Multi-objective Genetic Algorithm Approach Research Articles

Structural optimization and heat-fluid-solid simulation of a graded corundum-calcium hexaluminate purging plug by a multi-objective genetic algorithm approach

The purging plug is essential for enhancing production efficiency in molten steel refining, yet it faces challenges related to structural integrity due to its lifespan often not aligning with the ladle's repair cycle. This study introduces a novel corundum-calcium hexaluminate purging plug with gradual holes designed to alleviate internal stress concentration. Utilizing a multi-objective optimization model and fluid-solid coupling heat transfer method, the impact of structural parameters on thermal and mechanical properties was systematically investigated. The numerical simulation results indicate that increasing the number of gradient layers positively impacts temperature distribution. Notably, the D-210 (1.0 mm diameter) aperture reduces the stress gradient Δσmax by 648.06 MPa compared to D-212 (1.2 mm diameter) at the Y = 0.313 m section. Additionally, variation in inclination angle impacts tensile stress σt and shear stress τ, an inclination angle of 6° (α6) reduces maximum tensile stress by 211.41 MPa compared to an angle of 0° (α0).

Optimizing Air Contactor Design for Efficient Carbon Capture in Direct Air Capture Systems: A Multi-Disciplinary Approach

As carbon dioxide (CO2) emissions continue to rise, direct air capture (DAC) has emerged as an important technology for removing CO2 from ambient air to combat climate change. The air contactor design is a critical factor impacting DAC system performance and costs. This research describes a strategy for improving the design of a single air contactor unit to increase CO2 capture flux while minimizing costs using optimization techniques grounded in experimental data .The research focuses on a 100 m2 slab air contactor modeled utilizing cooling tower and scrubber principles. The system includes the chemical, mechanical, and structural engineering disciplines. The mass transfer coefficient, air velocity, packing depth, and capital cost per frontal area are four major design characteristics that were maximized.The model integrated requirements for low air velocity, pressure drop, and energy consumption, as well as adequate gas-liquid interaction via structured packing selection and depth.The design space was explored using a multi-objective optimization strategy based on gradient-based and heuristic methods. Initial sampling via a design of experiments methodology demonstrated response surface trends in the objective function across the design space. The response surfaces mapped the optimization objective as a function of the design variables. Using gradient information, sequential quadratic programming efficiently located an optimal design vector that met all constraints on the design variables and feasibility requirements. The constraints included bounds on mass transfer coefficient, air velocity, packing depth, and capital cost per frontal area. The optimized air contactor design achieved a CO2 capture flux of 10,440 kg/m2-yr at a cost of $ 0.068 per kg CO2 captured. The developed optimization framework maximizes contactor performance while decreasing operational costs. A multi-objective genetic algorithm approach was used to identify the trade-offs between the two competing objectives of maximized CO2 capture and minimized cost by expanding the formulation. The Pareto-optimal front of the optimization results offered numerous optimized solutions with specific DAC system setups. This methodology, as well as the optimum configurations identified, serve as the foundation for constructing larger-scale DAC systems, in future. Ongoing research aims to improve solution approaches by including advanced optimization software packages such as DAKOTA, which offers global optimization algorithms, as well as to boost model accuracy via integrating additional choice variables. Future optimization and modeling studies will include contactor interactions and the modeling of a full DAC facility to boost confidence that a globally optimal design has been established.Overall, this study illustrated an efficient optimization strategy for air contactor design and contributes to the improvement of sustainable carbon capture technology, which is crucial to controlling rising global CO2 levels. Contactor configuration optimization enables superior DAC system designs at lower costs, hence promoting the urgent deployment of negative emissions solutions.

