function of fuzzy numbers of desired material performance is used as the utility of the material performance index. Thereafter, the utility of each material performance index is further used to conduct the assessment of its partial preferable probability and formulate the multi-objective optimization by means of probability theory. Moreover, a typical example is presented to provide the rational process of the probabilistic fuzzy multi-objective optimization for material selection.

Research on Multi-Objective Process Parameter Optimization Method in Hard Turning Based on an Improved NSGA-II Algorithm

To address the issue of local optima encountered during the multi-objective optimization process with the Non-dominated Sorting Genetic Algorithm II (NSGA-II) algorithm, this paper introduces an enhanced version of the NSGA-II. This improved NSGA-II incorporates polynomial and simulated binary crossover operators into the genetic algorithm’s crossover phase to refine its performance. For evaluation purposes, the classic ZDT benchmark functions are employed. The findings reveal that the enhanced NSGA-II algorithm achieves higher convergence accuracy and surpasses the performance of the original NSGA-II algorithm. When applied to the machining of the high-hardness material 20MnCrTi, four algorithms were utilized: the improved NSGA-II, the conventional NSGA-II, NSGA-III, and MOEA/D. The experimental outcomes show that the improved NSGA-II algorithm delivers a more optimal combination of process parameters, effectively enhancing the workpiece’s surface roughness and material removal rate. This leads to a significant improvement in the machining quality of the workpiece surface, demonstrating the superiority of the improved algorithm in optimizing machining processes.

Multi-objective optimization of a hydrogen production process for polymer membrane coupled with electrochemical hydrogen pump from hydrogen-doped natural gas by NSGA-II

In this work, a novel process of polymer membrane coupled with electrochemical hydrogen pump (EHP) was proposed to achieve efficient hydrogen production from hydrogen-doped natural gas (HDNG), and was simulated by UniSim Design. A hydrogen separation unit of two-stage polymer membrane was used for achieving hydrogen enrichment from low H2 concentration HDNG. Due to the hydrogen upgrade, EHP was operated at a H2 concentration much higher than HDNG, resulting in low energy consumption. Benefiting from the synergy between hydrogen upgrade and purification, the coupling process brought substantial effect while improving the hydrogen purity and recovery rate and reducing the hydrogen separation cost. The real natural gas components were considered into HDNG to ensure the effectiveness of process simulation. An optimization strategy considering both product yield and production cost was proposed to achieve the trade-off between hydrogen recovery rate and separation cost. However, the impact of process parameters on the objectives is nonlinear and difficult to describe with mathematical expressions. Therefore, an agent model of the proposed process was established based on back propagation neural network (BPNN), and multi-objective optimization of H2 recovery rate and total annual cost (TAC) of hydrogen separation unit was achieved using non-dominated sorting genetic algorithm-II (NSGA-II). Investigations with HDNG under various operating conditions (feed pressure of 8–40 bar and feed H2 concentration of 5–25 mol%) were performed, which proved that 90 % overall H2 recovery rate and 99.99 mol% H2 purity of hydrogen product can be achieved at low cost, with the lowest hydrogen separation cost of 1.00 $/kg H2 achievable at 40 bar feed pressure and 25 mol% feed H2 concentration. In summary, this coupling process can provide engineering solutions for efficient hydrogen production from HDNG and promote the development of green hydrogen.

Multi-objective optimization of wire electrical discharge machining process using multi-attribute decision making techniques and regression analysis

Wire electrical discharge machining (WEDM) is one of the most important non-traditional machining methods that is widely used in various industries. The present research work is concerned with the influences of process variables on quality of machined specimen obtained from WEDM process. The process parameters to manufacture mold structure included wire feed speed, wire tension and generator power, and in the current research, the effects of these variables on the aim factors, namely dimensional accuracy, hardness and roughness of product surface have been investigated, simultaneously. In order to obtain the optimal experiment, the multi-objective optimization with discrete solution area has been employed. Method based on the removal effects of criteria (MEREC) and weighted aggregates sum product assessment (WASPAS) techniques have been used with the aim of weighting the objective functions and discovering the best practical experiment. In the following, the regression analysis has been employed to study the effects of variables on response factors. A good correlation between the results gained from two analysis methods was observed. Based on MEREC-WASPAS hybrid technique, the weights of roughness, hardness and dimensional accuracy of machined part were calculated to about 89%, 9% and 2%, respectively. In the selected optimal experiment, the amount of wire feed speed, wire tension and generator power variables were considered to, in turn, 2 cm/s, 2.5 kg, and 10%.

