Genetic Algorithm Research Articles

Comparative fitting methodology of cure kinetics models based on differential scanning calorimetry

The optimization of composite cure cycles is critical in materials engineering, requiring a deep understanding of the material’s cure kinetics. This study employs Differential Scanning Calorimetry (DSC) coupled with advanced fitting algorithms to determine the most accurate cure kinetics models for optimizing time-temperature profiles. Given the wide variability in model selection and fitting approaches in existing literature, our research systematically compares several cure kinetics models and their fitting methodologies to identify the most effective strategies. We highlight the advantages and limitations of each approach, with a particular focus on the robustness of the Levenberg–Marquardt (LM) algorithm compared to the Trust-Region-Reflective (TRR) algorithm. Given the existence of multiple local minima in the objective function, a global approach (genetic algorithm) is employed. Our findings reveal that, although the TRR and LM algorithms are faster, they are less accurate than the genetic algorithm. Moreover, the Kamal-Sourour model shows the highest prediction accuracy among the tested models. This comparative analysis provides a systematic approach and crucial insights into selecting the optimal cure kinetic model and corresponding parameters, based on experimental data, allowing for enhancing the efficiency and accuracy of composite curing processes.

Enhancing workflow efficiency with a modified Firefly Algorithm for hybrid cloud edge environments

Efficient scheduling of scientific workflows in hybrid cloud-edge environments is crucial for optimizing resource utilization and minimizing completion time. In this study, we evaluate various scheduling algorithms, emphasizing the Modified Firefly Optimization Algorithm (ModFOA) and comparing it with established methods such as Ant Colony Optimization (ACO), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO). We investigate key performance metrics, including makespan, resource utilization, and energy consumption, across both cloud and edge configurations. Scientific workflows often involve complex tasks with dependencies, which can challenge traditional scheduling algorithms. While existing methods show promise, they may not fully address the unique demands of hybrid cloud-edge environments, potentially leading to suboptimal outcomes. Our proposed ModFOA integrates cloud and edge computing resources, offering an effective solution for scheduling workflows in these hybrid environments. Through comparative analysis, ModFOA demonstrates improved performance in reducing makespan and completion times, while maintaining competitive resource utilization and energy efficiency. This study highlights the importance of incorporating cloud-edge integration in scheduling algorithms and showcases ModFOA’s potential to enhance workflow efficiency and resource management across hybrid environments. Future research should focus on refining ModFOA’s parameters and validating its effectiveness in practical hybrid cloud-edge scenarios.

Machine Learning and Genetic Algorithms in the Microfluidic Design of Highly Efficient Micromixers

Machine Learning and Genetic Algorithms in the Microfluidic Design of Highly Efficient Micromixers

OpenDock: a pytorch-based open-source framework for protein-ligand docking and modelling.

Molecular docking is an invaluable computational tool with broad applications in computer-aided drug design and enzyme engineering. However, current molecular docking tools are typically implemented in languages such as C++ for calculation speed, which lack flexibility and user-friendliness for further development. Moreover, validating the effectiveness of external scoring functions for molecular docking and screening within these frameworks is challenging, and implementing more efficient sampling strategies is not straightforward. To address these limitations, we have developed an open-source molecular docking framework, OpenDock, based on Python and PyTorch. This framework supports the integration of multiple scoring functions; some can be utilized during molecular docking and pose optimization, while others can be used for post-processing scoring. In terms of sampling, the current version of this framework supports simulated annealing and Monte Carlo optimization. Additionally, it can be extended to include methods such as genetic algorithms and particle swarm optimization for sampling docking poses and protein side chain orientations. Distance constraints are also implemented to enable covalent docking, restricted docking or distance map constraints guided pose sampling. Overall, this framework serves as a valuable tool in drug design and enzyme engineering, offering significant flexibility for most protein-ligand modelling tasks. OpenDock is publicly available at: https://github.com/guyuehuo/opendock.

