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Electrical power systems are the lifeblood of modern civilization, providing the essential energy infrastructure that powers our homes, industries, and technologies. As our world increasingly relies on electricity, and modern power systems incorporate renewable energy sources, the challenges have become more complex, necessitating advanced forecasting and optimization to ensure effective operation and sustainability. This review paper provides a comprehensive overview of electrical power systems and delves into the crucial roles that forecasting and optimization play in ensuring future sustainability. The paper examines various forecasting methodologies from traditional statistical approaches to advanced machine learning techniques, and it explores the challenges and importance of renewable energy forecasting. Additionally, the paper offers an in-depth look at various optimization problems in power systems including economic dispatch, unit commitment, optimal power flow, and network reconfiguration. Classical optimization methods and newer approaches such as meta-heuristic algorithms and artificial intelligence-based techniques are discussed. Furthermore, the review paper examines the integration of forecasting and optimization, demonstrating how accurate forecasts can enhance the effectiveness of optimization algorithms. This review serves as a reference for electrical engineers developing sophisticated forecasting and optimization techniques, leading to changing consumer behaviors, addressing environmental concerns, and ensuring a reliable, efficient, and sustainable energy future. [ABSTRACT FROM AUTHOR]

In this research paper, we propose a two-stage hybrid approach that uses machine learning techniques and meta-heuristic algorithms. The first step, known as data preparation, involves converting string values to numeric format and subsequently normalizing the data. To increase the performance beyond the limitations of traditional methods, we use population-based meta-heuristic algorithms, namely Atom Search Optimization (ASO) and Equilibrium Optimization (EO), for feature selection, aiming to achieve global optimization. The second step, called attack detection, focuses on distinguishing normal traffic from malicious traffic. To improve the performance of this step, we use K-means clustering and firefly algorithm (FA). In addition, an elitism method is randomly integrated. The resulting approach is called ASO-EO-FA-K-means. We evaluate the performance of our proposed method using two datasets, namely NSL-KDD, UNSW_NB15, and KDD_CUP99. To establish a benchmark, we compare our method with other approaches including Particles Swarm Optimization (PSO), Genetic, Grey Wolf Optimization (GWO), Ant colony optimization (ACO), Harris Hawk Optimization (HHO), NSGA-2, Multi-objective PSO, Multi-objective GWO, learning vector quantization (LVQ), XGBoost, particle swarm optimization based on C4.5 (PSO-C4.5) and genetic algorithm based on multilayer perceptron (GA-MLP)) we compare. The evaluation results show that the proposed method achieves the highest accuracy and the lowest error rate in three datasets NSL-KDD and UNSW_NB15 KDD-CUP99 with accuracy values of 0.998, 0.995 and 0.995, respectively. In addition, our method shows superior efficiency in terms of computation time. In general, our research shows the effectiveness of the ASO-EO-FA-K-means method in intrusion detection and provides better accuracy and efficiency compared to alternative approaches. In all three data sets, the results have shown that NSL-KDD data set with MSE 0.012, accuracy value 0.998 has obtained better results than other data sets. [ABSTRACT FROM AUTHOR]

Abrasive waterjet machining (AWJ) is characterized by significantly better efficiency and better precision for difficult-to-machine materials than conventional machining technologies. However, the larger number of control parameters characterizing this process needs optimization. The study compares the performance of three nature-inspired metaheuristic algorithms, ALO, GWO, and MFO for optimizing the abrasive water jet (AWJ) treatment. The Response Surface Methodology was used to determine the cost function. The study evaluates the convergence and computational cost of the algorithms to aid future developments in this field. The study aims to maximize the cutting thickness by predicting the optimal water-abrasive cutting parameters (nozzle diameter, abrasive concentration, feed speed). For all three algorithms, the maximum cutting depth was determined to be 87.47 mm, which differs only less than 3% from the actual value. The results highlight the potential of ant-lion optimization (ALO), grey wolf optimizer (GWO), and (MFO) moth-flame optimization algorithms for resolving optimization issues in AWJ machining. [ABSTRACT FROM AUTHOR]

Optimizing domestic energy use and increasing the efficiency of residential power supply chains depend much on home energy management (HEM) systems. This paper examines the utilization of advanced meta-heuristics, namely the Siberian Tiger Optimization (STO) and Sand Cat Swarm Optimization (SCSO) algorithms, for developing HEM systems. The paper presents an integrated STSC algorithm, enhanced by artificial intelligence, that monitors and optimizes household energy usage. To optimize electricity distribution, this algorithm seeks a compromise between reducing costs and lowering the peak-to-average proportion of power. After extensive simulations, the STSC algorithm outperforms previous meta-heuristics in Peak Average Ratio. It demonstrates the possibility of substantial cost reductions in residential settings, reaching up to 8.5%. This improves the overall efficiency of households' power supply chains. In addition to reducing costs, the STSC algorithm contributes to sustainability objectives by utilizing AI to minimize carbon emissions, including renewable energy sources, and facilitate adaptable demand solutions. This highlights its role in promoting sustainable supply chain practices in energy efficiency. The combined use of STO and SCSO algorithms in the STSC method is a new and innovative development in HEM systems. This study highlights the capacity of AI-driven technologies to effectively and environmentally optimize household energy consumption. [ABSTRACT FROM AUTHOR]

The classical methods for optimizing wireless power transfer (WPT) systems using mathematical equations or finite element methods can be time-consuming and may only sometimes yield optimal designs. In order to overcome this challenge, this paper introduces a novel approach integrating machine learning techniques with meta-heuristic methods to design and optimize a miniaturized, high-efficiency WPT receiving coil for biomedical applications. The objective is to achieve dimensions below 20 mm, a depth of 30 mm within the tissue, and a frequency of 13.56 MHz. Our approach leverages a neural network (NN) model to predict efficiency based on geometric coil parameters, eliminating the need for complex equations. The NN was trained on a dataset generated via finite element method simulations. We employ two meta-heuristic algorithms, the genetic algorithm and the coyote optimization method, to find optimal parameters that maximize efficiency. Our NN model demonstrates exceptional accuracy, exceeding 97%. Furthermore, the proposed WPT coil design approach enhances transfer efficiency by up to 76%, significantly reducing computation time compared to classical methods. Finally, we validate our results using finite element simulation with Ansys Maxwell 3D. [ABSTRACT FROM AUTHOR]

