Genetic Algorithm Method Research Articles

Investigating the impact of nano copper on the electrical conductivity of aluminum: A comprehensive study utilizing ANN, genetic algorithm, and fuzzy logic methods

Nanotechnology has emerged as a transformative field in material science, offering unprecedented opportunities to enhance the electrical properties of conventional materials through the incorporation of nano-sized particles. In this extensive study, we explore the effects of varying weight percentages (1%, 2%, and 3%) and lengths (30 nm, 60 nm, 150 nm, and 250 nm) of nano copper on the electrical conductivity of aluminum (Al) across different temperatures (20°C, 50°C, and 100°C). Additionally, we compare the predictive capabilities of Artificial Neural Networks (ANN), Genetic Algorithms (GA), and Fuzzy Logic (FL) methods in forecasting the electrical conductivity variations of Al based on these parameters.

Investigating the impact of nano copper on the electrical conductivity of aluminum: a comprehensive study utilizing genetic algorithms methods

Nanotechnology has emerged as a transformative field in material science, offering unprecedented opportunities to enhance the electrical properties of conventional materials through the incorporation of nano-sized particles. In this extensive study, we explore the effects of varying weight percentages (1%, 2%, and 3%) and lengths (30 nm, 60 nm, 150 nm, and 250 nm) of nano copper on the electrical conductivity of aluminum (Al) across different temperatures (20°C, 50°C, 80°C, and 100°C). Additionally, GA method and Numerical correction factor and constitutive equation using Zener –Holloman parameter were employed for modeling the rheological behavior of Al based on these parameters.

Adaptive metamodeling-based simulation optimisation

Simulation Optimisation is a powerful decision-making tool, but it can be challenging for complex, time-intensive models. This paper introduces Adaptive Metamodeling-based Simulation Optimisation (AMSO), a novel framework that enhances solution quality by integrating machine learning and metaheuristic techniques. AMSO combines Bagged-gradient Boosted Trees, Genetic Algorithms, Hyperparameter Optimisation, and Design of Experiments to efficiently explore promising solution areas. The study demonstrates AMSO’s application in two real-world scenarios: a resource allocation problem in a manufacturing digital twin model and a capacity expansion project at a mining plant. AMSO outperformed the Efficient Global Optimisation method, achieving solutions 8.1% and 9.7% better on average for the first and second case studies, respectively, with no significant increase in computational time. Additionally, AMSO matched the Genetic Algorithm method’s solution quality but reduced computational time by 83.6% and 90.6% in the first and second cases, respectively. AMSO is presented as a robust alternative for solving complex simulation models, complementing existing metamodeling-based methods and opening new research avenues with other machine learning models for faster, more accurate decision-making.

RNA-Seq analysis for breast cancer detection: a study on paired tissue samples using hybrid optimization and deep learning techniques

ProblemBreast cancer is a leading global health issue, contributing to high mortality rates among women. The challenge of early detection is exacerbated by the high dimensionality and complexity of gene expression data, which complicates the classification process.AimThis study aims to develop an advanced deep learning model that can accurately detect breast cancer using RNA-Seq gene expression data, while effectively addressing the challenges posed by the data’s high dimensionality and complexity.MethodsWe introduce a novel hybrid gene selection approach that combines the Harris Hawk Optimization (HHO) and Whale Optimization (WO) algorithms with deep learning to improve feature selection and classification accuracy. The model’s performance was compared to five conventional optimization algorithms integrated with deep learning: Genetic Algorithm (GA), Artificial Bee Colony (ABC), Cuckoo Search (CS), and Particle Swarm Optimization (PSO). RNA-Seq data was collected from 66 paired samples of normal and cancerous tissues from breast cancer patients at the Jawaharlal Nehru Cancer Hospital & Research Centre, Bhopal, India. Sequencing was performed by Biokart Genomics Lab, Bengaluru, India.ResultsThe proposed model achieved a mean classification accuracy of 99.0%, consistently outperforming the GA, ABC, CS, and PSO methods. The dataset comprised 55 female breast cancer patients, including both early and advanced stages, along with age-matched healthy controls.ConclusionOur findings demonstrate that the hybrid gene selection approach using HHO and WO, combined with deep learning, is a powerful and accurate tool for breast cancer detection. This approach shows promise for early detection and could facilitate personalized treatment strategies, ultimately improving patient outcomes.

