GA Algorithm Research Articles

Optimize the Synthesis Error in an Eight‐Bar Peaucellier–Lipkin Mechanism Using an Objective Function Maximization Approach and Application to Load Lifting

ABSTRACTThis work deals with optimizing the synthesis error in an eight‐bar Peaucellier—Lipkin mechanism, for its dimensional synthesis and applications in load‐lifting machines. A new method for the formulation of the problem of maximizing the objective function is proposed and makes it possible to obtain from the PSO algorithm a minimum synthesis error emin = 9.07E−06 mm for the generation of the straight trajectory when the search interval for the lengths of the bars is [1 mm, 15 mm] and a minimum error emin = 1.47E−04 mm when the search interval is [1000 mm, 15,000 mm]. For 10 simulations in Case 1 the average convergence time is tm = 55 s with the largest iteration at 10 (for t = 159 s); for 100 simulations in Case 2, the tm = 229 s with the largest iteration at 136 (for t = 2294 s). The minimum error of Case 1 is compared with the results of authors in the literature on the generation of the right trajectory because the search space is approximately equal. In the literature, emin = 0.648358 mm with the GA‐DE algorithm in 2010, emin = 2.3667E−005 mm with the MKH algorithm in 2016, emin = 0.027145 mm with the SAP‐TLBO algorithm in 2017, emin = 3.7E−4 with the GA algorithm in 2019. This new method brings a plus, because even when the search space is very large, the algorithm converges quickly and it allows the study to be extended to the generation of circular trajectories by just modifying the ratio between the frame bar and the crank bar. The practical implications of achieving an error as low as 9.07E−06 mm are the design of high‐precision industrial machines with reduced vibration, noise, and premature wear of joints. The results of the post‐design FEM analysis show that for a 1.4571 steel (X6CrNiMoTi17‐12‐2) with a thickness of 50 mm and a joint with a radius of 500 mm, the mechanical device obtained can support a load of 1500 kg.

TQ Evaluation of Higher Vocational English Sustainable Education Based on GA Algorithm and BP Algorithm

TQ Evaluation of Higher Vocational English Sustainable Education Based on GA Algorithm and BP Algorithm

AI-powered Smart Grids: Energy Optimization

This paper aims at explaining how AI can augment smart grids toward improved energy management. Mainly, the research is concerned with using artificial intelligence together in order to optimize energy distribution, minimize the cost of running organizations and finally establish means to harness sustainable energy resources. Four AI algorithms are utilized in this study: During the smart grid, load forecasting, fault detection, and energy optimization, ANN, SVM, GA, and RL approaches can be used. Based on the experimental findings, it is shown that the ANN achieved a 92% forecast precision; on the other hand, the utility of both the SVM and GA algorithms in energy optimization ranged from 18–23%. The RL algorithm returned the best result and cut wastage by 32% and enhanced load balance efficiency. These results demonstrate that AI strategy performs better than the conventional energy management systems, particularly for accommodating renewable generation and dynamic grid loads. By optimising the utilisation of the grid, and utilising artificial intelligence, energy losses are kept to an absolute minimum, thus supplementing sustainable efforts. The study establishes that smart grid using AI technology is the innovative way of enhancing energy management and thus sustainable smart infrastructures. In future work, issues of data security and scalability will be targeted in order to enhance the application of AI in smart grids.

A hybrid PSO and GA algorithm with rescheduling for task offloading in device–edge–cloud collaborative computing

A hybrid PSO and GA algorithm with rescheduling for task offloading in device–edge–cloud collaborative computing

A single and multiobjective robust optimization of a microgrid in distribution network considering uncertainty risk