Meta-heuristic algorithms (MHAs) have gained popularity in recent years due to their successful results in solving a wide variety of scientific problems. They offer ease of use, fast implementation, and effective convergence toward the optimal solution. Although MHAs have been extensively tested in solving well-known mathematical benchmark problems with one or more dimensions as well as civil and mechanical engineering problems in their initial demonstrations, controller design problems are not typically considered. Furthermore, the literature lacks a standardized optimization process for controller design problems using MHAs. Due to variations in iteration numbers, population sizes, number of trials, objective functions, and insufficient analysis presented in research papers, it becomes challenging to compare and evaluate the controller design performance of MHAs in a successful and fair manner. This work aims to establish a standardized approach for evaluating the performance of MHAs in controller design by proposing a consistent function evaluation metric. To achieve this goal, we present the most comprehensive and comparative study of MHAs' performance in controller design conducted to date. In this paper, we utilize two commonly used objective functions in controller design: Zwe Lee Gaing and Integral Time Absolute Error. Additionally, we employ a total of twenty algorithms, consisting of ten classical algorithms and ten recently popular algorithms. We evaluate the performance of these algorithms on the "automatic voltage regulation" electric power system problem, which serves as a widely used benchmark for meta-heuristically optimized controllers. We consider three different controllers with three (PID), five (FOPID), and seven (FOPIDD) parameters. The performance results of the selected algorithms are thoroughly discussed, considering various analysis techniques such as box plot analysis, convergence curves, and transient response performances, as well as statistical tests like Wilcoxon and Friedman tests. As a result, symbiotic organisms search, teaching–learning based optimization, chaos game optimization, supply–demand based optimization, and jellyfish search algorithms generally emerge as the best-performing algorithms across all optimization processes for the three types of controllers. Researchers interested in conducting further analysis and comparing the improved algorithms can access all the models,parameters, and codes used in this study from the provided link (https://www.mathworks.com/matlabcentral/fileexchange/161336-fractional-order-controller-optimization-for-avr). [ABSTRACT FROM AUTHOR]

Massive MIMO technology is recognized as a key enabler for beyond 5G (B5G) and next-generation wireless networks. By utilizing large-scale antenna arrays at the base station (BS), it significantly improves both system capacity and energy efficiency. Despite these advantages, the deployment of a high number of antennas at the BS presents considerable challenges, particularly in the design of signal detectors that can operate with low computational complexity. While the minimum mean square error (MMSE) detector offers optimal performance in these large-scale systems, it suffers from the computational burden that makes its practical implementation challenging. To mitigate this, various iterative methods and their improved versions have been introduced. However, these iterative methods often converge slowly and are less accurate. To address these challenges, this study introduces an improved variant of traditional accelerated over-relaxation (AOR), called optimized AOR (OAOR). AOR is an over-relaxation method, and its performance is highly dependent on its relaxation parameters. To find the optimal parameters, we have developed an innovative approach that integrates a nature-inspired meta-heuristic algorithm known as Particle Swarm Optimization (PSO). Specifically, we introduce a novel variant of PSO that improves upon basic PSO by enhancing the cognitive coefficients to optimize the relaxation parameters for OAOR. These key modifications to the standard PSO improve its ability to explore various solutions efficiently and help to find the optimal parameters more quickly for signal detection. It facilitates the OAOR with faster convergence towards the optimal solution by reducing the error rate, resulting in high detection accuracy and simultaneously decreasing computational complexity from O (K 3) to O (K 2) making it suitable for modern wireless communication systems. We conduct extensive simulations across various configurations of massive MIMO systems. The results indicate that our proposed method achieves better performance compared to existing techniques. This improvement is particularly evident in terms of both computational complexity and error rate. [ABSTRACT FROM AUTHOR]

The redundancy allocation problem (RAP) is one of the most commonly used approaches for improving the reliability of systems. In previous studies, to simplify the problem model and mathematical solution methods, some real conditions were not emphasized in analysing the issue of RAP. But the present study, while considering these conditions, enables considering multiple goals, analysing repairable systems and examining the failure rate and repair rate appropriate to the type of components. Two goals of this purpose are maximizing the Mean Time To First Failure (MTTFF) and minimizing the total cost of the system, considering the weight and volume limitations and number of plugin components. To solve the problem model four Multi-objective meta-heuristic algorithms have been used: NSGA-II, MOPSO, PESA-II and MALO. In addition, the simulation method is used to estimate the first objective function of the problem. [ABSTRACT FROM AUTHOR]

The optimal solution to the wind farm layout optimization problem helps in maximizing the total energy output from the given wind farm. Meta-heuristic algorithms are one of the famous methods for achieving this objective. In this paper, we focus on developing an efficient meta-heuristic based on the grey wolf optimizer for solving the wind farm layout optimization problem. The proposed algorithm is called enhanced chaotic grey wolf optimizer and it is introduced after validating it on a well-known benchmark set of 23 numerical optimization problems. By confirming its efficiency through these benchmarks, it is utilized for wind farm layout optimization. The proposed algorithm is comprised of four search strategies including a modified GWO search mechanism, modified control parameter, chaotic search, and adaptive re-initialization of poor solutions during the search. Two case studies of the wind farm layout optimization problem are considered for numerical experiments. Results are analyzed and compared with other state-of-the-art algorithms. The comparison indicates the efficiency of the proposed algorithm for solving numerical and wind farm layout optimization problems. [ABSTRACT FROM AUTHOR]

Partitional clustering is a type of clustering that can organize the data into non-overlapping groups or clusters. This technique has diverse applications across the different various domains like image processing, pattern recognition, data mining, rule-based systems, customer segmentation, image segmentation, and anomaly detection, etc. Hence, this survey aims to identify the key concepts and approaches in partitional clustering. Further, it also highlights its widespread applicability including major advantages and challenges. Partitional clustering faces challenges like selecting the optimal number of clusters, local optima, sensitivity to initial centroids, etc. Therefore, this survey describes the clustering problems as partitional clustering, dynamic clustering, automatic clustering, and fuzzy clustering. The objective of this survey is to identify the meta-heuristic algorithms for the aforementioned clustering. Further, the meta-heuristic algorithms are also categorised into simple meta-heuristic algorithms, improved meta-heuristic algorithms, and hybrid meta-heuristic algorithms. Hence, this work also focuses on the adoption of new meta-heuristic algorithms, improving existing methods and novel techniques that enhance clustering performance and robustness, making partitional clustering a critical tool for data analysis and machine learning. This survey also highlights the different objective functions and benchmark datasets adopted for measuring the effectiveness of clustering algorithms. Before the literature survey, several research questions are formulated to ensure the effectiveness and efficiency of the survey such as what are the various meta-heuristic techniques available for clustering problems? How to handle automatic data clustering? What are the main reasons for hybridizing clustering algorithms? The survey identifies shortcomings associated with existing algorithms and clustering problems and highlights the active area of research in the clustering field to overcome these limitations and improve performance. [ABSTRACT FROM AUTHOR]

