Genetic Algorithm Approach Research Articles

Cost Minimization with Project Crashing: Comparison of the Traditional Solution and Genetic Algorithm Approach

Existence of delays and cost overruns frequently puts the project viability in jeopardy. The integrated nature of these threats brings forward project scheduling as the primary determinant of project management success. The quality of project scheduling depends highly on the way resources are assigned to activities. In the project management literature, the efficiency of resource allocation is examined closely by the phenomenon called project crashing. This study introduces traditional and genetic algorithm approaches for the project crashing events and explains their steps in achieving the most efficient resource allocation. Within this context, the project crashing event is visualized, the insights of alternative approaches are described, and their implementations are illustrated with a case study. Besides, the procedures required for adopting the genetic algorithm approach to a typical problem are expressed. The case study illustration reveals the advantages and disadvantages of the genetic algorithm approach over the traditional approach. It is observed that the genetic algorithm approach can reach the solution in a single phase while the traditional approach requires multiple phases. On the other hand, the genetic algorithm approach may not reach the optimum solution unless the toolbox options are appropriately selected. This study presents the contribution of operational research to the project management body of knowledge by demonstrating the applicability and efficiency of genetic algorithm in the project crashing events. Researchers and industry practitioners may benefit from the proposed approach by following the indicated procedures to incorporate genetic algorithm into optimization issues in different fields.

Synergistic approach to wear rate forecasting in AZ31/5% Yttria stabilized zirconia reinforced composite through response surface methodology-genetic algorithm integration

In this work, the wear behavior of a novel AZ31 magnesium alloy reinforced with 5% yttria-stabilized zirconia (YSZ) composite was evaluated using a hybrid Response Surface Methodology (RSM) and Genetic Algorithm (GA) approach. The composite was fabricated using ultrasonic-assisted stir squeeze casting technique, ensuring homogeneous distribution of spherical YSZ particles, as validated by the scanning electron microscope integrated with energy dispersive spectroscope. Wear tests were carried out according to the ASTM standards using a pin-on-disc (POD) tribometer, with applied load (AL), sliding speed (SS), and sliding distance (SD) as the main parameters. An empirical wear rate regression model was developed using RSM/Box-behnken design, and Genetic Algorithm was deployed for parametric optimization, achieving a minimal wear rate of 0.0144 g/m under a load of 30 N, sliding speed of 260 rpm, and sliding distance of 400 m. Confirmation tests were performed to validate the GA predictions. The wear mechanisms were observed, showing reduced wear in GA-optimized samples due to optimized load distribution resulting minimized ploughing, grooving and delamination. This work highlights the efficacy of the hybridized RSM / GA for the wear performance in advanced magnesium alloy matrix composites.

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).

A Vortex Electromagnetic Wave Beam Control Method based on Amplitude-Modulated Concentric Circular Array

Abstract In order to enhance the design efficiency of vortex electromagnetic (EM) wave radar detection systems, a method for generating and controlling vortex EM waves based on amplitude-modulated concentric circular array (CCA) is proposed. This method allows for precise control of the vortex EM waves, ensuring accurate main lobe direction while effectively reducing sidelobes. Based on approximate orthogonal transformation, formulas for calculating the excitation amplitudes of each sub-circular array are derived and the time complexity of this method is negligible compared to search-based algorithms. Extensive simulation experiments are conducted and compared with the results obtained from the genetic algorithm (GA) approach, providing robust evidence of the effectiveness of this method(SLL < -30dB).