Semiconductors for enhanced solar photovoltaic-thermoelectric 4E performance optimization: Multi-objective genetic algorithm and machine learning approach

In this study, a groundbreaking exploration of a concentrated photovoltaic-thermoelectric (CPV-TE) module employing an unprecedented selection of six thermoelectric materials across diverse temperature ranges is presented. This work innovatively employs a multi-objective optimization framework that combines genetic algorithms and goal attainment methods, aiming to optimize energy, exergy, environmental, and economic (4 E) performance. Notably, this study is the first to construct and evaluate five regression-based machine learning models with an emphasis on minimal root mean squared error and mean absolute error for rapid CPV-TE performance predictions, essential for real-world applications. Our analysis unveils that among the tested materials, the CPV-TE module incorporating lead telluride (PbTe) achieves the highest 4 E performance, demonstrating a peak exergy efficiency of 14.2 %, and annual energy savings and carbon reduction of 13.5 kWh and 6.4 kg, respectively. Furthermore, the Gaussian Process Regression model is identified as the most effective among the machine learning models for forecasting performance across the materials. This study significantly advances the field by providing novel insights into thermoelectric material selection and optimization for CPV-TE modules, and establishing pioneering forecasting tools that catalyze the efficient deployment of these systems.

A comparative study of machine learning and response surface methodologies for optimizing wear parameters of ECAP-processed ZX30 alloy

Magnesium, valued for its lightweight, recyclability, and biocompatibility, faces challenges like its poor wear behavior and mechanical properties that limit its adaptation for a multitude of applications. In this study, various statistical analyses, and machine learning (ML) techniques were employed to optimize equal channel angular pressing (ECAP) process parameters for improving the wear behavior of Mg-3wt.% Zn-0.7 wt% Ca alloy. ECAP was conducted up to four passes via route Bc at 250 °C. Wear testing of both as-annealed (AA) and ECAP-processed alloys was performed using the dry ball-on-flat wear method under varying loads, speeds, and time. One pass (1P) and 4Bc-ECAP resulted in a notable uniform grain refinement of 86 % and 91 %, respectively, compared to the AA. X-ray diffraction (XRD) analysis confirmed a refined structure attributed to extensive dynamic recrystallization. Mechanical wear testing revealed a significant reduction in volume loss (VL), up to 56 % and 28.5 % after 1P and 4Bc samples, respectively, compared to the AA sample, supported by the observed texture intensity. The coefficient of friction (COF) stabilizes at 0.30–0.45, indicating low friction characteristics. Next, by adjusting wear load and speed through design of experiments (DOE), the wear output responses, VL and COF, were experimentally investigated. The output responses were predicted in the second step using ML, 3D response surface plots, and statistical analysis of variance (ANOVA). According to the regression model, the minimal VL was attained at a 5 N applied load. Also, the wear speed and VL at different passes are inversely proportional. On the other hand, the optimal COF was obtained at applied load about 2–3 N and 250 mm/s at different passes. The wear process variables were then optimized using different optimization techniques namely, genetic algorithm (GA), hybrid DOE–GA, and multi-objective genetic algorithm (MOGA) approaches.

Structural Optimization of Scarfing Machine with Acceleration Profile and Multi-Objective Genetic Algorithm Approach

Scarfing is a type of flame treatment used to improve the quality of metal generated during steelmaking. It employs the principles of gas cutting to remove impurities and defects. Due to the high-temperature conditions and the need for uniform metal treatment, mechanical scarfing performed via a frame is preferred over manual hand scarfing. To achieve stable mechanical scarfing, a properly designed frame is essential. Generally, while using more material can create stable equipment, it also increases costs. Therefore, this study proposed a design method that selects an acceleration profile to minimize the shock on the frame during scarfing equipment operation while using a multi-objective genetic algorithm to minimize weight and maximize rigidity. Because modifying existing scarfing equipment based on the optimization results would incur additional costs and time, pre-optimizing through simulation before equipment fabrication is crucial. Optimization was achieved via the dimensional optimization of the existing frame equipment. As a result, the weight of each part and the deformation decreased by an average of 17.05 kg and 3.93%, respectively.

Performance optimization for an optimal operating condition for a shell and heat exchanger using a multi-objective genetic algorithm approach.