Numerical thermal analysis and structural optimization of cascaded PCM heat sinks for thermal management of electronics

In the present study, an innovative cascaded phase change material (PCM) heat sink using organic PCMs was proposed for electronic thermal management. The design arranges multiple PCMs with progressively lower melting points along the heat transfer path, allowing active control of the melt front. A numerical model with 5 % accuracy was developed to evaluate thermal performance. Based on the validated model, the effects of PCM stage numbers, PCM volume fractions, fin types, and fin volume fractions were analyzed. The fin volume fractions were further optimized using a support vector machine (SVM) and genetic algorithm (GA) methods. Results showed that a three-stage PCM heat sink with pin fins and increasing configurations outperformed single-stage PCM heat sinks, enhancing heat transfer by up to 36.7 %. Additionally, the innovative cascaded PCM heat sink with a uniform 20 % fin volume fraction across the three stages achieved a 38.8 % improvement compared to single-stage configurations. When the fin volume fractions were set to 24.1 %, 17.6 %, and 13.0 % for the three stages, thermal performance of the innovative cascaded PCM heat sink was enhanced by up to 39.8 % compared to single-stage configurations. The findings provide valuable insights for the design and application of PCM-based thermal management systems, potentially leading to more efficient solutions for various engineering fields.

First-principle modeling of parallel-flow regenerative kilns and their optimization with genetic algorithm and gradient-based method

We present a one-dimensional first-principle model for parallel-flow regenerative kilns that accounts for the most important effects. These include the kinetics and thermal effects of the limestone decomposition as well as the heat transfer between the gaseous and solid phases. The model consists of two coupled equation systems for the upper and lower part of the kiln. The results of the model are validated qualitatively and are used to train an artificial neural network that predicts the conversion and the temperature in the crossover channel. The artificial neural network performs very well with values of the root mean squared error that are two to three orders of magnitudes lower than the range covered within the data. Finally, we use a genetic algorithm to optimize the feed mass flows such that the conversion and the fuel efficiency are improved in a Pareto-optimal manner. The results are compared to those of a gradient-based optimization method, which shows the usefulness and validity of the approach with the genetic algorithm.

Statement of Retraction: Application of particle swarm optimization, and genetic algorithm methods for maximizing the phase velocity in the multi-layer nanoscale system

Statement of Retraction: Application of particle swarm optimization, and genetic algorithm methods for maximizing the phase velocity in the multi-layer nanoscale system

Multi-UAV Cooperative Pursuit of a Fast-Moving Target UAV Based on the GM-TD3 Algorithm

Recently, developing multi-UAVs to cooperatively pursue a fast-moving target has become a research hotspot in the current world. Although deep reinforcement learning (DRL) has made a lot of achievements in the UAV pursuit game, there are still some problems such as high-dimensional parameter space, the ease of falling into local optimization, the long training time, and the low task success rate. To solve the above-mentioned issues, we propose an improved twin delayed deep deterministic policy gradient algorithm combining the genetic algorithm and maximum mean discrepancy method (GM-TD3) for multi-UAV cooperative pursuit of high-speed targets. Firstly, this paper combines GA-based evolutionary strategies with TD3 to generate action networks. Then, in order to avoid local optimization in the algorithm training process, the maximum mean difference (MMD) method is used to increase the diversity of the policy population in the updating process of the population parameters. Finally, by setting the sensitivity weights of the genetic memory buffer of UAV individuals, the mutation operator is improved to enhance the stability of the algorithm. In addition, this paper designs a hybrid reward function to accelerate the convergence speed of training. Through simulation experiments, we have verified that the training efficiency of the improved algorithm has been greatly improved, which can achieve faster convergence; the successful rate of the task has reached 95%, and further validated UAVs can better cooperate to complete the pursuit game task.