A modified bi-objective NSGA-II approach to sustainability in reconfiguration planning of dynamic cellular manufacturing systems

Manufacturing plant layouts are developed to facilitate optimal process flow. Modern manufacturing systems must meet present production demands and be adaptable to changes in process flow in the future. Dynamic Cellular Manufacturing Systems (DCMS) increase the flexibility of layouts by reconfiguring cell structure and equipment distribution, to effectively adjust part routings for optimal process flow. Frequent reconfiguring of plant layout may not always be feasible or economical, however, when new product releases are planned, reconfiguring the plant layout to optimise the work-flow may be extremely beneficial. This paper presents a Non-dominated Sorting Genetic Algorithm (NSGA-II) approach to solving a DCMS problem in a sustainable, and responsible manner. A bi-objective integer programming model was developed over multiple planning horizons with fluctuating product demands. This model aims to achieve sustainability by reducing the cost of production, mitigating the environmental impact of production, and minimise negative social impacts on labourers that work in such environments. A penalty function approach was used to enforce the model constraints during optimisation. This study details trade-offs between the economic factors of a DCMS, the environmental implications of reconfiguring such a system, and the social impacts of reconfigurations on the workforce.

Identification and functional analysis of genes selected using different chemometric techniques on multiarray expression data of liver from high-fat diet treated mice

The prevalence of obesity is increasing, and this lifestyle disease is related to a high-fat diet, a surplus in caloric intake, and increased inflammation. This study aimed to use a publicly available dataset of microarray gene expression data from the liver of high-fat diet fed mice (GSE39549) to determine the functional importance of small subsets of the overall genes. The regulatory aspects of the chosen mice genes were extrapolated to human genes for the determination of potential diagnostic and therapeutic targets. The chemometric approaches of principal component analysis (PCA), random forest (RF), and genetic algorithm (GA) were used as data reduction techniques to select 50 genes from a total of 15,000 genes to differentiate liver samples from high-fat diet and normal diet-fed mice. A subset of 30 genes from each of the techniques were processed with classification techniques of k-nearest neighbor and support vector machines. The results showed that random forest was best at differentiating the samples and GA was the least accurate. The results of functional annotation and protein-protein interactions showed that genes selected by PCA and RF were more associated with obesity as they identified functions related to inflammatory processes, as well as lipid and cholesterol metabolic processes. The genes selected by GA identified processes related to cilium and cell projection. The proteins identified by RF, such as Msmo and Sqle, had roles in cholesterol metabolic and biosynthetic processes. The results showed that combining the genes selected by PCA and RF allowed a better understanding of the overall functional protein modules. The crosstalk genes such as Abcg5 as well as Abcg8 that relate cholesterol metabolic and biosynthetic process to glutathione metabolic process were identified. Various miRNAs-gene interactions are present in humans for most of the genes identified by PCA, RF, or GA. Some genes that showed fewer interactions with human miRNAs are CIDEA, PLIN4, and NME8. The results suggest the use of different chemometric analyses in combination with functional genomics to identify different sets of targets for diagnostic, therapeutic, and future research.

ANNEGA: an artificial neural network embedded genetic algorithm approach for energy efficient street lighting

ANNEGA: an artificial neural network embedded genetic algorithm approach for energy efficient street lighting

Multi-objective optimization and inverse design of complementary field-effect transistor using combined approach of machine learning and non-dominated sorting genetic algorithms for next-generation semiconductor devices

Complementary field-effect transistors (CFETs), which are structures in which different types of transistors are vertically stacked with a shared control gate, are being focused on for continuing to satisfy Moore's law by overcoming the limitations in pitch scaling. The structures of semiconductor devices become more complex as technology node shrinks, and interrelated multivariate parameters increase. In addition, predicting problems and proposing solutions by identifying complex patterns within extensive data collected for emerging semiconductor designs pose significant computational challenges and are inherently difficult. As a breakthrough in design technology co-optimization for advanced devices, this study developed a novel optimization framework integrating technology computer-aided design simulations, machine learning, and non-dominated sorting genetic algorithms. The developed framework provides unbiased optimal solutions, even in a high-dimensional objective space, while considering the tradeoff relationships between multiple variables. In addition, it enables inverse design to identify the design parameters of devices that satisfy specific electrical performance criteria using only a forward model, while achieving an error rate of less than 2%. Using this framework, we analyzed the operational mechanism of CFETs by comparing the inverse designs of various devices. This novel approach is particularly important when the design space is complex and extensive and is well suited for developing devices that emerge with technological advancements in the semiconductor industry.