A Novel Artificial General Intelligence Security Evaluation Scheme Based on an Analytic Hierarchy Process Model with a Generic Algorithm

The rapid development of Artificial General Intelligence (AGI) in recent years has provided many new opportunities and challenges for human social production. However, recent evaluation methods have some problems with regard to consistency, subjectivity and comprehensiveness. In order to solve the above problems, in this paper, we propose an Artificial General Intelligence Security Evaluation scheme (AGISE), which is based on analytic hierarchy process (AHP) technology with a genetic algorithm, to comprehensively evaluate the AGI security based on multiple security risk styles and complex indicators. Firstly, our AGISE combines AHP technology with a genetic algorithm to realize reliable, consistent and objective evaluation for AGI security. Secondly, in our AGISE, we propose implementing more effective AGI security evaluation classification and indicator settings. Finally, we demonstrate the effectiveness of our AGISE through experiments.

Carbon price prediction in China based on ensemble empirical mode decomposition and machine learning algorithms.

The carbon emission trading market is crucial for reducing emissions, conserving energy, and enhancing the climate and environment. Studying carbon price forecasting can encourage China's involvement in international carbon financial instruments trading and promote the development of China's pilot carbon markets. This study employs ensemble empirical mode decomposition (EEDM) to convert the initial carbon price, which is a non-stationary signal, into several intrinsic mode functions and residual terms. Subsequently, hybrid machine learning methods are used to estimate the carbon price. For empirical analysis, the three main carbon markets with large trading volumes, Guangdong, Hubei, and Shenzhen, are selected from the eight pilot carbon markets. To achieve short-term carbon price prediction, a hybrid model combining a genetic algorithm (GA) and back propagation (BP) neural network is used. This model effectively addresses the issue of neural networks falling into local optimization. The results indicate that the hybrid algorithm is significantly superior to other algorithms for short-term prediction. Additionally, another hybrid model, combining the least squares support vector machine (LSSVM) and particle swarm optimization (PSO) algorithms, is employed to reduce forecast error while minimizing search parameters, which is not possible with traditional neural network models.

Logistics park charging station optimization using improved genetic algorithms

The one-time investment and later operation efficiency of the supporting charging station are critical for the length of the investment return cycle. In this article, to meet the charging demand of logistics parks as the precondition, through the minimum total cost (construction, operation, maintenance, and depreciation) as the objective function, the improved genetic algorithm (GA) is used to look for the optimal solution. The operations of the charging station planning of a logistics park in off-season and peak-season are simulated and analyzed. The results show that the model and calculation method proposed in this article can provide effective support for the planning decision of logistics park charging. This study can guide the improvement and management in charging station configuration.

Optimization study on circadian tunability and Rec. 2020 of RGB-LED-based displays considering the users with different ages

The reasonable design of spectral power distribution (SPD) of GaN-based light-emitting diode (LED) display becomes more and more important and popular. In this work, we perform an investigation on the circadian effects of three-primary (red R, green G, and blue B, also denoted as RGB) LED based self-luminous displays (such as emerging mini-LED or micro-LED based displays) considering the users with different ages. With the increment of peoples’ ages from 1 year to 100 years, the optical transmittance of humans’ eyes may decrease due to the aging of their body functions. Therefore, it is important and meaningful for performing a spectral optimization study on the circadian effects of light that is emitting from self-luminous LED-based displays for the users with different ages. Here, the melanopic efficacy of luminous radiation (MELR) is mainly used to evaluate the circadian effects of white light coming from RGB-LED displays. With the aid of multi-objective optimization genetic algorithm (MOGA), the maximum MELR and the minimum MELR value are separately obtained for the standard observers at the age of 32, while maintaining at a certain high color gamut of Rec. 2020 standard (i.e., 60 %, 70 %, 80 %, and 90 % Rec. 2020, respectively) for these LED-based displays. Then, an extrapolation of optimal SPD at the age of 32 is done to other ages. Another parameter that can be denoted as circadian stimulus (CS) is also calculated and discussed for different ages. It is believable that this work is helpful for guiding the design and fabrication of future RGB-LED-based displays in consideration of the users with different ages, meeting large requirements of human-centric lighting (HCL) in the future.