Deep learning and genetic algorithm driven accelerated design for frequency-multiplexed complex-amplitude coding meta-hologram

Frequency-multiplexed metasurfaces represent a significant innovation in breaking the functional limitations of traditional metasurfaces, showing immense potential in multi-channel communication. However, existing frequency-multiplexed metasurfaces primarily focus on pure phase and linear polarization modulation, neglecting the modulation for complex amplitude and circularly polarized waves. Additionally, crosstalk suppression between dual-frequency channels often requires meticulous tuning of the meta-atom structure. Therefore, manually designing a set of meta-atoms that satisfies both complex amplitude modulation and low crosstalk at dual frequencies is extremely challenging and time-consuming. Here, we utilize the method of deep learning and genetic algorithm to design a kind of meta-atom capable of bi-spectral 2-bit amplitude and arbitrary phase modulation, which greatly reduces the design difficulty and achieves excellent low-crosstalk performance. This method can be easily generalized to the design of other complex meta-atoms to improve the design efficiency. Furthermore, we propose a frequency-multiplexed complex-amplitude coding meta-hologram for modulating left-handed circularly polarized (LCP) waves. When illuminated with LCP light, it can reconstruct two distinct holographic images at two different frequencies in the near field with high quality. The independent modulation capability of the metasurface for multiple degrees of freedom of frequency, amplitude and phase gives it broad application prospects in multi-channel communication, data storage and perfect holography.

Feasibility of optimum energy use and cost analyses by applying artificial intelligence and genetic optimization methods in geothermal and solar energy-assisted multigeneration systems

This study evaluates the potential for optimizing energy utilization and cost analysis in geothermal and solar energy-supported multigeneration systems using artificial intelligence (AI) and genetic algorithm (GA) optimization methods. Integrating renewable energy sources aims to promote sustainable energy solutions focusing on efficiency and cost-effectiveness. A cogeneration system that leverages geothermal and solar energy for electricity generation and space cooling is analyzed. The model incorporates an organic Rankine cycle (ORC) for power generation and a geothermal-solar absorption cooling cycle for space cooling, developed using Artificial Neural Networks (ANN) and optimized through thermoeconomic methods. Thermodynamic and thermoeconomic analyses guide the optimization process using an ANN-based GA method. The system, with parameters such as a 130 °C geothermal source temperature, an 85 kg/s mass flow rate, and a 600 W/m2 monthly average solar radiation intensity in Afyonkarahisar, achieves overall energy and exergy efficiencies of 19.5 % and 43.5 %, respectively. Optimization via the GA method significantly improves performance, increasing net power output from 2240 kW to 2760 kW and cooling capacity from 2720 kW to 3061 kW. Additionally, the cost of electricity decreases by 12.1 %, from 0.0165 $/kWh to 0.0145 $/kWh, while the cooling cost is reduced by 41.9 %, from 0.0745 $/kWh to 0.0525 $/kWh.

Optimization Design of Bionic Leading-edge Protuberances on Horizontal Axis Wind Turbine Blades