While the demand for electrical energy in the world increases daily, a large part of this demand is still provided by fossil fuels. However, the most significant contribution to solving the economic and environmental problems that arise is the spread of renewable energy production systems. Solar power generation systems are one of these renewable energy generation systems. In this study, cell and module parameters are modeled and estimated in different ways to obtain maximum energy from solar cells used in solar power generation systems. Cell and model vendors need to provide complete information to the end user. Therefore, the systems created turn into a nonlinear problem with many unknown parameters. In this study, single-diode model (SDM), double-diode model (DDM), and triple diode model (TDM) for photovoltaic (PV) cells as well as parameter estimations of four different PV modules produced by other vendors were performed for the first time with the dingo optimization algorithm (DOA). The mathematical model of PV module parameters is derived using open-circuit voltage (Voc), short-circuit current (Isc), and maximum power point values (Pmpp). The parameter values obtained by the algorithm aim to get the maximum power point curve for each model and module with minimum error. These values are compared with five traditional and five recent meta-heuristic algorithms, which have extreme positions in the literature. [ABSTRACT FROM AUTHOR]

In this paper, a permanent magnet synchronous machine (PMSM) with an auxiliary winding (AW) on the rotor is analyzed by two-dimensional approach. This PMSM with AW (AWPMSM) can be used in many applications such as propulsion system, aircraft and traction because it includes rotor flux control capability. First, the magnetic field in different parts of AWPMSM is calculated based on Maxwell equations. Then, as a consequence of the magnetic field, the torque components, including cogging, reluctance, electromagnetic and instantaneous torque are computed. Next, torque-speed characteristic has been investigated. This AWPMSM can be located in the flux weakening mode in two ways, first one is to attenuate the rotor field by changing the direction of the AW field and the other one is to adjust the armature current angle, both of them have been investigated. After that, the overload capability and temperature effects have been analyzed. Finally, using the meta-heuristic algorithms such as genetic algorithm, particle swarm optimization, differential evolution and teaching learn base optimization the dimensions of AWPMSM and the initial angle of the rotor are determined in such a way that the torque-to-volume ratio is maximized. The influences of the type of armature winding and the magnetization patterns have also been investigated. The results obtained by the two-dimensional method have been confirmed numerically. [ABSTRACT FROM AUTHOR]

Ionic liquids (ILs) have attracted considerable interest due to their unique properties and prospective uses in various industries. However, their potential toxicity, particularly regarding enzyme inhibition, has become a growing concern. In this study, a QSAR model was proposed to predict the enzyme inhibition toxicity of ILs. A dataset of diverse ILs with corresponding toxicity data against three enzymes was compiled. Molecular descriptors that capture the physicochemical, structural, and topological properties of the ILs were calculated. To optimize the selection of descriptors and develop a robust QSAR model, the chaotic spotted hyena optimization algorithm, a novel nature-inspired metaheuristic, was employed. The proposed algorithm efficiently searches for an optimal subset of descriptors and model parameters, enhancing the predictive performance and interpretability of the QSAR model. The developed model exhibits excellent predictive capability, with high classification accuracy and low computation time. Sensitivity analysis and molecular interpretation of the selected descriptors provide insights into the critical structural features influencing the toxicity of ILs. This study showcases the successful application of the chaotic spotted hyena optimization algorithm in QSAR modelling and contributes to a better understanding of the toxicity mechanisms of ILs, aiding in the design of safer alternatives for industrial applications. [ABSTRACT FROM AUTHOR]

Network alignment plays a pivotal role in fields such as network science, biology, and social network analysis by identifying common structures and relationships across different networks. The process is challenging due to the diversity of network structures and the necessity to align networks from various domains or periods. To address these challenges, nature-inspired meta-heuristic optimization algorithms have emerged as powerful tools. These algorithms, inspired by natural processes such as evolution, swarm behavior, and other biological phenomena, provide effective solutions for complex optimization problems. This paper offers a thorough examination of the application of these meta-heuristic algorithms to network alignment. It explores a wide range of nature-inspired algorithms, including genetic algorithms, particle swarm optimization, ant colony optimization, and simulated annealing. The review delves into the underlying principles of each algorithm, their practical applications, and their performance in network alignment tasks. By analyzing detailed discussions and practical examples, the paper highlights the strengths and limitations of various meta-heuristic algorithms. It assesses their effectiveness in aligning networks across different scenarios, providing valuable insights for researchers and practitioners. The findings emphasize the potential of these algorithms in overcoming the complexities of network alignment, offering guidance for employing these techniques effectively. The paper also explores future research directions, suggesting ways to advance the field by leveraging nature-inspired algorithms. As a comprehensive resource, it consolidates existing knowledge and enhances understanding, supporting the development of innovative solutions and improved strategies for network alignment. [ABSTRACT FROM AUTHOR]

Optimizing domestic energy use and increasing the efficiency of residential power supply chains depend much on home energy management (HEM) systems. This paper examines the utilization of advanced meta-heuristics, namely the Siberian Tiger Optimization (STO) and Sand Cat Swarm Optimization (SCSO) algorithms, for developing HEM systems. The paper presents an integrated STSC algorithm, enhanced by artificial intelligence, that monitors and optimizes household energy usage. To optimize electricity distribution, this algorithm seeks a compromise between reducing costs and lowering the peak-to-average proportion of power. After extensive simulations, the STSC algorithm outperforms previous meta-heuristics in Peak Average Ratio. It demonstrates the possibility of substantial cost reductions in residential settings, reaching up to 8.5%. This improves the overall efficiency of households’ power supply chains. In addition to reducing costs, the STSC algorithm contributes to sustainability objectives by utilizing AI to minimize carbon emissions, including renewable energy sources, and facilitate adaptable demand solutions. This highlights its role in promoting sustainable supply chain practices in energy efficiency. The combined use of STO and SCSO algorithms in the STSC method is a new and innovative development in HEM systems. This study highlights the capacity of AI-driven technologies to effectively and environmentally optimize household energy consumption.

Abstract In this paper, a permanent magnet synchronous machine (PMSM) with an auxiliary winding (AW) on the rotor is analyzed by two-dimensional approach. This PMSM with AW (AWPMSM) can be used in many applications such as propulsion system, aircraft and traction because it includes rotor flux control capability. First, the magnetic field in different parts of AWPMSM is calculated based on Maxwell equations. Then, as a consequence of the magnetic field, the torque components, including cogging, reluctance, electromagnetic and instantaneous torque are computed. Next, torque-speed characteristic has been investigated. This AWPMSM can be located in the flux weakening mode in two ways, first one is to attenuate the rotor field by changing the direction of the AW field and the other one is to adjust the armature current angle, both of them have been investigated. After that, the overload capability and temperature effects have been analyzed. Finally, using the meta-heuristic algorithms such as genetic algorithm, particle swarm optimization, differential evolution and teaching learn base optimization the dimensions of AWPMSM and the initial angle of the rotor are determined in such a way that the torque-to-volume ratio is maximized. The influences of the type of armature winding and the magnetization patterns have also been investigated. The results obtained by the two-dimensional method have been confirmed numerically.