Does architectural design require single-objective or multi-objective optimisation? A critical choice with a comparative study between model-based algorithms and genetic algorithms

Efficiency and accuracy have been challenging in the design optimisation process driven by building simulation. The literature review identified the limitations of previous studies, prompting this study to explore the performance of single-objective versus multi-objective efficiency and accuracy on equivalent problems based on control variables and to consider more algorithmic options for a broader range of designs. This study constructed a comparative energy-related experiment whose results are in the same unit, either as a single-objective optimisation or split into two objectives. The project aims to reduce annual energy consumption and increase solar utilisation potential. Our approach focuses on the use of a surrogate modelling algorithm, Radial Basis Function Optimisation Algorithm (RBFOpt), with its multi-objective version RBFMOpt, to optimise the energy performance while quickly identifying new energy requirements for an iterative office building design logic, contrast to traditional genetic-algorithm-driven. In addition, the research also conducted a comparative study between RBFOpt and Covariance Matrix Adaptation Evolutionary Strategies (CMAES) in a single-objective comparison and between RBFMOpt and Nondominated Sorting Genetic Algorithm II (NSGA-II) in a multi-objective optimisation process. The comparison of these sets of Opt algorithms with evolutionary algorithms helps to provide data-driven evidence to support early design decisions.

Multi-objective optimization of hot embossing process for high-quality glass micro gratings

This study proposed a multi-objective optimization strategy to produce high-quality glass micro gratings. The applied methods include factor screening, surface response methodology (RSM), non-dominated sorting genetic algorithm (NSGA-II) and decision-making. Specifically, the significant factors were selected among several process parameters in hot embossing through screening experiments. Afterward, the regression models for the transferred ratio, uniformity of glass microstructures and warpage concerning those significant process parameters were established. Meanwhile, the significance of selected factors and their interactions was analyzed. Based on the regression models, the Parato front was obtained by using NSGA-II program, where the top 10 solutions were applied in glass hot embossing. Finally, a decision-making strategy was employed to further choose a feasible solution that allows the fabrication of glass micro gratings with a high transfer ratio, high uniformity, low warpage, low surface roughness, and no defect. The experimental results indicated that embossing temperature has the most significant effect on the replication accuracy of glass micro gratings, followed by embossing force and embossing time. However, warpage was not significantly sensitive to any process parameters in this hot embossing process. The defect-free glass micro gratings could be eventually fabricated under the optimal process parameters, achieving a transfer ratio and uniformity of higher than 95% and 83%, respectively. Moreover, their Ra surface roughness and warpage can be lower than 13nm and 1.5 μm. The satisfactory quality of embossed micro gratings revealed that the proposed multi-objective optimization strategy is feasible in glass hot embossing.

Optimization design of an innovative francis draft tube: Insight into improving operational flexibility

Hydropower has transitioned from base-load generation into an energy regulator in the hydro-wind-solar integrated systems, resulting in prolonged turbine operation under off-design conditions. Inspired by the new requirements of operation mode, an innovative Francis draft tube with inclined conical diffuser is presented to mitigate the pressure fluctuations and fatigue damage induced by flow instabilities. The design is optimized and comprehensively investigated to improve the operational flexibility. Computational fluid dynamics combined with design of experiments, artificial neural network and multi-objective optimization process lead to two optimal designs denoted as Opt-1 and Opt-2 from the Pareto fronts. Corresponding maximum pressure fluctuation amplitude is reduced by 15 % and 20 % compared to the traditional, respectively. An 81 % decrease of the fluctuating pressure recovery for the modified draft tube is obtained, leading to a steadier runner torque and consequently lower power swing. The asymmetric design of inclined daft tube is mainly targeted at the mitigation of the synchronous component as well as the attenuation of interaction between the swirling flow and elbow, accompanied with the mitigated energy of first-order vortex structure. The proposed innovative draft tube and optimization technique are found useful to broaden the operating range of hydraulic turbine in a computationally affordable way.