Economical process design of reactive-extractive distillation combining variable pressure and heat integration for separating a ternary azeotropic mixture

Research and development of environmentally friendly processes that can generate significant profits has always been a focus of attention in pharmaceutical and chemical fields. In this study, a novel reactive-extractive pressure-swing distillation (REPSD) process was designed to separate tetrahydrofuran (THF)-methanol-water (TMW) ternary azeotropic mixtures. Ethylene oxide (EO) is reacted with water to form ethylene glycol (EG) in a reactive distillation (RD) column, which removes the water from the mixture. Then, the THF-methanol (TM) mixture is separated by extractive distillation (ED) and pressure-swing distillation (PSD). A multi-objective genetic algorithm (MUOGA) with total annual cost (TAC) and total capital cost (TCC) as objective functions, along with heat integration techniques were used to optimise the designed REPSD process. Optimal operating conditions to achieve maximum economic benefits were obtained for the process. Subsequently, the environmental benefits of the process were evaluated. The results showed that the proposed REPSD with heat integration can significantly reduce the TAC and CO2 emissions by 29.1 and 26.9 %, respectively, compared to existing optimal heat integration extractive distillation (HIED) processes. This study provides a new perspective on the separation and purification of ternary azeotropic mixtures.

Investigation of the Electronic Structure in GaAs/AlxGa1‐xAs Quantum Dots with Four Electrons

AbstractIn this paper, a detailed analysis of the electronic structure of four‐electron quantum dots is performed with finite confinement potential by a modified variational optimization approach based mainly on the quantum genetic algorithm and the Hartree‐Fock‐Roothaan method. For the ground and higher excited configurations, our analysis covers a range of parameters like the average energies of ground and excited states, singlet and triplet state energies, orbital energies, and two‐electron Coulomb and exchange interaction energies. One‐electron kinetic energy, the Coulomb potential energy between electrons and impurity, the confinement potential energy for the electrons, and the probability of finding an electron inside or outside the quantum well are also studied. The results demonstrate that both spatial confinement and the height of the potential barrier have a pronounced effect on all energies in the strong and intermediate confinement regions, but this influence weakens significantly in large dot radii. The most substantial difference between singlet and triplet energies occurs in the 1s22s2p configurations, with this difference decreasing in higher configurations. Significant increases in the 1s and 2s orbital energies are observed at the dot radii where the 2p, 3d, and 4f electrons from the outermost orbit begin to penetrate the well.

AI-Driven Optimization of Roll Forming Parameters for Defect-Free Manufacturing in Advanced High-Strength Steels

This paper presents an innovative and novel AI-driven approach to optimizing the roll forming process for advanced high-strength steels (AHSS), which are prone to defects like wrinkling, springback, and cracking due to their high strength and low formability. Traditional trial-and-error methods for parameter selection are time-consuming and inefficient. The proposed framework integrates machine learning models, such as artificial neural networks (ANN), support vector machines (SVM), and random forests, with genetic algorithms for multi-objective optimization. The novelty of this paper lies in the real-time monitoring and adjustment capabilities, allowing for dynamic process control that adapts to variations in material properties. This approach not only predicts and reduces defects by 30%, but also decreases parameter selection time by 50%, significantly improving production efficiency. A case study on an AHSS automotive component demonstrates the effectiveness of this system in achieving defect-free manufacturing, reducing equipment costs, and enhancing throughput. The AI-based methodology represents a paradigm shift in roll forming, offering a more flexible, scalable, and cost-efficient solution for modern manufacturing. Future work will explore integration with finite element analysis (FEA) and reinforcement learning for further improvements in process control.

Enhanced Carbon/Oxygen Ratio Logging Interpretation Methods and Applications in Offshore Oilfields

As the development of most domestic and international oilfields progresses, many fields have entered a mature phase characterized by high water cut and high recovery, with water cut levels often exceeding 90%. Carbon/oxygen ratio logging has proven to be an indispensable tool for distinguishing oil layers from water layers in complex environments, especially where salinity is low, unknown, or highly variable. This logging method has become one of the most effective techniques for determining residual oil saturation in cased wells, providing critical insights into the oil–water interface. In this study, we evaluate two key interpretation models for carbon/oxygen ratio logging: the fan chart method and the ratio chart method. We optimize the interpretation parameters in the ratio chart model using an improved genetic algorithm, which significantly enhances interpretation precision. The optimized parameters enable a more seamless integration of logging results with reservoir and conventional logging data, reducing the influence of lithological variations and physical property differences on the measurements. This research establishes a robust theoretical foundation for enhancing the interpretation accuracy of carbon/oxygen ratio logging, which is crucial for effectively identifying water-flooded layers. These advancements provide vital technical support for monitoring oil–water dynamics, optimizing reservoir management, and improving production efficiency in oilfield development.