Abstract In large-scale software development, the increasing complexity of software products poses a daunting challenge to maintaining software quality. Given this challenge, software fault prediction (SFP) is a critical endeavor for effective budgeting and refinement of the testing process. Quantitative insights into software quality gained through measurements are crucial in enabling accurate SFP. With the proliferation of software in various fields, ensuring software reliability throughout the software life cycle has become paramount. Anticipating software bugs, which have the potential to reduce software maintenance costs dramatically, is a key approach to improving software reliability. In this regard, using nature-inspired metaheuristic algorithms is promising because of their ability to predict future conditions and identify software anomalies. This study examines the potential of various meta-heuristic algorithms, particularly particle swarm optimization, genetic, ant colony optimization, cuckoo search, lion optimization, firefly, moth-flame, whale optimization, and artificial bee colony algorithms, in addressing the SFP challenge. The study outlines the challenging problems, compares approaches based on fundamental variables, and offers suggestions for future studies, providing a comprehensive and systematic analysis of these algorithms in the context of SFP.

In this paper, we present an improved imperialist competitive algorithm for solving an inverse form of the Huxley equation, which is a nonlinear partial differential equation. To show the effectiveness of our proposed algorithm, we conduct a comparative analysis with the original imperialist competitive algorithm and a genetic algorithm. The improvement suggested in this study makes the original imperialist competitive algorithm a more powerful method for function approximation. The numerical results show that the improved imperialist competitive algorithm is an efficient algorithm for determining the unknown boundary conditions of the Huxley equation and solving the inverse form of nonlinear partial differential equations.

This paper proposes a novel approach for intrusion detection using a metaheuristic-based feature selection method combined with convolutional neural networks (CNNs). The feature selection method employs a decision tree and a metaheuristic algorithm to select the most important features from different datasets. The selected features are then feed into CNNs, including ResNet50, VGG16, and EfficientNet, to improve the accuracy of intrusion detection. Experimental results on several benchmark datasets show that the proposed method can be promising in terms of different criteria. The final results prove that EfficientNet and ResNet50 perform much better than VGG16. When EfficientNet and ResNet50 algorithms are applied to NSL-KDD, DEFCON and CDX datasets, the best accuracy rates are 96.2% and 81.3% correspondingly. In addition, while EfficientNet has the highest rate of 98.6% according to the specificity criterion, ResNet50 stands out with a recall rate of 95.1% and a rate of 95.2% for F1score. [ABSTRACT FROM AUTHOR]

The optimization challenge known as the optimal reactive power dispatch (ORPD) problem is of utmost importance in the electric power system owing to its substantial impact on stability, cost-effectiveness, and security. Several metaheuristic algorithms have been developed to address this challenge, but they all suffer from either being stuck in local minima, having an insufficiently fast convergence rate, or having a prohibitively high computational cost. Therefore, in this study, the performance of four recently published metaheuristic algorithms, namely the mantis search algorithm (MSA), spider wasp optimizer (SWO), nutcracker optimization algorithm (NOA), and artificial gorilla optimizer (GTO), is assessed to solve this problem with the purpose of minimizing power losses and voltage deviation. These algorithms were chosen due to the robustness of their local optimality avoidance and convergence speed acceleration mechanisms. In addition, a modified variant of NOA, known as MNOA, is herein proposed to further improve its performance. This modified variant does not combine the information of the newly generated solution with the current solution to avoid falling into local minima and accelerate the convergence speed. However, MNOA still needs further improvement to strengthen its performance for large-scale problems, so it is integrated with a newly proposed improvement mechanism to promote its exploration and exploitation operators; this hybrid variant was called HNOA. These proposed algorithms are used to estimate potential solutions to the ORPD problem in small-scale, medium-scale, and large-scale systems and are being tested and validated on the IEEE 14-bus, IEEE 39-bus, IEEE 57-bus, IEEE 118-bus, and IEEE 300-bus electrical power systems. In comparison to eight rival optimizers, HNOA is superior for large-scale systems (IEEE 118-bus and 300-bus systems) at optimizing power losses and voltage deviation; MNOA performs better for medium-scale systems (IEEE 57-bus); and MSA excels for small-scale systems (IEEE 14-bus and 39-bus systems). [ABSTRACT FROM AUTHOR]

Purpose – The proposed model has been aimed to predict stock market signals by designing an accurate model. In this sense, the stock market is analysed by the technical analysis of Japanese Candlestick, which is combined by the following meta heuristic algorithms: support vector machine (SVM), meta-heuristic algorithms, particle swarm optimization (PSO), imperialist competition algorithm (ICA) and genetic algorithm (GA). Design/methodology/approach – In addition, among the developed algorithms, the most effective one is chosen to determine probable sell and buy signals. Moreover, the authors have proposed comparative results to validate the designed model in this study with the same basic models of three articles in the past. Hence, PSO is used as a classification method to search the solution space absolutelyand with the high speed of running. In terms of the second model, SVM and ICA are examined by the time. Where the ICA is an improver for the SVM parameters. Finally, in the third model, SVM and GA are studied, where GA acts as optimizer and feature selection agent. Findings – Results have been indicated that, the prediction accuracy of all new models are high for only six days, however, with respect to the confusion matrixes results, it is understood that the SVM-GA and SVM-ICA models have correctly predicted more sell signals, and the SCM-PSO model has correctly predicted more buy signals. However, SVM-ICA has shown better performance than other models considering executing the implemented models. Research limitations/implications – In this study, the authors to analyze the data the long length of time between the years 2013–2021, makes the input data analysis challenging. They must be changed with respect to the conditions. Originality/value – In this study, two methods have been developed in a candlestick model, they are raw based and signal-based approaches which the hit rate is determined by the percentage of correct evaluations of the stock market for a 16-day period.