Three-Dimensional Coverage Path Planning for Cooperative Autonomous Underwater Vehicles: A Swarm Migration Genetic Algorithm Approach

Cooperative marine exploration tasks involving multiple autonomous underwater vehicles (AUVs) present a complex 3D coverage path planning challenge that has not been fully addressed. To tackle this, we employ an auto-growth strategy to generate interconnected paths, ensuring simultaneous satisfaction of the obstacle avoidance and space coverage requirements. Our approach introduces a novel genetic algorithm designed to achieve equivalent and energy-efficient path allocation among AUVs. The core idea involves defining competing gene swarms to facilitate path migration, corresponding to path allocation actions among AUVs. The fitness function incorporates models for both energy consumption and optimal path connections, resulting in iterations that lead to optimal path assignment among AUVs. This framework for multi-AUV coverage path planning eliminates the need for pre-division of the working space and has proven effective in 3D underwater environments. Numerous experiments validate the proposed method, showcasing its comprehensive advantages in achieving equitable path allocation, minimizing overall energy consumption, and ensuring high computational efficiency. These benefits contribute to the success of multi-AUV cooperation in deep-sea information collection and environmental surveillance.

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.

The hybrid approach of genetic algorithm and particle swarm optimization on reduced weld line defect in plastic injection molding

Weld lines are a serious defect observed in plastic injection molded parts, impacting both their cosmetic appearance and mechanical properties. Controlling the conditions of plastic injection is crucial to mitigate these weld lines. This study introduces a novel approach to identify polypropylene injection molding (PIM) conditions aimed at reducing weld lines in polypropylene parts. The PIM conditions considered in this study include melt temperature, injection pressure, packing pressure, packing time, and cooling time. An orthogonal array Taguchi L27 design was employed for the experimental setup, producing 27 polypropylene parts with varying combinations of process conditions. The width of weld lines generated on the parts’ surfaces was measured using an optimum microscope for all trials. Parametric analysis was conducted using response surface plots and contour plots to estimate the process conditions yielding minimum weld lines. Analysis of variance and regression analysis were employed to interpret the experimental data, with the resulting regression equation used to predict weld lines for a set of PIM process conditions. Finally, two efficient optimization algorithms, genetic algorithm (GA), and particle swarm optimization (PSO), were implemented using MATLAB programming to estimate the optimum process conditions for minimizing weld lines. The GA and PSO predicted weld line widths of 6.12302 μm and 6.123 μm, respectively, representing an 18.51% improvement in results. These findings demonstrate that the novel approach presented in this study can be effectively and reliably applied to address plastic product defects in the industry.

A Hybrid Genetic Algorithm Approach based on Patient Classification to Optimize Home Health Care Scheduling and Routing

This study aims to solve the multi-objective problem of home healthcare scheduling and routing. The former’s objectives are to upgrade the travel distance, the workload balance, and the waiting time of caregivers. A novel approach was proposed based on patient and caregiver clustering with the K-means++ algorithm in the first step and a hybrid genetic algorithm to optimize the global operation in the second step. The problem was solved regarding the deterministic and the uncertain aspect. The uncertain parameter investigated is the number of patients. A numeric study was conducted to prove the performance of the recommended approach using the Solomon Benchmark.

Optimization and performance evaluation of a novel absorption-based atmospheric water generator system for enhanced energy efficiency

Providing potable water poses a severe and chronic challenge for the human race. Conventional desalination systems are only feasible in large-scale production, requiring significant energy and a source of feeding water. Atmospheric water harvesting is a decentralized water production method that uses air humidity as a renewable water source. This technique holds promise for providing water even in hard-to-reach regions with no source of liquid water. However, the feasibility of this method declines in arid or semi-arid climates. In this paper, a novel absorption-based atmospheric water generator is proposed to improve the feasibility and cost-effectiveness of this technique to make it a viable method for producing fresh water, even in arid regions. The system is modeled using EES software to explore the effects of seven adjustable parameters on its performance. Subsequently, the modeled system is transferred into MATLAB through the utilization of artificial neural networks. A tri-objective optimization is then carried out using the genetic algorithm approach. The optimized system can produce 14016m3/year of fresh water, with a levelized cost of approximately 33.72$/m3, operating under hot and arid weather conditions (27°C and 25% RH). The data acquired from the system’s performance parameters is leveraged to derive correlations that are used to determine the system’s performance based on the input weather data. These correlations allow for the assessment of system feasibility across various regions and serve as a valuable shortcut in modeling similar systems for future research.