A novel niching genetic algorithm with heterosis for edge server placement

A novel niching genetic algorithm with heterosis for edge server placement

Genetic algorithm driven optimization of a guided mode resonator coupled to an infrared nanocrystal film

Genetic algorithm driven optimization of a guided mode resonator coupled to an infrared nanocrystal film

Combining Genetic Algorithm with Local Search Method in Solving Optimization Problems

This research is focused on evolutionary algorithms, with genetic and memetic algorithms discussed in more detail. A graph theory problem related to finding a minimal Hamiltonian cycle in a complete undirected graph (Travelling Salesman Problem—TSP) is considered. The implementations of two approximate algorithms for solving this problem, genetic and memetic, are presented. The main objective of this study is to determine the influence of the local search method versus the influence of the genetic crossover operator on the quality of the solutions generated by the memetic algorithm for the same input data. The results show that when the number of possible Hamiltonian cycles in a graph is increased, the memetic algorithm finds better solutions. The execution time of both algorithms is comparable. Also, the number of solutions that mutated during the execution of the genetic algorithm exceeds 50% of the total number of all solutions generated by the crossover operator. In the memetic algorithm, the number of solutions that mutate does not exceed 10% of the total number of all solutions generated by the crossover operator, summed with those of the local search method.

Optimized hybrid osprey with PSO control for improved VSC-HVDC-wind power integration

Innovative power systems include offshore wind farms (OWFs) to generate electricity from renewable sources. So, reliable control systems are also necessary to make sure these power systems run smoothly. The purpose of this study is to combine the Osprey Optimization with Particle Swarm Optimization (OOPSO) to fine-tune the gains of Proportional-Integral (PI) controllers within the investigated wind energy conversion system (WECS) to enhance its performance. The study confirms the effectiveness of using the Hybrid OOPSO algorithm to optimize PI controllers in a WECS. The WECS includes two voltage source converter (VSC) stations at each end of a high voltage direct current (HVDC) transmission system. The transmission system links OWFs to the grid. The research demonstrates that the Hybrid OOPSO algorithm is superior to the genetic algorithm (GA) and PSO by enhancing setup stability and allowing fast voltage regaining following different fault cases. The introduced method performs more remarkably than traditional methods such as GA and PSO. The results of this study have shown that the proposed OOPSO enhances the overshoot in onshore voltage transient response under a symmetrical fault condition by 26% over PSO, 24% over GA, and 16% over OOA. The study is conducted using MATLAB/Simulink. The study underscores the importance of advanced optimization methods to guarantee offshore wind energy systems' efficient and dependable functioning.

The Parameter Optimization of Support Vector Machine with Genetic Algorithm in Risk Early Warning Models

Machine Learning algorithms are widely used by lenders in risk early warning models. With Machine Learning, the risk levels of individual and corporate customers are determined at the account and customer level. Lenders want to manage risk by evaluating the payment performance of customer or account with the help of Machine Learning algorithms. Banks, which have an important place among lenders, develop risk early warning models with the help of learning algorithms using customer information. In the development process of risk early warning models, while banks generally use customer information and credit bureau information for the individual segment, they use financial, non-financial and behaviour-based information for the corporate segment. In this study, it is planned to develop a risk early model for customers in corporate service segment. For the customers of corporate service segment, Balance Sheet and Income Statement items were used and the financial ratios were calculated for risk early warning models. In the development of risk early warning models, Mutual Information method was used as a novel feature selection approach and Support Vector Machine method (linear function, radial basis function and sigmoid function) was used as a supervised learning approach. By changing the neighbourhood metric (k), important patterns were discovered with the Mutual Information method in feature selection process. The optimal C and gamma parameters for Support Vector Machine models have been tried to be determined with the Genetic Algorithm, which is among the Meta-Heuristic algorithms. In order to find the optimal metrics in this study, the metric values for all parameters of the SVM model (function specific) have been kept quite wide. In this dataset of corporate service customers, the small neighbourhood metric has been found to have a significant impact on model learning and performance.