In this paper, single and multi-objective robust optimization of a microgrid (MG) including photovoltaic (PV) and wind turbine (WT) sources with battery storage has been performed in a radial 33-bus distribution network considering uncertainty risk for minimizing the cost of energy losses, cost of electricity purchase from the post as well as power purchase from the MG. The problem is implemented in two approaches without uncertainty (deterministic) and with uncertainty (robust) via a flow direct algorithm (FDA). In the deterministic approach, the variables such as the site and the optimal size of the equipments in the short-term study horizon of 24 h, as well as the total cost, have been determined that the results of the MG deterministic optimization in the network indicate the reduction of network losses with the total cost minimization. The superior capability of the recommended deterministic approach based on the FDA is also confirmed in comparison with GA and PSO algorithms. In addition, the MG optimization problem with the robust approach with the information gap decision theory (IGDT) with risk-averse strategy is implemented to achieve the maximum radius of uncertainty (MRU) of renewable production and also network demand in three single and multi-objective cases. Based on the proposed robust approach, the level of system robustness has been determined in the condition of the worst uncertainty scenario against forecasting errors by considering different uncertainty budgets. It is also determined which uncertain parameter is more sensitive to the changes of the uncertainty budget. The findings demonstrated that in the multi-objective robust optimization, the constraints of the problem are not satisfied for budgets more than 40%, and in for sample the system uncertainty budget of 5%, there is a 27.51% decrease in resource production and a 0.87% increase in network load. The 40% uncertainty budget of the system is robust with a 16.80% decrease in resource generation and a 19.10% increase in network load. Therefore, one of the benefits of multi-objective optimization in comparison with single-objective optimization is balancing the sensitivity of uncertain parameters.

Research and Evaluation of a New Structural Damage Identification Method Based on a Refined Genetic Algorithm

In order to solve the problem of structural damage location and degree identification, the weighted mean of vectors algorithm (INFO), a high-performance optimization algorithm, was first introduced to identify structural damage. By comparison with the refined genetic algorithm (RGA), the accuracy and advantages of INFO are analyzed and evaluated. An objective function is constructed by combining the dynamic response transfer ratio without modal analysis. The INFO and RGA algorithms are used to optimize the objective function for damage identification. The simulation results verify the effectiveness of the three damage identification methods. The results show that the identification effect of the INFO algorithm can reach nearly 100% without noise influence, and the anti-noise ability is the strongest. Among the three algorithms, the damage identification accuracy of the INFO algorithm is the highest, followed by the RGA algorithm and the GA algorithm.

A new analytical technique for obtaining the optimal sizing, location, and scheduling of BESS in a grid-connected microgrid with multiple cases of heavy DG penetration

This paper aims to provide an optimal location, power, and energy rating for a battery energy storage system (BESS) in a grid-connected microgrid. The microgrid is pre-installed with heavy renewable distributed generations like solar and wind power plants. The paper suggests a straightforward optimal operating schedule for BESS, considering real-time data on solar irradiance, wind speed, load profile, and electricity tariff obtained from the US Energy Information Administration. The proposed method analytically identifies the optimal size and location of the storage system using the modified Q-PQV load flow technique. The method also proposes incorporating seasonal variations of the real-time data to obtain the optimal BESS size. A detailed cost-benefit analysis is exhibited to validate the economic feasibility. The methodological superiority is established by comparing the proposed algorithm with other soft computing techniques like PSO, GA, and JAYA algorithms. The novel technique is executed on 33-bus and 136-bus test systems with multiple cases of solar and wind-distributed generations. The proposed method provided a significant reduction in annual energy loss with a definite improvement in the voltage profile and overall profit of the system.

Comparative Sensitivity Analysis of GA, PSO, and PSO-GA Algorithms for Carbon Environment Development and Sustainability

<p style="text-align:justify;"><span style="font-family:"Times New Roman",serif;" lang="EN-US">To effectively address the CO<sub>2</sub> emissions in the tourism place, in this study focuses a Sanshan scenic spot for precise prediction. The main factors contributing CO<sub>2</sub> emission as, accommodation, transportation and catering services which intends to improve the need of carbon emission forecasting methods. To address this issue, we proposed an innovatively integrating the optimization algorithms Genetic Algorithm and Particle Swarm Optimization for accurately predicting the CO<sub>2</sub> emissions. Initially, this system taken input from the CO2 emissions occurred from the year of 2010 to 2020. The optimization model contains the input variables, initializing populations, evaluating fitness values, and iterating until convergence to found best prediction model. The experimental results of the proposed PSO-GA system attains 9.86 highest sensitivity showcases best performance in predicting CO<sub>2 </sub>emissions, as evidenced by its superior Adjusted Rand Index value of 1 after 160 iterations. The predicted and measured values of CO<sub>2</sub> emissions using this algorithm remains significant and advanced carbon environment development at scenic spots. The PSO-GA was used to calculate the carbon emissions of three mountainous scenic spots over the next five years, and the medium and high carbon levels should change to a medium carbon development stage by 2024.</span></p>