In this study, shell and heat exchangers are optimized using an integrated optimization framework. In this research, A structured Design of Experiments (DOE) comprising 16 trials was first conducted to systematically determine the essential parameters, including mass flow rates (mh, mc), temperatures (T1, t1, T2, t2), and heat transfer coefficients (€, TR, U). By identifying the first four principal components, PCA was able to determine 87.7% of the variance, thereby reducing the dimensionality of the problem. Performance-related aspects of the system are the focus of this approach. Key outcomes (€, TR, U) were predicted by 99% R-squared using the RSM models. Multiple factors, such as the mass flow rate and inlet temperature, were considered during the design process. The maximizing efficiency, thermal resistance, and utility were achieved by considering these factors. By using genetic algorithms, Pareto front solutions that meet the requirements of decision-makers can be found. The combination of the shell and tube heat exchangers produced better results than expected. Engineering and designers can gain practical insight into the mass flow rate, temperature, and key responses (€, TR, U) if they quantify improvements in these factors. Despite the importance of this study, it has several potential limitations, including specific experimental conditions and the need to validate it in other situations as well. Future research could investigate other factors that influence system performance. A holistic optimization framework can improve the design and engineering of heat exchangers in the future. As a result of the study, a foundation for innovative advancements in the field has been laid with tangible improvements. The study exceeded expectations by optimizing shell and heat exchanger systems using an integrated approach, thereby contributing significantly to the advancement of the field.

Optimization of landscape garden greening design based on multi objective genetic algorithm

This paper presents a novel approach to optimize landscape garden greening design using a multi-objective genetic algorithm (MOGA)[1]. Incorporating genetic algorithms into landscape architecture offers a promising avenue for efficiently navigating the complex and multidimensional design space inherent in green infrastructure projects. Through a comprehensive bibliometric analysis of existing literature, this study synthesizes key insights into the application of genetic algorithms in landscape design and identifies gaps for further exploration[2]. Leveraging the evolutionary process of genetic algorithms, our methodology focuses on simultaneously optimizing multiple objectives such as biodiversity conservation, aesthetic appeal, water efficiency, and ecosystem services provisioning[3]. By iteratively evolving and selecting landscape configurations based on fitness criteria derived from these objectives, the MOGA enables designers to explore a diverse range of design alternatives and identify Pareto-optimal solutions that balance competing priorities. The integration of genetic algorithms into landscape design facilitates an iterative and adaptive design process, allowing for the exploration of complex trade-offs and the generation of innovative design solutions. Through a case study application, we demonstrate the effectiveness of the MOGA approach in optimizing landscape garden greening designs, showcasing its potential to enhance sustainability, resilience, and functionality in urban green spaces. This research contributes to the growing body of knowledge on computational design methods in landscape architecture and provides valuable insights for practitioners and researchers seeking to leverage genetic algorithms for optimizing green infrastructure projects.

A multi-objective genetic algorithm approach applied to compact meander branch line couplers design for 5G-enabled IoT applications

A multi-objective genetic algorithm approach applied to compact meander branch line couplers design for 5G-enabled IoT applications

MULTI-OBJECTIVE GENETIC ALGORITHM FOR THE ASSEMBLY LINE WORKER ASSIGNMENT AND BALANCING PROBLEM: A CASE STUDY IN THE AUTOMOTIVE SUPPLY INDUSTRY

The primary challenge in assembly line design is the need for more appropriately allocating tasks and workers to workstations. This study addresses the problem of line balancing and worker assignments, considering the performance disparities among workers during the line balancing process. In the relevant literature, this problem is known as the Assembly Line Worker Assignment and Balancing (ALWAB) problem. This research examines a multi-objective ALWAB Type-2 problem, simultaneously evaluating cycle time and squared load assignment objectives. The study is conducted based on a real-life scenario in a sub-industry automotive industry that manufactures cable equipment. To solve this problem, a multi-objective genetic algorithm approach is proposed. Recognising that the selection of parameter values will influence the algorithm’s performance, parameter calibration has been performed. A full factorial experimental design and the irace method have been utilised for this purpose. The results are compared with those using parameter values utilised for similar problems in the literature. Furthermore, a sensitivity analysis has been carried out to examine the impact of various relative weight values of the objectives on the result. The results indicate that the experimental design generally yields superior results compared to other methods.