Multi-objective optimization and uncertainty quantification for inductors based on neural network

Purpose The prolonged training time of the neural network (NN) has sparked considerable debate regarding their application in the field of optimization. The purpose of this paper is to explore the beneficial assistance of NN-based alternative models in inductance design, with a particular focus on multi-objective optimization and uncertainty analysis processes. Design/methodology/approach Under Gaussian-distributed manufacturing errors, this study predicts error intervals for Pareto points and select robust solutions with minimal error margins. Furthermore, this study establishes correlations between manufacturing errors and inductance value discrepancies, offering a practical means of determining permissible manufacturing errors tailored to varying accuracy requirements. Findings The NN-assisted methods are demonstrated to offer a substantial time advantage in multi-objective optimization compared to conventional approaches, particularly in scenarios where the trained NN is repeatedly used. Also, NN models allow for extensive data-driven uncertainty quantification, which is challenging for traditional methods. Originality/value Three objectives including saturation current are considered in the multi-optimization, and the time advantages of the NN are thoroughly discussed by comparing scenarios involving single optimization, multiple optimizations, bi-objective optimization and tri-objective optimization. This study proposes direct error interval prediction on the Pareto front, using extensive data to predict the response of the Pareto front to random errors following a Gaussian distribution. This approach circumvents the compromises inherent in constrained robust optimization for inductance design and allows for a direct assessment of robustness that can be applied to account for manufacturing errors with complex distributions.

Multi-objective optimization of electro-pneumatic braking process with fuzzy logic control for heavy haul railway applications

ABSTRACT This paper presents a novel approach to multi-objective optimization in heavy haul trains’ electro-pneumatic braking systems using optimizable fuzzy control. Initial findings indicate that a straightforward replacement of pneumatically controlled valves with electro-pneumatic ones, without significant control schedule modifications, fails to substantially improve the dynamics behaviour of rolling-stock. Consequently, a more sophisticated strategy based on optimizable fuzzy logic control for the electro-pneumatic valves was developed. This approach utilizes multi-objective optimization to simultaneously minimize energy dissipation during braking and reduce force peaks in freight car draft gears. The optimal solution achieved demonstrates a 13.41% reduction in kinetic energy dissipation during downhill sections, along with a concurrent decrease in draft gear peak forces by 14.22% in traction and 3.16% in buff regimes compared to standard rolling-stock with pneumatically controlled valves. This optimized fuzzy braking controller outperforms standard procedures, highlighting its potential to enhance heavy haul train efficiency while adhering to safety standards.

Modeling and multi-objective optimization of forward osmosis process

In order to ensure efficient wastewater treatment and seawater desalination, adequate modeling and optimization of the forward osmosis (FO) process has the potential to be very helpful. This paper deals with the FO model parameters calibration and FO process optimization by a gradient-based optimization method. For this purpose, an upgraded FO model, which involves temperature- and agent-dependent parameters, was developed. The FO model calibration was done using NaCl as agents in draw solution, while MgCl2 was used for model validation. The agreements between simulated and measured FO performance were satisfactory; relative index of agreement are higher than 0.99. By using the proposed FO model, the optimization of FO process conditions was performed with various definitions of the objective and constraint functions. In case of maximizing the water flux, minimizing reverse solute flux, and fulfilling the required constraints, the ratio of water flux and reverse solute flux increased up to 40 % for NaCl and up to 20 % for MgCl2; meanwhile the effective osmotic pressure difference was improved 2-times for NaCl and up to 3.8-times for MgCl2. The optimization process proved to be stable and efficient and can easily be adapted or upgraded for more complex dynamic FO modeling.

Process optimization, multi-gene genetic programming modeling and reliability assessment of bioactive extracts recovery from Phyllantus emblica

This study investigates the feasibility of extracting bioactive antioxidants from Phyllantus emblica leaves using a combination of ethanol-water mixture (0-100%) and heat-assisted extraction technology (HAE-T). Operating temperature (30-50°C), solid-to-liquid ratio (1:20-1:60 g/mL), and extraction time (45-180 min) were varied to determine their effects on extract total phenolic content (TPC), yield (EY), and antioxidant activity (AA). The Box-Behnken experimental design (BBD) within response surface methodology (RSM) was employed, with multi-objective process optimization using the desirability function algorithm to find the optimal process variables for maximizing TPC, EY, and AA simultaneously. The extraction process was modeled using BBD-RSM and multi-gene genetic programming (MGGP) algorithm, with model reliability assessed via Monte Carlo simulation. HPLC characterization identified betulinic acid, gallic acid, chlorogenic acid, caffeic acid, ellagic acid, and ferulic acid as bioactive constituents in the extract. The study found that a 50% ethanol solution yielded the best extraction efficiency. The optimal process parameters for maximum EY (21.6565%), TPC (67.116 mg GAE/g), and AA (3.68583 µM AAE/g) were determined as OT of 41.61°C, S:L of 1:60 g/mL, and ET of 180 min. Both BBD-RSM and MGGP-based models satisfactorily predicted the observed process responses, with BBD-RSM models showing slightly better performance. Reliability analysis indicated high certainty in the predictions, with BBD-RSM models achieving 99.985% certainty for TPC, 97.569% for EY, and 98.661% for AA values.