Non-covalent interaction, topology, molecular properties, spectral and quantum chemical analysis of 2,5-dinitrophenol

Density functional theory (DFT) using the Becke-3-Parameter-Lee-Yang-Parr (B3LYP) functional and Ab-initio (HF) with the 6–311++G(d,p) basis set were employed to investigate the geometry, electronic structure, and absorption spectra of 2,5-Dinitrophenol(25DNP). The vibrational spectrum was utilized to identify functional groups. The ultraviolet–visible (UV–Vis) spectrum was simulated in the gas phase using TD-DFT to determine the range and percentage of transmission. The computed HOMO and LUMO energies indicate that charge transfer occurs within the molecule. The compound exhibits a molecular first hyperpolarizability (second-order optical nonlinearity) value of 1.690 × 10−30 esu, which is approximately five times greater than that of urea. For hyperpolarizability calculations, the hybrid Hartree-Fock exchange-correlation functional was used in conjunction with the 6–311++G(d,p) basis set. Fukui indices were employed to identify local reactive sites in the chemical system during electrophilic, nucleophilic, radical, and dual descriptor attacks. Docking simulations were performed using the Lamarckian Genetic Algorithm (LGA) and local search approach, ensuring proper establishment of the ligand molecules' starting position, orientation, and torsion. According to the molecular electrostatic potential (MEP) map, positive potential sites are found around hydrogen atoms, while negative potential sites are near electronegative atoms, providing insights into regions where the molecule may interact with other molecules, both internally and externally. Similarly, Mulliken charges and Fukui indices within the organic complex sustain the intermolecular hydrogen bonds.

Turbofan Performance Estimation Using Neural Network Component Maps and Genetic Algorithm-Least Squares Solvers

Computational models of turbofans that are oriented to assist the design and testing of innovative components are of fundamental importance in order to reduce their environmental impact. In this paper, we present an effective method for developing numerical turbofan models that allows reliable steady-state turbofan performance calculations. The main difference between the proposed method and those used in various commercial algorithms, such as GasTurb, GSP 12 and NPSS, is the use of neural networks as a multidimensional interpolation method for rotational component maps instead of classical β parameter. An additional aspect of fundamental importance lies in the simplicity of implementing this method in Matlab and the high degree of customization of the turbofan components without performing any manipulation of variables for the purpose of reducing the dimensionality of the problem, which would normally lead to a high condition number of the Jacobian matrix associated with the nonlinear turbofan system (and, thus, to significant error). In the proposed methodology, the component behavior can be modeled by analytical relationships and through the use of neural networks trained from component bench test data or data obtained from CFD simulations. Generalization of rotational component maps by feedforward neural networks leads to an average interpolation error up to around 1%, for all variables. The resulting nonlinear system is solved by a combined genetic algorithm and least squares algorithm approach, instead of the standard Newton’s method. The turbofan numerical model turns out to be convergent, and results suggest that the trend in overall turbofan performance, as flight conditions change, is in agreement with the outputs of the GSP 12 software.

Multiobjective optimization of transpiration cooling in hypersonic laminar flow using a genetic algorithm approach

The efficient utilization of transpiration cooling is crucial for addressing coolant consumption challenges in hypersonic vehicle design. This study focuses on optimizing the blowing ratio, a key parameter in transpiration cooling. A multiobjective genetic algorithm is employed to determine the optimal blowing ratio using an experimental correlation. The optimization objectives include average thermal effectiveness, length of cooling with thermal effectiveness greater than 0.5, and coolant reservoir volume over a 60 s duration cooling process. The results reveal that a blowing ratio of 0.108 % is the optimal point, with corresponding values for average thermal effectiveness, coolant reservoir volume, and length of cooling being 0.493, 7.093 m³, and 0.14 m, respectively. A comparison with the maximum experimental blowing ratio indicates a 56.61 % reduction in average thermal effectiveness, accompanied by a 56.68 % decrease in coolant reservoir volume. This study highlights the application of multiobjective genetic algorithms in optimizing the transpiration cooling blowing ratio for hypersonic vehicles.

