Genetic Algorithm Method Research Articles

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.

An Innovative Energy Storage System Based on Phase Change Material and Solar Energy Integrated With an Air Handling Unit to Produce Heating and Cooling

ABSTRACTThis study investigates the potential of using phase change material (PCM) in a building using an air handling unit (AHU) assisted by solar energy. To further enhance the system, an energy storage system (ESS) can be considered. Implementing ESS would allow the captured solar energy to be stored efficiently, ensuring a continuous and reliable power source for cooling or heating the air, even during non‐sunlight hours. The air‐conditioned zone is considered to be 220 m2 in which 30 people work for 12 h a day. An energy analysis is conducted to evaluate the performance of the proposed system in terms of energy and exergy loss. The genetic algorithm (GA) method is used to optimize the system. The results indicate that the system, including PCM and solar system, can reduce energy consumption by up to 8.9% compared to conventional AHUs in the hottest month of the year (July). The results indicated that employing PCM leads to a decrease of 124 kWh in heat loss during January and 229 kWh in heat gain during July. Taking into account the beneficial impacts of both PCM and the solar system, the yearly assessment demonstrates a 2.69% reduction in power demand, equating to an energy saving of 679 kWh. Furthermore, PCM in the proposed system can be used in integration with AHUs based on renewable energy systems to store renewable energy for buildings.

Decomposition based multi-objective evolutionary algorithm for energy-saving design of homestay buildings

To improve the prediction accuracy of energy-saving design for homestay buildings, a multi-objective optimization model is studied. A model of multi-objective optimization algorithm for energy efficiency design of home stay buildings based on decomposition multi-objective evolutionary algorithm is proposed. Decomposition based multi-objective evolutionary algorithm is selected. To select the preliminary algorithm for achieving energy-saving design of homestay buildings, it divides the objectives into algorithm determination and model construction and uses multi-objective optimization algorithms to solve the proposed optimization model. The validation results show that the minimum discomfort time calculated using the non-dominated sorting genetic algorithm is 555.30 and the energy consumption is 7.68, while the minimum discomfort time calculated using the non-dominated sorting genetic algorithm method is 896 and the energy consumption is 8.92. With alternative model, the speed of multi-objective Evolutionary algorithm is the fastest, reaching 6105.44 seconds, which is 68.80% lower than the proposed method. With the help of substitutes, the computational speed of the multi-objective particle swarm optimization algorithm has been greatly improved. Its computational speed has reached 1217.231 seconds, while the fastest multi-objective particle swarm optimization algorithm among the four comparison methods is only 3868.591 seconds. Although the individual improvement is not significant, the overall optimization is still considerable and has strategic foresight in the decision-making plan of decision-makers.

Dynamic parameter identification of modular robot manipulators based on hybrid optimization strategy: genetic algorithm and least squares method

Dynamic parameter identification of modular robot manipulators based on hybrid optimization strategy: genetic algorithm and least squares method

Using genetic algorithm for optimisation of cracking time during the breaking of concrete bloc with expansive cement

In this paper, statistical, optimization methods like multiple parameter linear regression and genetic algorithm were used to establish a mathematical model of the demolition time (DT). The performance of the model was evaluated using the statistical indicators. The quality of the model was statistically evaluated, with a significant value of the correlation coefficient of 0.99081376, and low errors. Regarding the optimization of the demolition time by the genetic algorithms method, the results obtained are conclusive with a demolition time of unreinforced concrete of (17 h) which is lower than the demolition time obtained experimentally (36 h).

Optimization of Weibull Distribution Parameters with Application to Short-Term Risk Assessment and Strategic Investment Decision-Making