Multi-Objective Optimization of Low-Alloy Hot-Rolled Strip Cooling Process Based on Gray Correlation Analysis

The residual stress in low-alloy hot-rolled strips seriously affects the use and processing of products. Reducing residual stress is important for improving the product quality of hot-rolled strips. In this paper, the changes in grain size and residual stress of hot-rolled strips under different cooling processes were investigated via thermal simulation experiments and electron backscatter diffraction. It was found that the optimum cooling process solution for single-objective optimization of grain size was a final rolling temperature of 875 °C, a laminar cooling speed of 50 °C/s, and a coiling temperature of 550 °C. When single-objective optimization of residual stress was carried out, the optimal cooling process scheme was 900 °C for final rolling temperature, 20 °C/s for laminar cooling speed, and 625 °C for coiling temperature. The significance of the effect of cooling processes on grain size and residual stress was analyzed based on the extreme deviation of the effect of each cooling process on grain size and residual stress in orthogonal experiments. The results show that the coiling temperature was the most influential factor on grain size and residual stress among the cooling process parameters. The difference was that grain size increased with increasing coiling temperature, and residual stress decreased with increasing coiling temperature. Using both grain size and residual stress as evaluation indicators, a multi-objective optimization of the cooling process for hot-rolled strips was carried out via the gray correlation analysis method. The optimized solution was 875 °C final rolling temperature, 30 °C/s laminar cooling speed, and 625 °C coiling temperature. At this time, the grain size was 4.8 μm, and the KAM (Kernel Average Misorientation) was 0.40°. The grain size under the actual production process scheme was 4.4 μm with a KAM of 0.78°. Compared to the actual production process solution, the multi-objective optimization solution showed little change in grain size, with only a 9% increase and a 49% reduction in KAM. The optimization scheme in this paper could significantly reduce the level of residual stresses while ensuring the fine grain size of hot-rolled strips, thus improving the overall quality of hot-rolled strips.

Simulation Analysis and Multiobjective Optimization of Pulverization Process of Seed-Used Watermelon Peel Pulverizer Based on EDEM

To enhance the utilization of seed-used watermelon peel and mitigate environmental pollution, a hammer-blade seed-used watermelon peel crusher was designed and manufactured, and its structure and working parameters were optimized. Initially, the seed-used watermelon peel crusher and seed-used watermelon peel model were constructed, and the model’s parameters were calibrated. Subsequently, the discrete element method (EDEM2022) was employed to investigate the effects of spindle speed (MSS), the number of hammers (NCB), and feeding volume (FQ) on the pulverizing process. Multivariate nonlinear regression prediction models were developed for the percentage of pulverized particle size less than 8 mm (Psv), pulverizing efficiency (Ge), and power density (Ppd), followed by the analysis of influencing factors and prediction models using ANOVA. The multiobjective optimization of the prediction model utilizing the improved hybrid metacellular genetic algorithm CellDE resulted in solutions of 90.02%, 89.57%, and 8.35 × 10−3 t/(h-kw) for Psv-opt, Ge-opt, and Ppd-opt, respectively. The corresponding optimal interaction values of MSS, NCB, and FQ were determined to be 1500 r/min, 108, and 150 kg/min. Finally, a prototype test was conducted by combining the optimal factor interaction values, yielding statistically calculated values of 96.63%, 92.37%, and 7.76 × 10−3 t/(h-kw) for Psv-pr, Ge-pr, and Ppd-pr, respectively. The results indicate that the optimized values of Psv-opt, Ge-opt, and Ppd-opt models have an error of less than 8% compared to the statistically calculated values of the prototype test and outperform the values of Psv-ori, Ge-ori, and Ppd-ori obtained under the original parameters.