Enhancing Vibration Control in Smart Beams: A Comparative Study of Piezoelectric-Based Controllers Under Fluid-Induced Vibrations

Smart structures with integrated piezoelectric elements play a pivotal role in mitigating vibrations across diverse applications, by safeguarding against structural damage and performance degradation. This study delves into the active control of vibrations in beams, particularly in fluid-immersed environments where hydrodynamic forces induce complex beam oscillations. The dynamic behavior of geometrically nonlinear, simply supported beams integrated with piezoelectric elements was examined. The position of the piezoelectric patch was optimized through the GA algorithm to minimize the settling time. The optimal location for the piezoelectric patch was determined in the middle of the beam. The investigation encompasses harmonic excitation forces spanning frequencies from 10[Formula: see text]Hz to 200[Formula: see text]Hz. Our study evaluates the system’s response under two critical conditions: one with the presence of external hydrodynamic forces and the other without. To achieve effective vibration control, we employ both Proportional-Derivative (PD) and Sliding Mode Controllers (SMC), optimizing their parameters for minimum settling time as an objective function. The results underscore the remarkable superiority of the SMC, reducing settling times by an impressive 50% when compared to the PD controller. Additionally, the SMC demonstrates the ability to significantly reduce control efforts, as evidenced by actuator voltage. In the context of forced vibrations, the SMC maintains its superior performance, especially at lower frequencies. However, at higher frequencies, the emergence of chattering affects the SMC’s performance, although it remains more effective than the PD controller in low-frequency scenarios. This study sheds light on the effectiveness of piezoelectric-based control strategies for smart beams, offering valuable insights into their performance under various vibration conditions. These findings hold significant promise for practical applications where vibration control is critical.

WITHDRAWN: A Review of Optimization Methods for Production and Injection Well Placement in Hydrocarbon Reservoirs

Well placement optimization is a crucial issue in the oil and gas industry, aiming to enhance efficiency and reduce costs. In this study, we investigate research conducted on optimizing the placement of production and injection wells using optimization algorithms. This study was carried out in order to observe how to use optimization algorithms, their relative advantages and disadvantages, as well as the use of input parameters that help in the optimal location of production and injection wells using machine learning. In this regard, general optimization methods used in the articles are presented. Then, effective parameters, used algorithms, and library science metrics are introduced, and finally, previous studies are reviewed, as well as discussion and conclusions. Analyzes show that PSO and GA methods are the most popular and efficient methods among researchers. Research results indicate that PSO and GA algorithms can achieve high efficiency in optimizing well placements in the oil and gas industry. These improvements can lead to increased oil production, cost reduction, and enhanced resource efficiency. This article describes the principles and methods of using these algorithms for optimizing the placement of wells and presents an evaluation of the practical results.

Short term road network Macroscopic Fundamental Diagram parameters and traffic state prediction based on LSTM

Macroscopic Fundamental Diagram (MFD) is widely used in traffic state evaluation due to its description of the macro level of urban road network. This study focuses on the discrimination and short-term prediction of macro traffic states in urban road networks, using MFD combined with FCM clustering for state partitioning to characterize different macro states of the road network. To predict the MFD state, this paper builds two LSTMs to perform short-term predictions on two important parameters in MFD: road network weighted flow qw and road network weighted density kw. The parameters of the first three statistical intervals and the predicted time period are used as inputs to output the MFD parameters for the predicted time period. To ensure that the two LSTM structures and hyper-parameters settings can achieve the best prediction performance for MFD, the parameter optimization process of both should be included in the same search framework for hyper-parameters search. Therefore, this paper uses GA algorithm combined with multi-objective particle swarm optimization algorithm as the solving algorithm, with the accuracy of solving two MFD parameters and the accuracy of MFD point positioning as the hyper-parameters solving objectives. The study was validated using actual road network data from Hong Kong, and the results showed that the method proposed in this paper has an MRE prediction error of less than 7.8% for the two parameters of MFD, and can predict the future temporal trend of the two parameters, demonstrating the feasibility of MFD related predictions. The model's prediction of the overall shape and change trajectory trend of MFD is consistent with reality, and some test sets in MFD state prediction show high accuracy, the overall accuracy is 81.45%. To verify the effectiveness of the multi-objective search algorithm, typical LSTM models and RNN models were used for comparison. The experiment proved that the model used in this study performed better in error control and state prediction. This study explores and practices a short-term prediction method for road network MFD parameters, MFD status, and their changing trends. Provided path reference for urban road network prediction, traffic control status, and MFD related research.