Accurate parameter estimation is fundamental in financial modeling, especially in investment analysis, where the Modified Internal Rate of Return (MIRR) plays a key role in evaluating investment performance. This study aims to enhance risk and return predictions in Sharia-compliant property investments by exploring the efficacy of various optimization techniques for estimating Weibull distribution parameters within the MIRR framework. To achieve this, we employed a comparative analysis of optimization methods, including Simulated Annealing (SA), Differential Evolution (DE), Genetic Algorithm (GA), and traditional Numerical Methods (NM). Performance was assessed through metrics such as Root Mean Squared Error (RMSE), Akaike Information Criterion (AIC), R-squared (R2) values, and Kolmogorov-Smirnov (KS) statistics. The results reveal that metaheuristic algorithms (SA, DE, GA) significantly outperform traditional numerical methods in terms of parameter estimation accuracy. Specifically, SA achieved the lowest RMSE of 0.042, with a Weibull shape parameter estimate of 1.254 and variance of 0.004, followed closely by DE with an RMSE of 0.048, and GA with 0.046. In contrast, NM exhibited a higher RMSE of 0.067, with a shape parameter estimate of 1.310 and a variance of 0.006. The AIC values for metaheuristic methods ranged from 14.25 to 14.68, compared to 15.12 for NM, and R2 values for metaheuristic methods ranged from 0.932 to 0.945, compared to 0.910 for NM. KS statistics further underscored the superior model fit of metaheuristics, with SA showing the lowest KS value of 0.045. The study underscores the critical role of metaheuristic optimization in improving the accuracy of parameter estimation based on MIRR models. This enhancement provides more reliable risk assessments and returns predictions, offering valuable insights for informed investment decision-making and contributing to optimized financial outcomes in the property sector.

A hybrid intrusion detection approach based on message queuing telemetry transport (MQTT) protocol in industrial internet of things

AbstractThe number of attacks against Industrial Internet of Things (IIoT) devices has increased over the past years, particularly on widely used communication protocols like Message Queuing Telemetry Transfer (MQTT). The fast increase in IIoT applications brings both critical challenges and technical gaps in cybersecurity. On the other hand, traditional cyber‐attack detection approaches scrap to address and support the run‐time responsibilities of IIoT environments. This study presents a hybrid Genetic Algorithm and Random Forest (GA_RF) method for detecting cyber‐attacks in Industrial Control Machines (ICS) that use MQTT protocol in the IIoT environment. This architecture integrates ICS with edge devices and cloud servers, using a GA_RF algorithm to detect anomalies in data collected by sensors. Normal data is processed locally and then sent to the cloud for storage and return, ensuring continuous monitoring and security. Also, the MQTT‐IOT‐IDS2020 dataset as a real test case was applied for prediction of the proposed GA_RF method with compare to some other powerful machine and deep learning models. The experimental results show that the proposed GA_RF method has an optimum accuracy of 99.87%–100% for detecting cyber‐attacks. This hybrid algorithm also achieved 0–0.0015 in Mean Absolute Error (MAE) and 100% in Precision, Recall, and F‐score factors. This result led to the proposed architecture, which connects the ICS to a server while running GA_RF on the IIoT environment. In conclusion, this study indicates the effectiveness of GA_RF and aims to improve security by using the MQTT protocol in IIoT.

Design of electrostatic lenses through genetic algorithm and particle swarm optimisation methods integrated with differential algebra

Genetic algorithm (GA) and particle swarm optimisation (PSO) techniques have been integrated with the differential algebra (DA) method in charged particle optics to optimise an Einzel lens. The DA method is a robust and efficient tool for the calculation of aberration coefficients of electrostatic lenses, which makes use of nonstandard analysis for ray tracing a particle as it is subjected to the field generated by a lens. In this study, initial populations of lenses with random geometrical configurations are generated. These initial populations are then subjected to GA and PSO algorithms to alter the geometry of each lens for a set number of iterations. The lens performance is evaluated by calculating the spot size using the aberrations coefficients up to third-order generated by the DA method. Moreover, a focusing column comprising two lenses and a Wien filter was optimised using GA method.