Abstract The complex operating environments of horizontal axis wind turbines has made the phenomenon of blade flow separation more obvious. As a passive flow control method, the bionic humpback whale leading-edge protuberances have been shown to effectively enhance the performance of the wind turbine. This study employs a multi-objective genetic algorithm and Computational Fluid Dynamics method to analyse the impact of leading-edge protuberances on the aerodynamic performance and flow characteristics of the blades. The results indicate a significant improvement in blade performance after optimization in the wind turbine’s output power. The streamwise vortices generated by the bionic protuberances not only reduce the suction surface pressure, but also suppress the spanwise flow over the blade surface. The length of the flow separation Region is reduced from 0.410 to 0.368 radius. In the process of wind speed change, the tangential forces on the bionic blade exhibit a wave-like variation. The bionic protuberance alters the pressure coefficient of the position. Compared with the original blade, the pressure coefficient of the trough sections on both sides of the bionic blade protuberance is improved. In this study, the bionic protuberance applied to the leading-edge of horizontal-axis wind turbine blades is proposed for the first time, and holds practical value for guiding practical applications.

Data-driven multi-objective optimization of aerodynamics and aeroacoustics in dual Savonius wind turbines using large eddy simulation and machine learning

Savonius rotor is a popular form of vertical-axis wind turbine (VAWT) for small-scale and urban applications because of its straightforward design and self-starting ability. Dual VAWTs present challenges in terms of wake interactions and noise, particularly in urban areas. Optimizing these parameters is essential for future wind energy adoption. This research is the first to analyze how the interaction of wakes from adjacent rotors, combined with a deflector, affects both the aerodynamic performance and noise levels of dual Savonius rotors. Large Eddy Simulation is applied, as it effectively captures detailed turbulent wind flows and their interactions with wind turbines. A multi-objective optimization method combining Machine Learning and Computational Fluid Dynamics (CFD) is developed to optimize rotors for maximum power efficiency and minimum noise, considering their wake interactions with a unique deflector system. First, the influence of geometric parameters on aerodynamics and aeroacoustics characteristics of rotors is analyzed, and the database is generated using Design of Experiment approach. Next, the CFD model is replaced by Artificial Neural Network (ANN) model established for predicting rotor performances. A Multi-Objective Genetic Algorithm method is used to optimize aerodynamics and aeroacoustics characteristics of rotors. Finally, optimal design parameters are identified from the Pareto front using the technique for order of preference by similarity to ideal solution decision-making method. The ANN model demonstrated high accuracy with an RANN2 of 0.995 and 0.971 for the average power coefficient (CP) and overall sound pressure level (OSPL) predictions, respectively. Multi-objective optimization revealed the best configuration of the deflector with bleed jets, improving the average CP up to 57.5% and reducing OSPL to an almost 5.2% compared to the dual rotor case at TSR = 0.8.

Mploying Virial Coefficients for Optimal Solutions in Variational Calculations

In this study, virial coefficients for one and two-electron hydrogen and helium-like quantum dot structures confined in an infinite potential well were calculated. The virial coefficients were determined based on the dot radius using the Quantum Genetic Algorithm (QGA) method. Calculations were performed using Fernandez's expression; however, due to calculation errors in confined systems, this equivalent expression was found unsuitable as a direct stopping criterion. Instead, virial coefficients were calculated using the ⟨T⟩/⟨V⟩ relationship, and the results were plotted. The fitting function obtained for the virial coefficients is proposed as an effective cutoff criterion for electronic structure calculations of quantum dot systems.

Intelligent vehicle lateral control strategy research based on feedforward + predictive LQR algorithm with GA optimisation and PID compensation

Targeting the lateral motion control problem in the intelligent vehicle autopilot structural system, this paper proposes a feedforward + predictive LQR algorithm for lateral motion control based on Genetic Algorithm (GA) parameter optimisation and PID steering angle compensation. Firstly, based on the vehicle dynamics tracking error model, the intelligent vehicle LQR lateral motion controller as well as the feedforward controller are designed, and upon which the predictive controller is added to eliminate the system lag.Subsequently, exploiting the advantage that the PID algorithm is not model-based, a PID steering angle compensation controller that can directly control and correct the lateral error is designed. Second, a LQR controller based on path tracking deviation is designed by using the parameter rectification method of genetic algorithm (GA), which optimizes the control parameters of the lateral motion controller and improves the adaptivity of the control accuracy. Finally, Based on the Carsim-Simulink co-simulation platform, the simulation validation and analysis of double lane change (DLC) test and circular condition test (CCT) are carried out, and the results indicate that compared with the other two LQR controllers, the optimised controllers improved more than 50% in lateral error and heading error control, and the vehicle sideslip angle and vehicle yaw rate are in the range of −0.05° to 0.05° and − 0.15 rad/s to 0.10 rad/s, and it showed improved performance in tracking accuracy and satisfied vehicle stability constrains.