Multi-objective genetic algorithm approach for enhanced cumulative hydrogen and methane-rich syngas emission through co-pyrolysis of de-oiled microalgae and coal blending

Towards establishing low-carbon bio-economy, the energy-rich syngas is considered a global energy carrier. Targeting hydrogen as a promising advanced fuel, the significance of methane also increases due to its direct conversion capability into hydrogen. The current study aims to use a co-pyrolysis-based valorization of de-oiled microalgae and low-rank coal blend to generate H2 and CH4 rich syngas. Pyrolysis kinetic models, Kissinger-Akahira-Sunose (KAS) and Starink (STK) are used to evaluate apparent activation energy (Ea). The gradual addition of microalgae (0⎼100%) in coal reduces Ea from 189.11⎼55.87 kJ/mol and 180.16⎼54.61 kJ/mol by KAS and STK method, respectively. The maximum hymethane carrying ratio is observed 2.51 and 3.51 at optimized conditions of response surface methodology (RSM) and artificial neural network-based multi-objective genetic algorithm (ANN-MOGA), respectively. Maximum H2 (54.5 %) in the syngas is observed at mid pyrolysis stage (451 °C) using ANN-MOGA optimized conditions (blending ratio - 42.25 % and heating rate-13.8 °C/min). This study highlights the advantage of ANN-MOGA optimization over statistical based optimization. Hence, incorporating of the evolutionary algorithm as integrated ANN-MOGA optimization could be an efficient way for hymethane rich syngas emission in co-pyrolysis approach to gain carbon-neutral energy with zero waste discharge.

A Multi-Objective Genetic Algorithm Approach for Silicon Photonics Design

A multi-objective genetic algorithm approach is formulated to optimize the design of silicon-photonics complex circuits with contradicting performance metrics and no closed-form expression for the circuit performance. A case study is the interleaver/deinterleaver circuit which mixes/separates optical signals into/from different physical channels while preserving the wavelength-division-multiplexing specifications. These specifications are given as channel spacing of 50 GHz, channel 3-dB bandwidth of at least 20 GHz, channel free spectral range of 100 GHz, crosstalk of −23 dB or less, and signal dispersion less than 30 ps/nm. The essence of the proposed approach lies in the formulation of the fitness functions and the selection criteria to optimize the values of the three coupling coefficients, which govern the circuit performance, in order to accommodate the contradicting performance metrics of the circuit. The proposed approach achieves the optimal design in an incomparably short period of time when contrasted with the previous tedious design method based on employing Z-transform and visual inspection of the transmission poles and zeros.

Design of Hybrid-Laminar-Flow-Control Wing and Suction System for Transonic Midrange Aircraft

Hybrid laminar flow control (HLFC) has shown significant promise in the viscous drag reduction of aircraft. However, the use of HLFC for commercial applications requires further simplification. The current study proposes tools for the conceptual design of transonic HLFC wing and suction system. In the first part of the study, airfoil sections for the wing are optimized for minimum total drag using a multi-objective genetic algorithm approach at six spanwise locations. The induced drag of the wing is estimated using a vortex lattice method solver. In the second part of the study, suction system design is performed using ASPeCT, an in-house solver for HLFC system design. A simplified inner structure for the suction system is proposed, which can be integrated easily within the wing structure. A total drag penalty approach is proposed to establish a tradeoff between matching the target suction distribution and the complexity of the suction system. Finally, the additional weight and off-design performance of the suction system are analyzed for a ±0.1 change in the design lift coefficient. A maximum fuel reduction of 7% can be expected with the HLFC system taking into account the additional weight added and power off-take from the engine.

Optimization of Process Parameters for Better Surface Morphology of Electrical Discharge Machining-Processed Inconel 825 Using Hybrid Response Surface Methodology-Desirability Function and Multi-objective Genetic Algorithm Approaches

Optimization of Process Parameters for Better Surface Morphology of Electrical Discharge Machining-Processed Inconel 825 Using Hybrid Response Surface Methodology-Desirability Function and Multi-objective Genetic Algorithm Approaches

Multi-Objective Parametric Shape Optimisation of Body-Centred Cubic Lattice Structures for Additive Manufacturing