Multiobjective Optimization of Hydrophobic Process for Performance Analysis of Cover Date Bunch

ABSTRACT Technologies like bunch covers have been shown to improve quality of date fruits. It is one of the approaches which are believed effective to protect the most sensitive stage of date fruit from autumnal rain and carob date. An experimental procedure is set up to allow farmers to predict, analyze, and optimize cover bunch performance after its use in protection applications and to select the ideal bag. The cover quality is designed based on a mathematical model and further optimized using MINITAB. From this analysis, the impact of each individual parameter on the desired response parameters has been studied. The results proved that the best cover could be found in optimal conditions. Using the influential parameters as tear strength, tensile strength, weight, air permeability and resistance to water penetration, the results indicate that the cover bunch remains influenced after harvesting period by the resistance to water penetration properties and tear strength as well.

Combining artificial and human intelligence to manage cross-cultural knowledge in humanitarian logistics: a Yin–Yang dialectic systems view of knowledge creation

Purpose Aiming to resolve cross-cultural paradoxes in combining artificial intelligence (AI) with human intelligence (HI) for international humanitarian logistics, this paper aims to adopt an unorthodox Yin–Yang dialectic approach to address how AI–HI interactions can be interpreted as a sophisticated cross-cultural knowledge creation (KC) system that enables more effective decision-making for providing humanitarian relief across borders. Design/methodology/approach This paper is conceptual and pragmatic in nature, whereas its structure design follows the requirements of a real impact study. Findings Based on experimental information and logical reasoning, the authors first identify three critical cross-cultural challenges in AI–HI collaboration: paradoxes of building a cross-cultural KC system, paradoxes of integrative AI and HI in moral judgement and paradoxes of processing moral-related information with emotions in AI–HI collaboration. Then applying the Yin–Yang dialectic to interpret Klir’s epistemological frame (1993), the authors propose an unconventional stratified system of cross-cultural KC for understanding integrative AI–HI decision-making for humanitarian logistics across cultures. Practical implications This paper aids not only in deeply understanding complex issues stemming from human emotions and cultural cognitions in the context of cross-border humanitarian logistics, but also equips culturally-diverse stakeholders to effectively navigate these challenges and their potential ramifications. It enhances the decision-making process and optimizes the synergy between AI and HI for cross-cultural humanitarian logistics. Originality/value The originality lies in the use of a cognitive methodology of the Yin–Yang dialectic to metaphorize the dynamic genesis of integrative AI-HI KC for international humanitarian logistics. Based on system science and knowledge management, this paper applies game theory, multi-objective optimization and Markov decision process to operationalize the conceptual framework in the context of cross-cultural humanitarian logistics.

Multi-objective optimization and evolution of dissimilar welding process between Cr-Mo steel and austenitic stainless steel for power plant application

In this study, response surface methodology (RSM) was employed to optimize the welding parameters’ effects on mechanical properties of dissimilar welds between Cr-Mo steel grade (P11) and austenitic stainless steel (AISI 316LN). To determine the best welding parameters, variance analysis (ANOVA), desirability function, and perturbation analysis were used to create regression models and identify the significant parameters influencing tensile strength and hardness gaps in the weld joints. The results indicated that welding speed is the most significant parameter affecting both the austenitic hardness gap and tensile strength, while gas flow has the most significant impact on the hardness gap of Cr-Mo steel. Furthermore, welding speed positively influences the mechanical properties of dissimilar weld, whereas welding current has a slight negative effect on tensile strength. The optimum welding parameters were found to be 130 A for welding current, 70 mm min−1 for welding speed, and 13 l min−1 for welding gas flow, resulting in hardness gap values of 18.10 HV (Stainless steel side), 27.38 HV (Cr-Mo steel side), and a tensile strength of 453.90 MPa. The optimum parameter effect is concentrated at the weld interfaces between the fusion zone and the heat-affected zone. This effect led to limitations in grain coarsening, a reduction in the martensite and delta ferrite phase percentages, a slight increase in the bainite ratio, and a decrease in carbide precipitations. As a result, a homogenization of strain distribution in the optimum weld was achieved, leading to ductile fracture in Cr-Mo steel.