Genetic algorithm optimization of langevin thermostat and thermal properties of graphene-aluminum nanocomposites: a molecular dynamics

Abstract The thermal properties of a laminated structure of graphene-coated aluminum composite nanomaterial were investigated through non-equilibrium molecular dynamics (NEMD) simulations to address the problem of temperature deviation in the thermostat volume applied. This paper presents a new insight into the best values of timestep and Langevin thermostat damping parameters for each atom in the nanomaterial with different size configurations using the genetic algorithm (GA) method by considering the timestep and thermostat damping parameters for each atom type, as well as the thickness of the nanomaterial, the thermostat, buffer, and heat flow lengths. The initial population results indicate that the thermostat temperature deviation increases with higher thermostat damping coefficients and timestep. However, the deviation decreases significantly with increased heat flow and thermostat lengths. Variations in buffer length and aluminum thickness do not have a significant effect on temperature. The application of a GA for optimization leads to a decrease in thermostat temperature deviation. The optimized parameters resulted in better thermostat temperature deviations when analyzing the temperature, aluminum thickness, and both buffer and thermostat lengths. Additionally, the thermal conductivity of aluminum-graphene nanomaterial decreases with increasing temperature, buffer length, and aluminum thickness, but increases by up to 9.85% with increasing thermostat length.

GA and PSO Techniques Based Optimal Tuning of Power System Stabilizers in a PV Integrated Power Network

This paper involves the employment of Genetic Algorithm (GA) and Particle Swam Optimization (PSO) methods for the optimal tuning of gain and time constant parameters of power system stabilizers in a power network with integrated Photovoltaic (PV) energy conversion systems. To examine the critical modes of the PV integrated system and the impact of the PV integration on various modes of oscillations, eigenvalue analysis is carried out under various PV penetration levels. The investigations indicate that the rotor angle oscillations following a disturbance have less damping as the PV penetration level increases. To enhance the small signal stability, the excitation systems are provided with the signal from power system stabilizers (PSS). The gain and time constant parameters of the PSS are tuned utilizing the GA and PSO methods and compared with the conventional proportional-integral (PI) PSS. The preliminary investigations on the PV integrated standard IEEE power system reveal that the PSO and GA-based PSS are significantly better than the PI-PSS, and further, PSO-PSS is marginally more effective than the GA-PSS in damping the rotor angle oscillations.

Carbon-mechanic dual-control design decision model of prefabricated structural components

Prefabricated enclosure structures (PES) have become the key technology for low-carbon construction in the engineering field due to their low labor demand, fast construction speed, and low environmental pollution. In the context of low-carbon development, efficiently designing PES that not only possess high-strength but also demonstrate significant greenhouse gas (GHG) reduction benefits has become a critical challenge. The objective of this study is to address the challenges of automated design and scientific decision-making methods for PES components. First, the Carbon-mechanic dual-control assessment model (CDAM) is proposed, providing a novel evaluation dimension for the design of prefabricated components. Subsequently, based on the CDAM, an automated design decision-making process, and model for components are established using an improved genetic algorithm and parameterized method. Finally, under the constraints of actual engineering conditions, adaptive solutions were automatically generated for specific cases. These solutions were comprehensively evaluated and optimized using CDAM, resulting in design guidelines and optimal solutions for PES that balance environmental performance and load-bearing capacity. The case analysis results show that the optimal scheme based on the carbon-mechanic dual-control (CM) index is: the sectional dimension is 830 mm, the reinforcement ratio is 0.9 % and the cavity ratio is 45 %, which shows the ideal characteristics of low-carbon and high-strength. Compared with the single index decision scheme, this scheme achieve a maximum of 42 % GHG emissions reduction. The high comprehensive performance scheme is characterized by small sectional dimension, high reinforcement ratio, and high cavity ratio. Cavity ratio is a key parameter to achieve high performance design. The research findings provide an important theoretical basis and technical support for the low-carbon transformation in the engineering field.