There has been significant interest in additively manufactured lattice structures in recent years due to their enhanced mechanical and multi-physics properties, making them suitable candidates for various applications. This study presents a multi-parameter implicit equation model for designing body-centred cubic (BCC) lattice structures. The model is used in conjunction with a multi-objective genetic algorithm (MOGA) approach to maximise the stiffness of the BCC lattice structure while minimising von-Mises stress within the structure under a specific loading condition. The selected design from the MOGA at a specific lattice density is compared with the classical BCC lattice structure and the designs generated by a single-objective genetic algorithm, which focuses on maximising stiffness or minimising von-Mises stress alone. By conducting a finite element analysis on the optimised samples and performing mechanical testing on the corresponding 3D-printed specimens, it was observed that the optimised lattice structures exhibited a substantial improvement in mechanical performance compared to the classical BCC model. The suitability of multi-objective and single-objective optimisation approaches for designing lattice structures was further investigated by comparing the corresponding designs in terms of their stiffness and maximum von-Mises stress values. The results from the numerical analysis and experimental testing demonstrate the significance of the application of an appropriate optimisation strategy for designing lattice structures for additive manufacturing.

Multi-objective distributionally robust approach for optimal location of renewable energy sources

Wind turbines and solar photovoltaic (PV) systems are intermittent and uncertain energy sources that disturb grid planning operations. In this paper, we establish a multi-objective distributionally robust optimization model for optimal locations of wind turbines and solar photovoltaics (PV) that minimize the variance of renewable energy sources and maximize power production. Moreover, this paper evaluates the accuracy of the Autoregressive Moving Average (ARMA), Deep Learning Gated Recurrent unit (GRU), and Deep Learning Long Short-Term Memory (LSTM) as forecasting models for wind speed and solar irradiation and compares their root mean square errors (RMSE). Using the forecasting error information, we characterize the uncertain variables in the ambiguity set, incorporating the bounds, means, and covariance values. Furthermore, we propose a modified multi-objective non-dominated sorting genetic algorithm (NSGA-II) approach to achieve a tractable Pareto front solution. To verify the effectiveness of the model, we use the actual candidate sites for wind turbines and solar photovoltaic (PV) systems in Saudi Arabia. The results demonstrate that our proposed model is an attractive and less conservative solution than a multi-objective robust optimization model when considering forecasting uncertainties.

Multi-objective optimization design of the Hinge Sleeve of Cubic based on Kriging.

In this study, the multi-objective optimal design of the Hinge Sleeve of Cubic (HSC) was achieved by combining the central composite design (CCD), Kriging and multi-objective genetic algorithm (MOGA) approaches. Firstly, the model of the HSC was established and the appropriate design variables were selected. The mass, the maximum deformation and the maximum equivalent stress of the HSC were taken as the optimization objectives. After comparative analysis of the parameters, the parameter with the greatest influence on the optimization objectives was selected as the geometric constraint. Subsequently, according to the results of the experimental design, the Kriging model was used to establish the response surface optimization model of the objective function. And finally the best optimization results were obtained by using MOGA. The experimental results show that the optimization strategy is reliable and the mass of the optimized model is reduced by 24.84%, which achieves the lightweight design of the HSC while meeting the actual production requirements, saves the design cost and improves the material utilization.

Parametric study and optimization of pillow-plate heat exchanger using multi-objective genetic algorithm and entropy generation minimization approaches

Parametric study and optimization of pillow-plate heat exchanger using multi-objective genetic algorithm and entropy generation minimization approaches

Formulation and characterization of a low-cost high-energy grain-based beverage premix: A multi-objective genetic algorithm approach

There is a huge demand for low cost healthy energy drinks amongst the consumers. A novel multigrain beverage premix (MGBP) was formulated using barley, pearl millet, quinoa, and yellow split pea by multi-objective genetic algorithm (MOGA) technique. The effects of processing like roasting and malting on nutritional compositions and correlations were studied using principal component analysis (PCA). Malting had significant effect on almost all nutritional components (p < 0.05). Roasting increased the total carbohydrate content by 67.7% in yellow split pea and 77.2% in barley flours. The Pearson correlation coefficient demonstrated a strong correlation of energy content with carbohydrate and fat content. The optimum combination obtained from MOGA and validated by fuzzy analysis was 30, 28, 30 and 12 g of roasted yellow pea flour (RYPF), roasted pearled barley flour (RPBF), malted pearl millet flour (MPMF) and malted quinoa flour (MQF), respectively per 100 g of MGBP with an energy value of 345.8 kcal and cost Rs. 4.88. The processing triggered the protein-starch interaction and agglomeration as confirmed by scanning electron microscopy (SEM); however, the chemical structure remained intact as revealed by Fourier transform infrared (FTIR) spectroscopy.