Abstract In this paper, a new algorithm named the improved transient search optimization algorithm (ITSOA) is utilized to solve classical test functions, optimize the consumption of building energy, and optimize hybrid energy system production. The conventional TSOA draws inspiration from the fleeting behavior of electrical circuits with energy storage components. Rosenbrock’s direct rotation technique is used to improve the traditional TSOA performance against exploration and exploitation unbalance. First, the ITSOA performance is investigated in solving 23 classical benchmark functions, and the outcomes have shown the superior capability of the recommended algorithm in comparison with the conventional TSOA, DMO, SHO, GA, MRFO, and PSO methods. Also, the ITSOA proficiency is verified in solving the building energy optimization (BEO) problem for minimizing the energy usage of two simple and detailed buildings. The optimization results showed that the optimized solutions of ITSOA in single and multi-objective optimizations compared to conventional TSOA, DMO, SHO, GA, MRFO, and PSO obtained a lower value of the cost function. Also, the superiority of ITSOA has been confirmed to solve the BEO compared to previous methods. Moreover, the multi-objective optimization results have shown that ITSOA is able to determine the ultimate solution among the Pareto front set based on the fuzzy decision-making approach and building energy utilization decisions.

Abstract Transmission expansion planning (TEP) is a vital process of ensuring power systems' reliable and efficient operation. The optimization of TEP is a complex challenge, necessitating the application of mathematical programming techniques and meta-heuristics. However, selecting the right optimization algorithm is crucial, as each algorithm has its strengths and limitations. Therefore, testing new optimization algorithms is essential to enhance the toolbox of methods. This paper presents a comprehensive study on the application of ten recent meta-heuristic algorithms for solving the TEP problem across three distinct power networks varying in scale. The ten meta-heuristic algorithms considered in this study include Sinh Cosh Optimizer, Walrus Optimizer, Snow Geese Algorithm, Triangulation Topology Aggregation Optimizer, Electric Eel Foraging Optimization, Kepler Optimization Algorithm (KOA), Dung Beetle Optimizer, Sea-Horse Optimizer, Special Relativity Search, and White Shark Optimizer (WSO). Three TEP models incorporating fault current limiters and thyristor-controlled series compensation devices are utilized to evaluate the performance of the meta-heuristic algorithms, each representing a different scale and complexity level. Factors such as convergence speed, solution quality, and scalability are considered in evaluating the algorithms’ performance. The results demonstrated that KOA achieved the best performance across all tested systems in terms of solution quality. KOA’s average value was 6.8% lower than the second-best algorithm in some case studies. Additionally, the results indicated that WSO required approximately 2–3 times less time than the other algorithms. However, despite WSO’s rapid convergence, its average solution value was comparatively higher than that of some other algorithms. In TEP, prioritizing solution quality is paramount over algorithm speed.

This paper presents a comprehensive review of the most recent papers and research trends in the fields of wind energy and artificial intelligence. Our study aims to guide future research by identifying the potential application and research areas of artificial intelligence and machine learning techniques in the wind energy sector and the knowledge gaps in this field. Artificial intelligence techniques offer significant benefits and advantages in many sub‐areas, such as increasing the efficiency of wind energy facilities, estimating energy production, optimizing operation and maintenance, providing security and control, data analysis, and management. Our research focuses on studies indexed in the Web of Science library on wind energy between 2000 and 2023 using sub‐branches of artificial intelligence techniques such as artificial neural networks, other machine learning methods, data mining, fuzzy logic, meta‐heuristics, and statistical methods. In this way, current methods and techniques in the literature are examined to produce more efficient, sustainable, and reliable wind energy, and the findings are discussed for future studies. This comprehensive evaluation is designed to be helpful to academics and specialists interested in acquiring a current and broad perspective on the types of uses of artificial intelligence in wind energy and seeking what research subjects are needed in this field. [ABSTRACT FROM AUTHOR]

In recent years, quadcopter-type unmanned aerial vehicles have been preferred in many engineering applications. Because of its nonlinear dynamic model that makes it hard to create optimal control, quadcopter control is one of the main focuses of control engineering and has been studied by many researchers. A quadcopter has six degrees of freedom movement capability and multi-input multi-output structure in its dynamic model. The full nonlinear model of the quadcopter is derived using the results of the experimental studies in the literature. In this study, the control of the quadcopter is realized using the symbolic limited optimal discrete controller synthesis (S-DCS) method. The attitude, altitude, and horizontal movement control of the quadcopter are carried out. To validate the success of the SDCS controller, the control of the quadcopter is realized with fractional order proportional-integral-derivative (FOPID) controllers. The parameters of the FOPID controllers are calculated using Fire Hawk Optimizer, Flying Fox Optimization Algorithm, and Puma Optimizer, which are recently developed meta-heuristic (MH) algorithms. The performance of the S-DCS controller is compared with the performance of the optimal FOPID controllers. In the path planning part of this study, the optimal path planning performances of the SDCS method and the MH algorithms are tested and compared. The optimal solution of the traveling salesman problem (TSP) for a single quadcopter and min-max TSP with multiple depots for multi quadcopters are obtained. The methods and the cases that optimize the dynamic behavior and the path planning of the quadcopter are investigated and determined. [ABSTRACT FROM AUTHOR]

Suspended sediment load estimation is vital for the development of river initiatives, water resources management, the ecological health of rivers, determination of the economic life of dams and the quality of water resources. In this study, the potential of Feed Forward Neural Network (FFNN), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Shuffled Frog Leaping Algorithm (SFLA) models was evaluated for suspended sediment load (SSL) estimation in Yeşilırmak River. The heat map of Pearson correlation values of meteorological and hydrological parameters in 1973–2021, which significantly impacted SSL estimation, was examined to estimate SSL values. As a result of the analysis it was developed a prediction model with three different combinations of precipitation, stream flow and past SSL values (M1: streamflow, M2: streamflow and precipitation, M3: streamflow, precipitation, and SSL). The prediction accuracy of the models was visually compared with the Coefficient of Determination (R2), Bias Factor (BF), Mean Absolute Error (MAE), Mean Bias Error (MBE), Root Mean Square Error (RMSE), Akaike Information Criterion (AIC), Kling-Gupta Efficiency (KGE) statistical criteria and Bland-Altan plot, boxplot, scatter plot and line plot. Based on the analyses, the PSO-ANN model in the M1 model combination showed good estimation performance with an RMSE of 1739.92, MAE of 448.56, AIC of 1061.55, R2 of 0.96, MBE of 448.56, and BF of 0.29. Similarly, the SFLA-ANN model in the M2 model combination had an RMSE of 1819.58, MAE of 520.64, AIC of 1069.9, R2 of 0.96, MBE of 520.64, and BF of 0.19. In the M3 model combination, the SFLA-ANN model achieved an RMSE of 1423.09, MAE of 759.88, AIC of 1071.9, R2 of 0.81, MBE of 411.31, and BF of -0.77. Overall, these models can be considered good estimators as their predicted values are generally close to the measured values. The study outputs can help ensure water structures' effective lifespan and operation and take precautions against sediment-related disaster risks. [ABSTRACT FROM AUTHOR]