Optimization design of unmanned mining truck path tracking controller based on road adhesion coefficient estimation

Abstract In response to the issues of inaccurate tracking precision caused by continuous turning and variable road adhesion coefficients on complex roads in mining areas for unmanned mining trucks, this paper adopts unscented Kalman filter (UKF) to estimate the road adhesion coefficient. Based on this, an adaptive preview feedforward linear quadratic regulator (LQR) control algorithm is designed using genetic algorithm, feedforward control, and linear quadratic optimal path tracking control methods. Firstly, a nine degree of freedom vehicle dynamics model and a two degree of freedom vehicle lateral dynamics model are established, and the Dugoff model to calculate tire forces is introduced; secondly, utilizing UKF to estimate the road adhesion coefficient, and leveraging active disturbance rejection control for rapid tracking of road curvature, an optimized design of the LQR controller is carried out. Then, the effectiveness of the designed controller was verified through Trucksim/Simulink joint simulation testing. Finally, the hardware in the loop test results showed that the designed controller had good tracking accuracy and strong adaptability in complex road and special working conditions in mining areas.

Bi-Level Optimal Configuration of Electric Thermal Storage Boilers in Thermal–Electrical Integrated Energy System

Electric thermal storage boilers (ETSBs) are important devices in enhancing the electric–thermal decoupling ability and spatiotemporal transfer of integrated energy system (IES), which is beneficial for improving system flexibility and energy utilization efficiency. In order to obtain more accurate and comprehensive results, a bi-level optimal model is proposed to study the site selection and capacity configuration of ETSB in IES based on the established mathematical model of ETSB. The objective of upper-level optimization of the model is obtaining the lowest energy supply cost when configuring the location and capacity of ETSB, while the lower-level model optimizes the operation scheduling with the goal of obtaining the lowest operational cost. The mixed-integer linear programming method and the genetic algorithm method are selected to obtain the optimal model. To illustrate the effectiveness and advantages of the proposed method, case studies are carried out. The optimal configuration scheme for an ETSB is obtained by comparing the lowest energy supply cost under different configuration parameters. Furthermore, the impact of an ETSB on the system is also analyzed based on the variations in energy balance, abandoned energy, and energy allocation before and after configuring the ETSB.

A Bayesian optimization-genetic algorithm-based approach for automatic parameter calibration of soil models: Application to clay and sand model

This paper develops a novel approach for automatic parameter calibration of soil constitutive models by combining Bayesian optimization (BO) and genetic algorithm (GA). The BO method enables locating the possible point over a large multi-parameter search domain quickly, by which the search domain is no longer required to be accurately pre-defined. Thereafter, the GA is employed to find the best solution within the BO-optimized search space. This calibration procedure can quickly give the best-matched model parameters for both sands and clays even with the same large search space. Taking a critical state-based soil model (Clay And Sand Model (CASM)) as an example, the superiority and capability of the proposed calibration method are examined by comparing experimental data with predicted results for both clays and sands. It is shown that the BO-GA-based approach can reduce errors by 46.4% for clays and 41.2% for sands compared with the conventional GA method and outperforms other traditional optimization algorithms. Eventually, the evolution process of BO and GA is visualized to better understand the principles of automatic parameter calibration, which is followed by model uncertainty analyses.