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This research article addresses the performance analysis of Markovian retrial queueing system with two types of customers, unreliable server, and Bernoulli feedback. Both regular customers (RC) and prime customers (PC) may either join, or balk from the system based on the trade-off between service profit and delay cost. When the system is busy, the regular customers have to choose whether to join a retrial orbit and make re-attempts or leave the system. Furthermore, due to congestion among regular customers, the server may discontinue the service during breakdown. Due to the unavailability of the service process, customers may experience dissatisfaction. Therefore, our objective is to introduce a Bernoulli feedback service process to enhance service quality, ensuring that customers are successfully served with a certain probability. To analyze the proposed model mathematically, Chapman-Kolmogorov (C-K) inflow-outflow balanced equations have been framed. Then, the probability generating function (PGF) method employed to explicitly derive the queue size distribution, throughput, and other performance metrics. These performance measures provide critical insights into system behavior, which are then incorporated to determine the equilibrium strategies for two types of joining strategies: (i) non-cooperative strategies and (ii) cooperative strategies. Finally, optimization approaches are employed to determine the optimum cost and make tactical decisions regarding the quality of service (QoS) in an integrated manner. The cost optimization is done using metaheuristic optimization techniques such as PSO and GWO. The analytic results established are validated by numerical simulation. The effect of various parameters on the performance indices are examined by cost optimization and sensitivity analysis. The comparison of both algorithms, including average fitness, standard deviation, and convergence analysis, were used and combined with Wilcoxon rank-sum test. [ABSTRACT FROM AUTHOR]

This research investigation is concerned with social optimization and customers’ strategic behavior for a double orbit retrial queueing model with vacation, aiming to enhance the performance of virtual call centers. In many call center scenarios, if the server is busy, the arriving customer moves to premium/ordinary orbit, i.e., becomes a repeated customer; otherwise, if the server is accessible, the arriving customer joins the system to receive the required service. Once the service is completed, the server will look into the premium orbit to check whether there is any customer who needs service. If no new customer from premium/ordinary orbit or outside arrives and the system is empty, then the server takes a vacation. The customer’s decision to wait or balk from the system depends on the server’s status and the reward for receiving the service. By using a probability generating function and iterative approach, the long-run probability distribution of the queue size and other metrics, viz. equilibrium thresholds, entering probability, etc., have been obtained. Moreover, the social welfare function is analyzed based on two given information levels. The optimal solution is presented by solving the social welfare maximization problem using particle swarm optimization and harmony search techniques. The impact of different parameters on the performance metrics in Virtual Call Centers (VCC) is examined. [ABSTRACT FROM AUTHOR]

Defocus blur is often encountered in images taken with optical imaging equipment. It might be unwanted, but it might also be a deliberate artistic effect, which means it might help how we see the scenario in an image. In specific applications like image restoration or object detection, there may be a need to divide a partially blurred image into its blurred and sharp regions. The effectiveness of blur detection is influenced by how features are combined. In this paper, we propose a parameter-free metaheuristic optimization strategy known as teacher-learning-based optimization (TLBO) to find an optimal weight vector for the combination of blur maps. First, we compute multi-scale blur maps, i.e., features using an LBP-based blur metric. Then, we apply a regularization scheme to refine the initial blur maps. This results in a smooth, edge-preserving blur map that leverages structural information for improved segmentation. Lastly, TLBO is used to find the optimal weight vectors of each refined blur map for the linear feature combination. The proposed model is validated through extensive experiments on two benchmark datasets, and its performance is comparable against five state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The RIME algorithm is a novel physical-based meta-heuristic algorithm with a strong ability to solve global optimization problems and address challenges in engineering applications. It implements exploration and exploitation behaviors by constructing a rime-ice growth process. However, RIME comes with a couple of disadvantages: a limited exploratory capability, slow convergence, and inherent asymmetry between exploration and exploitation. An improved version with more efficiency and adaptability to solve these issues now comes in the form of Hybrid Estimation Rime-ice Optimization, in short, HERIME. A probabilistic model-based sampling approach of the estimated distribution algorithm is utilized to enhance the quality of the RIME population and boost its global exploration capability. A roulette-based fitness distance balanced selection strategy is used to strengthen the hard-rime phase of RIME to effectively enhance the balance between the exploitation and exploration phases of the optimization process. We validate HERIME using 41 functions from the IEEE CEC2017 and IEEE CEC2022 test suites and compare its optimization accuracy, convergence, and stability with four classical and recent metaheuristic algorithms as well as five advanced algorithms to reveal the fact that the proposed algorithm outperforms all of them. Statistical research using the Friedman test and Wilcoxon rank sum test also confirms its excellent performance. Moreover, ablation experiments validate the effectiveness of each strategy individually. Thus, the experimental results show that HERIME has better search efficiency and optimization accuracy and is effective in dealing with global optimization problems. [ABSTRACT FROM AUTHOR]