Petroleum reservoirs are often influenced by various flow behaviours including the mixture of gas, water and oil. The gas relative permeability is used to estimate how much of the gas in the reservoir is producible at a given water saturation level. Therefore, the gas relative permeability is a significant parameter to characterize the behaviour of petroleum reservoirs. However, the measurement of gas relative permeability by traditional methods tends to be comparatively expensive and time-consuming. In the recent years, the machine learning techniques provided new alternatives for predicting the gas relative permeability. For this purpose, five new methods were proposed based on kernel extreme learning machine (KELM) technique. Five meta-heuristic algorithms were adopted to tune the model hyper-parameters of KELMs, i.e., butterfly optimization algorithm (BOA), tunicate swarm algorithm (TSA), Multi-verse optimizer (MVO), Golden jackal optimization (GJO) and Harris hawk's optimization (HHO). Five-fold cross validation was used to increase the model generalization. An extensive dataset from the experiments which contain 1024 data were taken to develop models. Four classical statistical indicators were used to measure the model performance, i.e., root mean squared error (RMSE), coefficient of determination (R2), variance accounted for (VAF) and mean absolute error (MAE). In addition, two comprehensive manners, overall evaluation index (GI) and Taylor Diagram, were evaluated to provide overall model assessments. Proposed hybrid KELM models performed better than several other machine learning techniques. BOA-KELM model with swarm size 150 generated the best generalization for the testing set and could be recommended to predict the gas relative permeability with the same inputs used in this study. The detailed performance of BOA-KELM includes: training set (GI:0.1736; R2: 0.9902; RMSE: 0.7477; VAF: 99.0218; MAE: 10.6636), testing set (GI:0.4164; R2: 0.9789; RMSE: 0.5314; VAF: 97.8917; MAE: 4.1706). The mutual information technique was employed to examine the influence of influential factors to the model interpretation and it can be found that the gas saturation had a larger influence on the hybrid KELM models. When it was used as an individual input, the overall prediction decreased but acceptable prediction performance still can be obtained by hybrid KELM models. In the case of the gas saturation to be the only input, the best testing R2 (0.94) could be generated by MVO-KELM which is higher than the R2 from the empirical method named Corey-Brooks model and several other machine learning techniques. The main novelty of this study is that five new machine learning methods were proposed to predict the gas relative permeability and performed better than other empirical or machine learning techniques. Highlights: Five new methods based on KELM were proposed to predict gas relative permeability in reservoir. Meta-heuristic algorithms were used to tune the hyper-parameters in KELM. BOA-KELM model with swarm size 150 brought the best generalization ability. Hybrid KELM models performed better than other classical and machine learning model for multi-inputs. Mutual information was used to explore the interpretation of inputs. [ABSTRACT FROM AUTHOR]

This study presents a novel method, termed RBAVO-DE (Relief Binary African Vultures Optimization based on Differential Evolution), aimed at addressing the Gene Selection (GS) challenge in high-dimensional RNA-Seq data, specifically the rnaseqv2 lluminaHiSeq rnaseqv2 un edu Level 3 RSEM genes normalized dataset, which contains over 20,000 genes. RNA Sequencing (RNA-Seq) is a transformative approach that enables the comprehensive quantification and characterization of gene expressions, surpassing the capabilities of micro-array technologies by offering a more detailed view of RNA-Seq gene expression data. Quantitative gene expression analysis can be pivotal in identifying genes that differentiate normal from malignant tissues. However, managing these high-dimensional dense matrix data presents significant challenges. The RBAVO-DE algorithm is designed to meticulously select the most informative genes from a dataset comprising more than 20,000 genes and assess their relevance across twenty-two cancer datasets. To determine the effectiveness of the selected genes, this study employs the Support Vector Machine (SVM) and k-Nearest Neighbor (k-NN) classifiers. Compared to binary versions of widely recognized meta-heuristic algorithms, RBAVO-DE demonstrates superior performance. According to Wilcoxon's rank-sum test, with a 5% significance level, RBAVO-DE achieves up to 100% classification accuracy and reduces the feature size by up to 98% in most of the twenty-two cancer datasets examined. This advancement underscores the potential of RBAVO-DE to enhance the precision of gene selection for cancer research, thereby facilitating more accurate and efficient identification of key genetic markers. [ABSTRACT FROM AUTHOR]

Nowadays, phishing attacks have grown rapidly, and there is an urgent need to introduce a suitable detection method that has the ability to detect different types of phishing attacks. This paper investigates the capability to use bio-inspired meta-heuristic algorithms to improve the performance of the detection engine for phishing attacks by reducing the number of features. This improvement was practiced by investigating the effectiveness of five meta-heuristic algorithms: Particle Swarm Optimization (PSO), Firefly Optimization Algorithm (FFA), Multi-Verse Optimizer (MVO), Moth-Flame Optimization algorithm (MFO), and BAT optimization algorithm, to select the relevant features that could be affected directly by different types of phishing attacks. The suggested detection model was tested and evaluated using four benchmark phishing attack datasets, and the Apache Spark-based decision tree algorithm was selected as a detection engine. The conducted experiments have demonstrated that the Spark-based MVO algorithm achieved the highest detection rate for detecting different types of phishing attacks within four phishing attack datasets. Moreover, the suggested detection model was able to reduce effectively the feature space, which could enhance the computational efficiency. [ABSTRACT FROM AUTHOR]

Structural health monitoring (SHM) is an important and practical procedure for ensuring the structural integrity and serviceability of civil engineering structures such as bridges, buildings, and dams. Model-driven or data-driven strategies for structural response prediction are now widely combined with advances in engineering for use in SHM applications. Engineers have recently demonstrated increasing interest in using machine learning (ML) and artificial intelligence (AI) to achieve a variety of benefits and possibilities, notably for predicting structural reactions. This study serves as a comprehensive overview of the use of ML applications for structural response prediction in the context of SHM for civil engineering structures, with a particular focus on ML, deep learning (DL), and meta-heuristic algorithms. Accordingly, this study summarizes existing knowledge, presents concepts in a simple way, highlights trends, provides methodological insights, and provides a valuable resource for researchers, stakeholders, and decision-makers to benefit from. It is observed that the use of ML, DL, and meta-heuristic algorithms to predict the response of civil engineering structures within an acceptable accuracy range can be employed for SHM, resulting in improved speed, efficiency, and accuracy compared to conventional approaches. [ABSTRACT FROM AUTHOR]