Intelligence computational analysis of letrozole solubility in supercritical solvent via machine learning models

Supercritical fluids (SCFs) can be used to prepare drugs nanoparticles with improved solubility. SCFs have shown superior advantages in pharmaceutical industry as an environmentally friendly alternative to toxic/harmful organic solvents. They possess gas-like transport characteristics and liquid-like solvation power for solutes. Evaluation of chemotherapeutic drugs’ solubility in supercritical carbon dioxide (SCCO2) has been recently an attractive subject for developing this method in pharmaceutical sector. To reach this purpose, the utilization of accurate models is of great necessity to estimate experimental-based solubility data. In this paper, the authors tried to employ machine learning (ML) approaches to estimate the solubility of Letrozole (LET) drug as chemotherapeutic agent and correlate its values in wide ranges of temperature and pressure. To do this, PAR (Passive Aggressive Regression), RF (Random Forest), and RBF-SVM are the models used (Support Vector Machine with RBF kernel). These models optimized in terms of their hyper-parameters using GA algorithm. The optimized PAR, RF, RBF-SVM models obtained coefficients of determination (R-squared) of 0.8277, 0.9534, and 0.9947. Also, the MSE error rate of the models are 0.1342, 0.0305, and 0.0045, in the same order. The final result of the evaluations shows the optimized RBF-SVM model as the most appropriate model in this research. The model exhibits a maximum prediction error of 0.1289.

Competitive Game Model and Evolutionary Strategy Analysis of Green Power and Thermal Power Generation

In the context of achieving carbon peak and carbon neutrality goals, the power industry has become a key and challenging place to promote the green and low-carbon transformation of the economy and society. We selected green power generation enterprises and thermal power generation enterprises in the power industry as the research objects and applied relevant theories such as game theory and low-carbon economy theory to analyze the low-carbon transformation path of electricity under the “dual carbon” goal. We quantitatively analyzed the competition and cooperation relationship between green power and thermal power. Based on mutual benefit preferences, a competitive game model was constructed, and the optimal competitive equilibrium electricity quantity and price of both parties were discussed in different scenarios. The master–slave game problem was transformed into a double-layer game model and solved using the GA algorithm. We draw the following conclusions: (1) When green power enterprises and thermal power enterprises compete to achieve equilibrium, a high degree of altruistic willingness is a necessary condition for both parties to maximize their own utility. (2) A high-level mutually beneficial relationship is a key factor in effectively improving overall profits. (3) In the long-term mutually beneficial competitive relationship between green power and thermal power, by quantitatively adjusting the internal and external factors that affect system evolution, the quantitative adjustment of the feasible domain boundaries of the evolution of the mutually beneficial competitive relationship can be achieved, thereby influencing the existing transformation of the competitive relationship to evolve toward the desired direction.

Global Path Planning for Articulated Steering Tractor Based on Multi-Objective Hybrid Algorithm.

With the development of smart agriculture, autopilot technology is being used more and more widely in agriculture. Because most of the current global path planning only considers the shortest path, it is difficult to meet the articulated steering tractor operation needs in the orchard environment and address other issues, so this paper proposes a hybrid algorithm of an improved bidirectional search A* algorithm and improved differential evolution genetic algorithm(AGADE). First, the integrated priority function and search method of the traditional A* algorithm are improved by adding weight influence to the integrated priority, and the search method is changed to a bidirectional search. Second, the genetic algorithm fitness function and search strategy are improved; the fitness function is set as the path tree row center offset factor; the smoothing factor and safety coefficient are set; and the search strategy adopts differential evolution for cross mutation. Finally, the shortest path obtained by the improved bidirectional search A* algorithm is used as the initial population of an improved differential evolution genetic algorithm, optimized iteratively, and the optimal path is obtained by adding kinematic constraints through a cubic B-spline curve smoothing path. The convergence of the AGADE hybrid algorithm and GA algorithm on four different maps, path length, and trajectory curve are compared and analyzed through simulation tests. The convergence speed of the AGADE hybrid algorithm on four different complexity maps is improved by 92.8%, 64.5%, 50.0%, and 71.2% respectively. The path length is slightly increased compared with the GA algorithm, but the path trajectory curve is located in the center of the tree row, with fewer turns, and it meets the articulated steering tractor operation needs in the orchard environment, proving that the improved hybrid algorithm is effective.