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.

Gas exchange optimization in aircraft engines using sustainable aviation fuel: A design of experiment and genetic algorithm approach

The poppet valves two-stroke (PV2S) aircraft engine fueled with sustainable aviation fuel is a promising option for general aviation and unmanned aerial vehicle propulsion due to its high power-to-weight ratio, uniform torque output, and flexible valve timings. However, its high-altitude gas exchange performance remains unexplored, presenting new opportunities for optimization through artificial intelligence (AI) technology. This study uses validated 1D + 3D models to evaluate the high-altitude gas exchange performance of PV2S aircraft engines. The valve timings of the PV2S engine exhibit considerable flexibility, thus the Latin hypercube design of experiments (DoE) methodology is employed to fit a response surface model. A genetic algorithm (GA) is applied to iteratively optimize valve timings for varying altitudes. The optimization process reveals that increasing the intake duration while decreasing the exhaust duration and valve overlap angles can significantly enhance high-altitude gas exchange performance. The optimal valve overlap angle emerged as 93 °CA at sea level and 82 °CA at 4000 m altitude. The effects of operating parameters, including engine speed, load, and exhaust back pressure, on the gas exchange process at varying altitudes are further investigated. The higher engine speed increases trapping efficiency but decreases the delivery ratio and charging efficiency at various altitudes. This effect is especially pronounced at elevated altitudes. The increase in exhaust back pressure will significantly reduce the delivery ratio and increase the trapping efficiency. This study demonstrates that integrating DoE with AI algorithms can enhance the high-altitude performance of aircraft engines, serving as a valuable reference for further optimization efforts.

On determining structural wall layout during the adaptive reuse process of historic masonry buildings

Defining modifications to a building’s layout during the adaptive reuse of historic masonry buildings can be challenging since the existing structural grid constrains the transformations to be performed. This paper proposes a novel methodology for exploring modifications to a structural wall layout during the adaptive reuse process of historic buildings. The primary objective is to redefine structural wall arrangements to accommodate new openings and/or cuts in existing structural elements, offering designers flexibility in spatial arrangement modifications. Considering that existing buildings are subjected to damage, an evolutionary approach is used to define which wall segments can be removed from the existing layout, according to a multi-objective function that targets minimizing the presence of damaged elements and maximizing the presence of undamaged elements in the design, in addition to minimizing the building’s eccentricity. The problem considers structural constraints based on the compressive capacity and minimum length of shear components. A computational strategy based on Genetic Algorithm is conceptualized as a tool for deriving potential layout solutions that adhere to structural requirements while facilitating the incorporation of new openings. By systematically evolving potential solutions over multiple generations, the algorithm navigates the design space to pinpoint areas within the original shear layout where material can be removed while respecting the given structural constraints. The methodology’s validity is demonstrated through a case study situated in a UNESCO World Heritage Site, providing evidence of the practical application and success of the genetic algorithm approach in real-world scenarios. The findings presented in this paper contribute to the convergence of structural engineering and architectural preservation, offering a systematic and efficient means of determining structural wall layouts for the adaptive reuse of historic masonry buildings. Integrating computational techniques into the adaptive reuse process holds transformative potential, establishing a robust framework for sustainable urban development that honors and enhances the historical fabric of our built environment.

Optimization of Thickness of YSZ Coating on an Aluminium Piston in I.C. Engines Using a Genetic Algorithm Approach

Optimization of Thickness of YSZ Coating on an Aluminium Piston in I.C. Engines Using a Genetic Algorithm Approach