Multi-objective particle swarm optimization algorithm based on multi-strategy improvement for hybrid energy storage optimization configuration

In order to fully leverage the advantages of hybrid energy storage systems in mitigating voltage fluctuations, reducing curtailment rates of wind and solar power, minimizing active power losses, and enhancing power quality within distributed generation systems, while effectively balancing the economic and security aspects of the system, this paper establishes a multi-objective hybrid energy storage optimization configuration model that comprehensively considers the lifecycle economic costs, node voltage deviations, and system active power losses. To address the limitations of single-objective solution algorithms and the lack of diversity and premature convergence in multi-objective optimization processes, a multi-objective particle swarm optimization by multi-strategy improvements (CMOPSO-MSI) is proposed to solve the model. By introducing adaptive grid crowding distance and roulette wheel selection strategies to dynamically update the Pareto solution set, population uniformity and diversity are ensured. An improved dynamic nonlinear weighting strategy is introduced to enhance the algorithm's global search capability and convergence performance. A Gaussian mutation strategy is incorporated into the iteration process to enhance the uniformity of non-dominated solutions and particle search capabilities The aim is to achieve an optimal balance between the configuration cost and stability of hybrid energy storage systems. A simulation analysis under multiple cases is performed, taking an IEEE 33-node power distribution system as an example, with introducing photovoltaic and wind energy sources. The results demonstrate that the proposed algorithm achieves better Pareto frontiers and convergence performance compared to conventional multi-objective particle swarm optimization algorithms MOPSO, and classical multi-objective optimization algorithm NSGA-II, validating the effectiveness and accuracy of the proposed model and algorithm strategies in solving the hybrid energy storage optimization configuration problems. The approach concurrently considers system economics and grid stability while improving power quality.

Multi-objective Optimization of Injection Molding Process Based on One-Dimensional Convolutional Neural Network and the Non-dominated Sorting Genetic Algorithm II

<div>In the process of injection molding, the vacuum pump rear housing is prone to warping deformation and volume shrinkage, which affects its sealing performance. The main reason is the improper control of the injection process and the large flat structure of the vacuum pump rear housing, which does not meet its production and assembly requirements (the warpage deformation should be controlled within 1.1 mm and the volume shrinkage within 10%). To address this issue, this study initially utilized orthogonal experiments to obtain training samples and conducted a preliminary analysis using gray relational analysis. Subsequently, a predictive model was established based on a one-dimensional convolutional neural network (1D CNN). Input parameters from the injection molding process, including melt temperature, mold temperature, packing pressure, packing time, injection pressure, injection time, and cooling time, were used while warping deformation and volume shrinkage were considered as outputs. Global optimization was performed using the non-dominated sorting genetic algorithm II (NSGA-II), and the optimal combination of process parameters was evaluated using the criterion importance through intercriteria correlation—technique for order preference by similarity to ideal solution (CRITIC-TOPSIS). Moldflow analysis demonstrated that the obtained indicators outperformed the optimization results from orthogonal experiments, confirming the effectiveness of the injection molding process parameter optimization method based on 1D CNN-NSGA-II. In comparison to the pre-optimization results, product warping deformation decreased by 40.68%, and volume shrinkage reduced by 18.14%, and all of them meet the production requirements.</div>

Multi-objective optimization of water-alternating flue gas process using machine learning and nature-inspired algorithms in a real geological field

Flue gas water-alternating gas (flue gas-WAG) is a promising technique for enhancing oil production and reducing greenhouse gas emissions. The effective utilization of this technique relies on identifying the optimal factors, often determined through numerous numerical simulations. This paper introduces a cost-effective optimization framework that integrates machine learning models and diverse optimization algorithms to identify the best parameters for injecting flue gas-WAG into the "Gullfaks" reservoir. Robust Machine learning models, including Multilayer Perceptron, Cascade Forward Neural Network (CFNN), Radial Basis Function, and Generalized Regression Neural Network, were employed as proxy models. The CFNN model, with satisfactory agreement with actual data (average absolute relative error of 0.4543 % for oil recovery factor (RF) and 1.9366 % for CO2 storage), is selected for optimization through metaheuristic algorithms, including Non-dominated sorting genetic algorithm version II (NSGA-II), Pareto envelope-based selection Algorithm version-II (PESA_II), Multi-objective particle swarm optimization (MOPSO), and Multi-objective gray wolf optimization (MOGWO(. Among the optimization algorithms, MOGWO outperforms others in terms of speed and accuracy, yielding Pareto-optimal solutions with an RF of 0.8284 and a CO2 storage of 1.5314 × 107 (kg-mol). The proposed approach uses Pareto dominance for insightful field development planning, enabling decision-makers to choose flue gas-WAG parameters based on future circumstances.