Improving Autonomous Underwater Vehicle Navigation: Hybrid Swarm Intelligence for Dynamic Marine Environment Path-finding

Underwater research and monitoring operations rely significantly on Autonomous Underwater Vehicles (AUVs) for scientific investigations, resource management, and monitoring, and underwater infrastructure is provided maintenance levels amid other applications. Efficient navigation and preventative methods are only a couple of the numerous challenges that Path-Finding (PF) in rapidly changing and sophisticated Underwater Environments (UE) requires overcoming. Dynamic environments and real-time improvements are problems for traditional models. In order to provide superior solutions for navigating uncertain UE, this work suggests a hybrid optimization technique that combines Ant Colony Optimization (ACO) for local path selection with Particle Swarm Optimization (PSO) for global path scheduling. Runtime efficiency, accuracy, and distance focused on decrease are three metrics that demonstrate how the PSO-ACO hybrid method outperforms conventional algorithms, proving its significance for improving AUV navigation. The improvement of AUV functions in fields such as underwater research, along with others, is supported by the current research, which further assists with the invention of Autonomous Underwater Navigation Systems (AUNS). The PSO+ACO hybrid method is superior to the PSO, ACO, and GA algorithms in pathfinding with a 6.43-second execution time and 93.5% accuracy—the ACO model completed in 12.53 seconds, superior to the proposed system.

Numerical Simulation and ANN Prediction of Crack Problems within Corrosion Defects.

Buried pipelines are widely used, so it is necessary to analyze and study their fracture characteristics. The locations of corrosion defects on the pipe are more susceptible to fracture under the influence of internal pressure generated during material transportation. In the open literature, a large number of studies have been conducted on the failure pressure or residual strength of corroded pipelines. On this basis, this study conducts a fracture analysis on buried pipelines with corrosion areas under seismic loads. The extended finite element method was used to model and analyze the buried pipeline under seismic load, and it was found that the stress value at the crack tip was maximum when the circumferential angle of the crack was near 5° in the corrosion area. The changes in the stress field at the crack tip in the corrosion zone of the pipeline under different loads were compared. Based on the BP algorithm, a neural network model that can predict the stress field at the pipe crack tip is established. The neural network is trained using numerical model data, and a prediction model with a prediction error of less than 10% is constructed. The crack tip characteristics were further studied using the BP neural network model, and it was determined that the tip stress fluctuation range is between 450 MPa and 500 MPa. The neural network model is optimized based on the GA algorithm, which solves the problem of convergence difficulties and improves the prediction accuracy. According to the prediction results, it is found that when the internal pressure increases, the corrosion depth will significantly affect the crack tip stress field. The maximum error of the optimized neural network is 5.32%. The calculation data of the optimized neural network model were compared with the calculation data of other models, and it was determined that GA-BPNN has better adaptability in this research problem.

3D mask based lung function monitoring system using machine learning for early identification of lung disorders