A new approach to instantaneous power based control of DFIG using active disturbance rejection control

This paper presents the design and control of a wind energy converter system that uses a doubly fed induction generator. It introduces a model that incorporates instantaneous active and reactive power as state variables to represent both the stator and rotor sides of the doubly fed induction generator. Subsequently, a linear active disturbance rejection-based control regulator is proposed for the control loops. The parameters of the linear active disturbance rejection controllers (ADRC) are adjusted by two methods: the single parameter tuning approach and the genetic algorithm (GA) method. The performance of the suggested controller is ultimately compared with a tuned proportional-integral (PI) control regulator in the MATLAB environment. In order to examine the performance of the system, various test scenarios are taken into account. These scenarios include different percentages of load change in both sub-synchronous and super-synchronous modes of operation, faults with different fault periods are considered in both modes of operation. The conventional PI controller-based approach requires 35 control cycles to reach steady state under load change conditions and 50 control cycles under symmetrical fault conditions, regardless of whether the system is operating in sub synchronous or super synchronous mode. The single parameter-based tuned ADRC approach only requires 15 control cycles for load change and 20 control cycles for symmetrical fault scenarios. In contrast, for both the load change and symmetrical fault scenarios, the ADRC with GA-based parameter tuning reaches steady state in just 5 control cycles. Furthermore, a case involving wind speed variation is presented to demonstrate the maximum power point tracking capability of the proposed control structure. Similarly, for the robustness test of the control regulator, variations in resistance and inductance are introduced to observe their impact on the response of the control regulators. Finally, a real-time hardware in loop setup is presented to validate the control strategy during the load change in the sub-synchronous mode of operation of the doubly fed induction generator.

A genetic algorithm method for improving suboptimal sensor arrangements in coverage and connectivity problems

This paper is concerned with the solution of a Coverage and Connectivity Problem (CCP). The proposed method performs the placement of sensors forming a connected Wireless Sensor Network (WSN) to fully cover a Field of Interest (FoI). Full coverage is attained despite the presence of opaque obstacles that impair both sensing and communication. To this end, we leverage set operations involving polygons to tessellate the free space. Moreover, we propose the concept of CR-visibility to assess the total area coverage of a polygon by a sensor. The deployment of the minimal number of sensors to completely cover the FoI is cast into the form of an Integer Linear Program (ILP). Two different formulations of connectivity constraints are appended to the ILP. The first one is necessary and sufficient for connectivity, whereas the second alternative is only sufficient. While the number of inequalities in the former grows combinatorially with the number of sensors, the growth is linear in the latter, rendering it more computationally appealing for real-sized FoI. Lastly, we formulate an unconstrained version of the CCP, which is solved by a Genetic Algorithm (GA) with integer variables. We present a small simulation scenario for initial illustration of the proposed method, and a larger, real-life scenario based on the map of an actual urban setting. The results obtained with the real-life scenario show that: (i) high-quality solutions can be obtained in short computation times by imposing the sufficient constraints for connectivity; (ii) the large number of inequalities associated to the necessary and sufficient constraints render the numerical solution impractical; (iii) by using random initialization, the GA solution of the unconstrained problem requires more sensors than the ILP solution with the sufficient connectivity constraint; (iv) including that ILP solution in the initial population of the GA enables it to find a sensor placement that requires fewer sensors.

Prediction models for retinopathy of prematurity occurrence based on artificial neural network

IntroductionEarly prediction and timely treatment are essential for minimizing the risk of visual loss or blindness of retinopathy of prematurity, emphasizing the importance of ROP screening in clinical routine.ObjectiveTo establish predictive models for ROP occurrence based on the risk factors using artificial neural network.MethodsA cohort of 591 infants was recruited in this retrospective study. The association between ROP and perinatal factors was analyzed by univariate analysis and multivariable logistic regression. We developed predictive models for ROP screening using back propagation neural network, which was further optimized by applying genetic algorithm method. To assess the predictive performance of the models, the areas under the curve, sensitivity, specificity, negative predictive value, positive predictive value and accuracy were used to show the performances of the prediction models.ResultsROP of any stage was found in 193 (32.7%) infants. Twelve risk factors of ROP were selected. Based on these factors, predictive models were built using BP neural network and genetic algorithm-back propagation (GA-BP) neural network. The areas under the curve for prediction models were 0.857, and 0.908 in test, respectively.ConclusionsWe developed predictive models for ROP using artificial neural network. GA-BP neural network exhibited superior predictive ability for ROP when dealing with its non-linear clinical data.