Reconfiguration strategies are used to reduce power losses and increase the reliability of the distribution systems. Since the optimal reconfiguration problem is a multi-objective optimization problem with non-convex functions and constraints, meta-heuristic algorithms are the most suitable choice for the problem-solving approach. One of the new meta-heuristic algorithms that exhibits excellent performance in solving multi-objective problems is the wild mice colony (WMC) algorithm, which is implemented based on aggressive and mating strategies of wild mice. In this paper, the distribution network reconfiguration problem is solved to reduce power losses, improve reliability, and increase the voltage profile of network buses using the WMC algorithm. In addition, the obtained results are compared with conventional multi-objective algorithms. The optimal reconfiguration problem is applied to the IEEE 33-bus and 69-bus test systems. The comparative study confirms the superior performance of the proposed algorithm in terms of convergence speed, execution time, and the final solution. [ABSTRACT FROM AUTHOR]

Particle Swarm Optimization (PSO) has drawn attention due to its widespread use in scientific and engineering fields. However, it suffers from a major limitation which is its slow exploration capability leading to stagnation. To overcome this limitation, various algorithms have been hybridized to improve the exploration phase of PSO but still there is a need to improve it further. Keeping this in mind, this paper proposes a novel hybrid meta-heuristic algorithm called the Hybrid Pelican-Particle Swarm Optimization (HPPSO) for solving complex optimization problems. The purpose of hybridization is motivated by the excellent exploration capability of the Pelican Optimization Algorithm (POA). The performance of the proposed HPPSO has been tested on 33 standard benchmark functions in MATLAB (R2023a). For evaluation, the obtained results of proposed HPPSO algorithm are compared with conventional PSO and POA along with other numerous hybridized algorithms of PSO (PSOGSA, HFPSO, PSOBOA, and PSOGWO). The results are analyzed statistically through convergence curves, boxplot and a non-parametric Wilcoxon signed rank test. These analyses show that the proposed HPPSO algorithm achieves a better optimum than other algorithms used in the present paper. [ABSTRACT FROM AUTHOR]

In this paper, a new algorithm named the improved transient search optimization algorithm (ITSOA) is utilized to solve classical test functions, optimize the consumption of building energy, and optimize hybrid energy system production. The conventional TSOA draws inspiration from the fleeting behavior of electrical circuits with energy storage components. Rosenbrock's direct rotation technique is used to improve the traditional TSOA performance against exploration and exploitation unbalance. First, the ITSOA performance is investigated in solving 23 classical benchmark functions, and the outcomes have shown the superior capability of the recommended algorithm in comparison with the conventional TSOA, DMO, SHO, GA, MRFO, and PSO methods. Also, the ITSOA proficiency is verified in solving the building energy optimization (BEO) problem for minimizing the energy usage of two simple and detailed buildings. The optimization results showed that the optimized solutions of ITSOA in single and multi-objective optimizations compared to conventional TSOA, DMO, SHO, GA, MRFO, and PSO obtained a lower value of the cost function. Also, the superiority of ITSOA has been confirmed to solve the BEO compared to previous methods. Moreover, the multi-objective optimization results have shown that ITSOA is able to determine the ultimate solution among the Pareto front set based on the fuzzy decision-making approach and building energy utilization decisions. [ABSTRACT FROM AUTHOR]

The sine cosine algorithm (SCA), a meta‐heuristic optimization method, is used in this study to provide a precise linear and elliptical antenna array design for synthesizing the ideal far‐field radiation pattern in the fifth‐generation (5G) communication spectrum. The wireless communication system will undergo dramatic changes thanks to the forthcoming 5G technology, which offers exceptionally high data rates, increased capacity, reduced latency, and outstanding service quality. The most important component of 5G communications is an accurate antenna array design for an optimum far‐field radiation pattern synthesis with a suppressed sidelobe level (SLL) value and half power beam width (HPBW). While long‐distance communication necessitates a low HPBW, the entire side lobe area needs a suppressed SLL to prevent interference. The SCA is used in this case to the optimal feeding currents applied to each array member in the design examples of the concentric circular antenna arrays (CCAA) discussed in this article. It shows the litheness and attainment of the propound algorithm named SCA, chosen CCAAs with three rings and varying amounts of components or antenna array sets those are stated as follows: Set I (4, 6, 8 elements), Set II (8, 10, 12 elements), Set III (6, 12, 18 elements), Set IV (8, 14, 20 elements) with and without the center element are amalgamate. Apply the PSO, Jaya, and SCA optimization algorithms for all four Sets of antenna arrays and compare the attained results; the SLL values achieved by the SCA technique are contrasted with those of other current optimization techniques. The outcomes of all examinations reveal that the SCA algorithm achieved a superior SLL reduction over other optimization techniques. [ABSTRACT FROM AUTHOR]

Cloud computing is considered suitable for organizations thanks to its flexibility and the provision of digital services via the Internet. The cloud provides nearly limitless computing resources on demand without any upfront costs or long-term contracts, enabling organizations to meet their computing needs more economically. Furthermore, cloud computing provides higher security, scalability, and reliability levels than traditional computing solutions. The efficiency of the platform affects factors such as Quality of Service (QoS), congestion, lifetime, energy consumption, dependability, and scalability. Load balancing refers to managing traffic flow to spread it across several channels. Asymmetric network traffic results in increased traffic processing, more congestion on specific routes, and fewer packets delivered. The paper focuses on analyzing the use of the meta-optimization algorithm based on the principles of natural selection to solve the imbalance of loads in cloud systems. To sum up, it offers a detailed literature review on the essential meta-heuristic algorithms for load balancing in cloud computing. The study also assesses and analyses meta-heuristic algorithm performance in load balancing, as revealed by past studies, experiments, and case studies. Key performance indicators encompass response time, throughput, resource utilization, and scalability, and they are used to assess how these algorithms impact load balance efficiency. [ABSTRACT FROM AUTHOR]

Harmony Search (HS) algorithm is a swarm intelligence algorithm inspired by musical improvisation. Although HS has been applied to various engineering problems, it faces challenges such as getting trapped in local optima, slow convergence speed, and low optimization accuracy when applied to complex problems. To address these issues, this paper proposes an improved version of HS called Equilibrium Optimization-based Harmony Search Algorithm with Nonlinear Dynamic Domains (EO-HS-NDD). EO-HS-NDD integrates multiple leadership-guided strategies from the Equilibrium Optimizer (EO) algorithm, using harmony memory considering disharmony and historical harmony memory, while leveraging the hidden guidance direction information from the Equilibrium Optimizer. Additionally, the algorithm designs a nonlinear dynamic convergence domain to adaptively adjust the search space size and accelerate convergence speed. Furthermore, to balance exploration and exploitation capabilities, appropriate adaptive adjustments are made to Harmony Memory Considering Rate (HMCR) and Pitch Adjustment Rate (PAR). Experimental validation on the CEC2017 test function set demonstrates that EO-HS-NDD outperforms HS and nine other HS variants in terms of robustness, convergence speed, and optimization accuracy. Comparisons with advanced versions of the Differential Evolution (DE) algorithm also indicate that EO-HS-NDD exhibits superior solving capabilities. Moreover, EO-HS-NDD is applied to solve 15 real-world optimization problems from CEC2020 and compared with advanced algorithms from the CEC2020 competition. The experimental results show that EO-HS-NDD performs well in solving real-world optimization problems. [ABSTRACT FROM AUTHOR]