Optimizing pipe network design and central plant positioning of district heating and cooling System: A Graph-Based Multi-Objective genetic algorithm approach

• A graph-based multi-objective genetic algorithm for district energy system design. • Simultaneously optimize DHC pipe network layout and central plant positioning. • Improve valid offspring design generation and enable plant positioning optimization. • Six real-world case study to demonstrate outperformance of proposed work. District heating and cooling systems are one of the most promising technologies to support rapid decarbonization of our energy system as they enable significant energy and emissions savings. However, realizing such savings is highly dependent on the pipe network design and the central plant positioning. Today, the pipe network design and the central plant positioning still rely on expert experience and most of them are sub-optimal in terms of cost and efficiency performance. To tackle this issue, we propose a graph-based multi-objective genetic algorithm for simultaneous optimization of the pipe network design and the position of a central plant in a district heating and cooling system. Differing from a traditional genetic algorithm, our proposed algorithm uses a custom mutation and crossover technique to ensure the generation of valid offspring. The extra relocation step in the algorithm enables the optimization of the central plant location. Finally, we used the proposed algorithm to design the pipe network layout and decide the central plant location for six city districts in a metropolitan area in China. To assess and benchmark the performance of our proposed method, we utilized an expert-based approach in which several experienced engineers produced reference designs. Results demonstrate that our proposed algorithm outperforms the reference designs for all six cases. The proposed method was able to achieve a maximal pressure drop reduction of 37% and a maximal cost saving of 10% compared to the reference design.

Multi-objective optimization of µ-EDM parameters for µ-hole drilling on Hastelloy C 276 super alloy using Response Surface Methodology and multi-objective genetic algorithm

Hastelloy C 276 is one of the nickel-based super alloys which has an excellent resistance against corrosion at high temperature. It has a major application in nuclear and aerospace industry. Micro-electrical discharge machining (µ-EDM) is a highly accurate and precise non-conventional micro-machining process to fabricate this alloy. Thus, in this research paper an effort has been made to examine the impact of input process parameters like capacitance, gap voltage and pulse on time (t on ) on the response variables i.e., material removal rate (MRR), tool wear rate (TWR) and diametral overcut (DOC) during µ-EDM of Hastelloy C 276. Response Surface Methodology (RSM) is a collection of statistical and mathematical tools which determine the relationship between input and output characteristics. Box-Behnken design (BBD) is a special type of RSM based design that reduces the no of experiments without affecting the variation between the input and response parameters. RSM based BBD is used in the current study to develop the mathematical models for each response variable. RSM based desirability function (RSM-DF) which convert multiple responses into a scale free desirability, is used as an optimization tool in the present study. The current work used the multi-objective genetic algorithm (MOGA), a non-conventional searching based multi-response optimization algorithm, to optimize the machining parameters for maximum MRR along with minimum TWR and DOC. The lowest machining time is achieved by experiment no 10 and the least entry µ-hole diameter is drilled by experiment no 15. ANOVA analysis accomplished that capacitance has the highest significant contribution towards MRR, TWR and DOC. 3D response surface plot discloses that MRR, TWR and DOC is increased with capacitance until 5600 pF, 5600 pF and 8900 pF and then there is little reduction took place for all the three responses. The optimization result from RSM-DF is capacitance of 1001.560 pF, gap voltage of 137.6670 volt and t on of 41.532 µs for maximum MRR along with minimum TWR and DOC. MOGA approach reveals that maximum MRR of 0.005153 mm 3 /min along with minimum TWR of 0.002423 mm 3 /min and minimum DOC of 0.028691 mm is found at a capacitance of 1001.610 pF, gap voltage of 137.6671 volt and t on of 48.025 µs. Conformation experiments are conducted and the absolute percentage errors between the experimental and optimized values are found to be less than 5% for both approaches. Field emission scanning electron microscopy (FESEM) analysis reveals that MOGA settings drilled µ-hole with less DOC, more circularity and less surface cracks compared to the initial and RSM-DF parametric settings. A deposition of C and Cu took place in the side wall of the micro-hole from the dielectric fluid and tool material respectively.