Optimisation of process-induced residual stresses in composite laminates by different genetic algorithm and finite element simulation coupling methods

To address the residual stress induced during the cure of fibre reinforced thermoset polymer composites, two different approaches were suggested for coupling a non-dominated sorting genetic algorithm (NSGA-II) with finite element (FE) simulations based on a viscoelastic constitutive law. These two approaches were proposed with consideration of different ways of integrating NSGA-II and the FE model. In Approach A, NSGA-II was performed based on results from a series of simulations under various combinations of cure variables. Alternatively, Approach B employed NSGA-II to iteratively update and optimise the cure profile for subsequent simulations. Results indicated that both approaches achieved simultaneous reductions in cure time and macroscale residual stress, with Approach B showing further improvements due to the direct coupling between the NSGA-II and simulations. Specifically, the maximum residual stress and cure time optimised by Approach A were reduced by 5%–9% and 22%–50% respectively, while those obtained by Approach B were reduced by 7%–10% and 32%–49% respectively, compared to those based on the manufacturer recommended cure profile. The evolution of stress in composites based on optimised cure profiles from these two approaches was also elucidated. Additionally, microscale modelling further revealed a 3%–5% reduction in the average residual stress within a representative volume element (RVE) model was also shown, depending upon the approach adopted. Ultimately, by combining a NSGA-II and FE simulations, the optimisation of cure time and residual stress at the macroscale and cure time together with a reduction of microscale stress could be realised.

Optimization of reservoir release operation using genetic algorithm method

Genetic Algorithms (GA) are highly effective in optimizing hydropower release parameters, identifying global optima in complex systems, and adapting to varying circumstances, making them ideal for managing hydropower conditions. This study employs GA to optimize hydropower release at Wonorejo Reservoir, Indonesia, aiming to balance power generation with flood control, irrigation, and environmental sustainability. The application of GA improved operational efficiency, resulting in a 10.24 % increase in electricity production. Notably, during the rainy season, the improvement was 27.3 % higher than in the dry season, highlighting GA responsiveness to seasonal variations. These findings demonstrate GA potential as a valuable tool for reservoir management.

Collision-free trajectory planning for UAVs based on sequential convex programming

This paper proposes an optimization-based approach to solve the collision-free trajectory planning problem for unmanned aerial vehicles (UAVs) with nonlinear dynamical systems. The problem is modeled as a non-convex optimization problem, and then iteratively solved by sequential convex programming algorithm which approximates non-convex constraints by convexification methods. We linearize the dynamical equations of the UAV and penalize collision-free constraints with a hinge loss. Furthermore, a variable trust region method is employed to ensure the convergence and stability of the algorithm. We apply the algorithm to three-degree-of-freedom UAV case studies. Numerical results validate that our method can provide collision-free trajectories with a 27.19% reduction in total flight time and a 93.61% reduction in computation time on average compared with the genetic algorithm method.

Optimum Cutting Parameters for Carbon-Fiber-Reinforced Polymer Composites: A Synergistic Approach with Simulated Annealing and Genetic Algorithms in Drilling Processes

This paper presents a unique approach to generate a number of cutting knowledge blocks for the surface roughness analysis of the drilling process for carbon-fiber-reinforced polymer composite (CFRP) materials. The influence of drilling on the surface quality of woven CFRP materials was investigated experimentally. The CFRP material (0/90° fiber orientation) was drilled at different cutting parameters and the surface roughness of the hole was measured. A set of tests was carried out using carbide drills of 8 mm in diameter at 50, 70, and 90 m/min cutting speeds, 2, 3, and 4 flute numbers, and 0.2, 0.3, and 0.4 mm/rev feed rates. The Simulated Annealing (SA) and Genetic Algorithm (GA) methods were used for optimization. Based on the experimental findings and optimization techniques applied, optimal cutting parameters were derived, which were subsequently adjusted to enhance surface quality. Overall, the cutting parameters are carefully optimized to achieve good surface roughness quality in the drilling of CFRP.

Application of Genetic Algorithm and Analytical Method to Determine the Appropriate Locations and Capacities for Distributed Energy System

Application of Genetic Algorithm and Analytical Method to Determine the Appropriate Locations and Capacities for Distributed Energy System