Response control of chimneys using tuned mass damper, multi-tuned mass damper and tuned mass inerter system

Growing industry and shifting environmental regulations have made chimneys taller and more flexible, thus making them susceptible to wind forces. Consequently, it becomes necessary to analyze the chimney for undesired wind force causing vortex induced vibration (VIV) and to enhance their performance by the implementation of appropriate countermeasures. In this study, three passive control devices, namely, tuned mass inerter system (TMIS), tuned mass damper (TMD) and multi-tuned mass damper (MTMD), are used to control the VIV of high-rise chimneys. The TMIS comprises a mass element, spring, damper, and inerter subsystem. The MTMD is distributed over the top one-third height of the chimney. The three control devices control the response of the chimneys under random vortex induced forces, and their relative performances are evaluated. The modal spectral analysis and Genetic Algorithm are employed to establish a method for optimizing the damper-structure system for VIV control. The response analysis duly considers the effect of the nonlinear aero-elastic effect. The novelty of the paper lies in the application of TMIS for chimney subjected to vortex induced vibration and comparing its results with other traditional dampers (TMD and MTMD) for the three chimneys of height 120 m, 210 m, and 360 m of varying diameter, taken as illustrative examples. In addition, the study examines the impact of different mass ratios and the mass of the inerter on chimneys. For the three chimneys considered, it is observed that the performance of the TMD is best among the three control techniques adopted. For the tallest chimney (360 m), all three control devices display an optimum mass ratio for which the response reduction is maximum. The performance of the TMIS improves with the increased height of the chimney.

Volatile Constituents of Cymbopogon citratus (DC.) Stapf Grown in Greenhouse in Serbia: Chemical Analysis and Chemometrics

The present study investigated the volatile constituents of Cymbopogon citratus (lemongrass) grown in a greenhouse environment in Serbia, marking the first commercial cultivation of the plant for essential oil production in the region. The essential oils and hydrolates obtained through steam distillation were analyzed via gas chromatography–mass spectrometry (GC-MS), and the resulting chemical data were further processed using chemometric methods. This study applied quantitative structure retention relationship (QSRR) analysis, employing molecular descriptors (MDs) and artificial neural networks (ANNs) to predict the retention indices (RIs) of the compounds. A genetic algorithm (GA) was used to select the most relevant MDs for this predictive modeling. A total of 29 compounds were annotated in the essential oils, with geranial and neral being the dominant components, while 37 compounds were detected in the hydrolates. The ANN models effectively predicted the RIs of both essential oils and hydrolates, demonstrating high statistical accuracy and low prediction errors. This research offers valuable insights into the chemical profile of lemongrass cultivated in temperate conditions and advances QSRR modeling for essential oil analysis.

Grade Classification of Camellia Seed Oil Based on Hyperspectral Imaging Technology.

To achieve the rapid grade classification of camellia seed oil, hyperspectral imaging technology was used to acquire hyperspectral images of three distinct grades of camellia seed oil. The spectral and image information collected by the hyperspectral imaging technology was preprocessed by different methods. The characteristic wavelength selection in this study included the continuous projections algorithm (SPA) and competitive adaptive reweighted sampling (CARS), and the gray-level co-occurrence matrix (GLCM) algorithm was used to extract the texture features of camellia seed oil at the characteristic wavelength. Combined with genetic algorithm (GA) and support vector machine algorithm (SVM), different grade classification models for camellia seed oil were developed using full wavelengths (GA-SVM), characteristic wavelengths (CARS-GA-SVM), and fusing spectral and image features (CARS-GLCM-GA-SVM). The results show that the CARS-GLCM-GA-SVM model, which combined spectral and image information, had the best classification effect, and the accuracy of the calibration set and prediction set of the CARS-GLCM-GA-SVM model were 98.30% and 96.61%, respectively. Compared with the CARS-GA-SVM model, the accuracy of the calibration set and prediction set were improved by 10.75% and 12.04%, respectively. Compared with the GA-SVM model, the accuracy of the calibration set and prediction set were improved by 18.28% and 18.15%, respectively. The research showed that hyperspectral imaging technology can rapidly classify camellia seed oil grades.