The goals of this research include investigating the efficiency of the finite element method and its combination with meta-heuristic algorithms to solve the optimization problem of the pump and treat (PAT) system. In this research, the hybrid optimization-simulation models were developed to determine the optimal groundwater remediation strategy using the pump and treat (PAT) system. The results indicated that when we consider minimizing the contaminant in groundwater at the end of the remediation period as the objective function, locating the pumping wells in the path of the contaminant flow and close to the contaminant source. In a single objective problem, the GA-FEM model with an average value of 0.0005036 in five runs of the model had the best performance among other models. The results of the two-objective problem indicated that MOMVO-FEM, despite a few solutions in optimal Pareto-front, could find a better location for pumping wells. Finally, it can be said that among factors such as the location of pumping wells and pumping rate, the most influential factor in choosing the right pumping and treatment policy is the proper location of pumping wells. Also, the location of contamination pumping wells does not necessarily correspond to the location of the contamination seepage. [ABSTRACT FROM AUTHOR]

In this paper, we present an improved imperialist competitive algorithm for solving an inverse form of the Huxley equation, which is a nonlinear partial differential equation. To show the effectiveness of our proposed algorithm, we conduct a comparative analysis with the original imperialist competitive algorithm and a genetic algorithm. The improvement suggested in this study makes the original imperialist competitive algorithm a more powerful method for function approximation. The numerical results show that the improved imperialist competitive algorithm is an efficient algorithm for determining the unknown boundary conditions of the Huxley equation and solving the inverse form of nonlinear partial differential equations. [ABSTRACT FROM AUTHOR]

This study employed meta-heuristic clustering algorithms to determine the source and mechanism of groundwater salinization in Quebec's Saguenay-Lac-Saint-Jean (SLSJ) region, utilizing hydrogeochemical (38 inorganic constituents, including minor, major, and trace elements) and isotopic data (δ18O and δ2H). A total of 382 groundwater and precipitation samples were examined. Among the meta-heuristic algorithms, Artificial Bee Colony K-Means (ABCKM), Differential Evolution K-Means (DEKM), Harmony Search K-Means (HSKM), Particle Swarm Optimization K-Means (PSOKM), and Genetic K-Means (GKM) were used and investigated, and finally, PSOKM displayed superior performance and was chosen for further investigation. Analysis of diverse plots and hydrogeochemical modeling unveiled the impact of the Laflamme Sea invasion on groundwater chemistry. PSOKM1, PSOKM4, and PSOKM5 exhibited notable carbonate and silicate dissolution, with PSOKM4 demonstrating predominant carbonate dissolution. Cation exchange was identified through binary plots and Chloro Alkaline Index (CAI), with reverse cation exchange predominantly observed in most PSOKM4 samples, while positive values suggested direct cation exchange in other clusters. Spatial dynamics analysis using HFE-D indicated that salinization occurs as groundwater flows through crystalline bedrock aquifers, resulting in a transition from HCO3− dominance in PSOKM4 to Cl− dominance in the remaining clusters. Interaction between groundwater and rock along this path facilitated a transformation towards a Na-Cl end-member. The closely aligned stable isotopes with the Global Meteoric Water Line (GMWL) indicated a blend of meteoric water and seawater as the groundwater source. [ABSTRACT FROM AUTHOR]

Wireless mesh networks (WMNs) have become a popular solution for expanding internet service and communication in both urban and rural areas. However, the performance of WMNs depends on generating optimized time-division multiple access (TDMA) schedules, which distribute time into a list of slots called superframes. This study proposes novel meta-heuristic algorithms to generate TDMA link schedules in WMNs using two different interference/constraint models: multi-transmit-receive (MTR) and full-duplex (FD). The objectives of this study are to optimize the TDMA frame for packet transmission, satisfy the constraints, and minimize the end-to-end delay. The significant contributions of this study are: (1) proposing effective and efficient heuristic solutions to solve the NP-complete problem of generating optimal TDMA link schedules in WMNs; (2) investigating the new FD interference model to improve the network capacity above the physical layer. To achieve these objectives and contributions, the study uses two popular meta-heuristics, the artificial bee colony (ABC) and/or genetic algorithm (GA), to solve the known NP-complete problems of joint scheduling, power control, and rate control. The results of this study show that the proposed algorithms can generate optimized TDMA link schedules for both MTR and FD models. The joint routing and scheduling approach further minimizes end-to-end delay while maintaining the schedule's minimum length and/or maximum capacity. The proposed solution outperforms the existing solutions in terms of the number of active links, end-to-end delay, and network capacity. The research aims to improve the efficiency and effectiveness of WMNs in most applications that require high throughput and fast response time. [ABSTRACT FROM AUTHOR]

The optimal parameter values in a feed-forward neural network model play an important role in determining the efficiency and significance of the trained model. In this paper, we propose an upgraded artificial electric field algorithm (AEFA) for training feed-forward neural network models. This paper also throws some light on the effective use of multi-agent meta-heuristic techniques for the training of neural network models and their future prospects. Seven real-life data sets are used to train neural network models, the results of these trained models show that the proposed scheme performs well in comparison to other training algorithms in terms of high classification accuracy and minimum test error values including gradient-based algorithms and differential evolution variants. Some fundamental modifications in AEFA are also proposed to make it more suitable for training neural networks. All the experimental findings show that the search capabilities and convergence rate of the proposed scheme are better than those of other capable schemes, including gradient-based schemes. [ABSTRACT FROM AUTHOR]

The methodology under the term model-based software engineering (MBSE) gained importance already around 20 years ago, after the publication of the Model-Driven Architecture (MDA) initiative by the Object Management Group (OMG). This development methodology continues to evolve, giving rise to recent proposals such as low-code or no-code. Something that has not changed, as recent surveys point out, is the need for powerful testing approaches and tools for these new methodologies. In MBSE, test inputs are models, so it is key to have frameworks for model generation. However, the main shortcomings of existing model-generation frameworks are their performance limitations and the need for domain-specific knowledge, which seriously hampers their industrial adoption. In this paper, we present the Yekta low-code framework that allows to generate models in a simple way through the application of metaheuristic algorithms.