AbstractEarly identification of illness can aid in lowering the death rate related to lung illnesses. Asthma, Chronic Obstructive Pulmonary Disease (COPD), and bronchiectasis are all chronic respiratory illnesses that cause irritation and oedema of the airway due to increased mucus discharge. Monitoring the asthmatic patient's physiological state is vital to avoiding dangerous circumstances. This study offers a regular lung function monitoring system that employs Machine Learning (ML) approach to aids in the prompt detection of symptoms of illness and the prevention of significant epidemics of the lung condition. A collection of sensors are coupled to the microcontroller in a 3D mask created using 3D printing technology. When a person wearing a face mask breathes in and out, the sensor values are instantly retrieved. The sensor data is sent to the cloud via a Wi‐Fi module for additional evaluation, and categorisation is performed using genetic algorithms, Support Vector Machine (SVM), and Principal Component Analysis (PCA). The GA, SWM, and PCA algorithms identify lung sickness using data from sensors obtained from the 3D masks through the web interface. There were 250 participants in total, comprising persons from all ages, smoker and those who do not smoke as well as asthmatics. The classifiers are trained utilising a set of pretrained values obtained from freely accessible datasets. Furthermore, patients are alerted when physiological indicators deviate from normal and when favourable atmospheric circumstances change.

Household health and medical care consumption decisions considering intertemporal uncertainty

To assist residents in exploring the optimal household health and medical care consumption decision-making strategies in an intertemporal uncertain environment, a consumption decision model based on a three-dimensional (3D) path planning PSO-GA-ACO was constructed. The results showed significant differences in the average optimal paths for household health and medical care consumption decisions among residents with different consumption preferences during the three different periods of the Covid-19 pandemic. During the Covid-19 period, the gap in household health and medical care consumption narrowed among residents with different consumption preferences, especially between steady and impulsive consumers. When comparing the post-Covid-19 period with the pandemic period, the monthly health and medical care consumption amounts decreased for both conservative and impulsive consumers, while it continued to rise for steady consumers. The 3D path planning PSO-GA-ACO algorithm improved the speed and accuracy of searching for optimal solutions and better described the decision-making behavior of household health and medical consumption compared to the traditional 3D path planning algorithms PSO, GA, and ACO. This study not only provides optimal household health and medical care consumption decision-making strategies for residents facing complex and uncertain environments but also offers scientific evidence for healthcare manufacturers and suppliers to take effective production and supply actions.

Expectation Parameters in the Poisson Mixture Regression Model for Latent Class by Applying Genetic Algorithm and Maximization Algorithm

In a Poisson mixture regression model for latent class, observations come from different sub-sources or classes, and the observed data are assumed to be generated by a specific (finite) mixture of unobserved or latent classes. The problem lies in the optimal assignment of observations to their respective classes. This requires sophisticated methods for estimating the parameters in the model. Usually, the model parameters are estimated by the conventional EM algorithm. The research aims to compare the EM algorithm and the genetic algorithm GA. Using simulation, the two algorithms were compared based on the MSE criterion, with different sample sizes (n = 50, 90, 120) and three scenarios (S1, S2, S3) for default values of the parameters. The results showed the superiority of the GA genetic algorithm over the EM algorithm, as the GA genetic algorithm gave the lowest MSE values. Paper type: Research paper

Intelligent prediction model of a polymer fracture grouting effect based on a genetic algorithm-optimized back propagation neural network

Polymer grouting can effectively improve the stability of surrounding rock fractures. However, in practical construction, it is difficult to judge the degree of coupling between the slurry and the rock, and the effective grouting range after grouting. Therefore, early prediction of the effect of grouting on the surrounding rock is crucial. In this paper, a new artificial intelligence method is proposed to predict the polymer fracture grouting effect. The genetic algorithm optimized back propagation neural network (GA-BP) is employed to construct an intelligent prediction model. To acquire a substantial dataset for constructing the model, an easily assembled/disassembled test apparatus for polymer fracture grouting is designed. The maximum coupling degree of the fractures and slurry diffusion distance are chosen as the evaluation metrics for the grouting effectiveness. The influences of the fracture characteristic parameters and grouting volume on the grouting effect are investigated. Furthermore, a comprehensive analysis is conducted on the spatiotemporal diffusion characteristics and slurry-rock coupling mechanism of polymer grouting. Compared to traditional BP neural networks, and three other machine learning algorithms (decision trees, random forests and gradient boosting decision trees), the GA-BP model outperforms them in terms of R2 (coefficient of determination), MSE (mean squared error), MBE (mean bias error), MAE (mean absolute error) and RMSE (root mean squared error) in both the test and training sets. The GA algorithm significantly improves the accuracy and robustness of the prediction model. The optimized model demonstrates significant accuracy in predicting grouting results and assessing efficiency, providing a practical reference for grouting construction.