The limited nature of fossil resources and their unsustainable characteristics have led to increased interest in renewable sources. However, significant work remains to be carried out to fully integrate these systems into existing power distribution networks, both technically and legally. While reliability holds great potential for improving energy production sustainability, the dependence of solar energy production plants on weather conditions can complicate the realization of consistent production without incurring high storage costs. Therefore, the accurate prediction of solar power production is vital for efficient grid management and energy trading. Machine learning models have emerged as a prospective solution, as they are able to handle immense datasets and model complex patterns within the data. This work explores the use of metaheuristic optimization techniques for optimizing recurrent forecasting models to predict power production from solar substations. Additionally, a modified metaheuristic optimizer is introduced to meet the demanding requirements of optimization. Simulations, along with a rigid comparative analysis with other contemporary metaheuristics, are also conducted on a real-world dataset, with the best models achieving a mean squared error (MSE) of just 0.000935 volts and 0.007011 volts on the two datasets, suggesting viability for real-world usage. The best-performing models are further examined for their applicability in embedded tiny machine learning (TinyML) applications. The discussion provided in this manuscript also includes the legal framework for renewable energy forecasting, its integration, and the policy implications of establishing a decentralized and cost-effective forecasting system. [ABSTRACT FROM AUTHOR]

Finite control set model predictive control (FCS-MPC) is an attractive control method for electric drives. This is primarily due to the ease of implementation and robust responses. When applied to rotor current control of the Doubly Fed Induction Generator (DFIG), FCS-MPC has thus far exhibited promising results when compared to the conventional Proportional Integral control strategy. Recently, there has been research conducted regarding the reduction in switching frequency of FCS-MPC. Preliminary studies indicate that a reduction in switching frequency will result in larger current ripples and a greater total harmonic distortion (THD). However, research in this area is limited. The aim of this study is two-fold. Firstly, an indication into the effect of weighting factor magnitude on current ripple is provided. Thereafter, the research work provides insight into the effect of such weighting factor on the overall current ripple of FCS-MPC applied to the DFIG and attempts to determine an optimal weighting factor which will simultaneously reduce the switching frequency and keep the current ripple within acceptable limits. To tune the relevant weighting factor, the utilization of swam intelligence is deployed. Three swarm intelligence techniques, particle swarm optimization, the African Vulture Optimization Algorithm, and the Gorilla Troops Optimizer (GTO), are applied to achieve the optimal weighting factor. When applied to a 2 MW DFIG, the results indicated that owing to their strong exploitation capability, these algorithms were able to successfully reduce the switching frequency. The GTO exhibited the overall best results, boasting steady-state errors of 0.03% and 0.02% for the rotor direct and quadrature currents whilst reducing the switching frequency by up to 0.7%. However, as expected, there was a minor increase in the current ripple. A robustness test indicated that the use of metaheuristics still produces superior results in the face of changing operating conditions. The results instill confidence in FCS-MPC as the control strategy of choice, as wind energy conversion systems continue to penetrate the energy sector. [ABSTRACT FROM AUTHOR]

This paper addresses the optimal placement and sizing of Modular Static Synchronous Series Compensators (M-SSSCs) to enhance power system performance. The proposed methodology optimizes four key objectives: reducing transmission line loadability, minimizing power losses, mitigating voltage deviations, and enhancing voltage stability using the L-index. The methodology is validated on two systems: the IEEE 14-bus test network and a sub-area of the Colombian power grid, characterized by aging infrastructure and operational challenges. The optimization process employs three metaheuristic algorithms—Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Teaching–Learning-Based Optimization (TLBO)—to identify optimal configurations. System performance is analyzed under both normal operating conditions and contingency scenarios (N − 1). The results demonstrate that M-SSSC deployment significantly reduces congestion, enhances voltage stability, and improves overall system efficiency. Furthermore, this work highlights the practical application of M-SSSC in modernizing real-world grids, aligning with sustainable energy transition goals. This study identifies the optimal M-SSSC configurations and placement alternatives for the analyzed systems. Specifically, for the Colombian sub-area, the most suitable solutions involve installing M-SSSC devices in capacitive mode on the Termocol–Guajira and Santa Marta–Guajira 220 kV transmission lines. [ABSTRACT FROM AUTHOR]

This paper deals with the optimization of the operational efficiency of a fleet of mobile robots, assigned with delivery-like missions in complex outdoor scenarios. The robots, due to limited onboard computation resources, need to offload some complex computing tasks to an edge/cloud server, requiring artificial intelligence and high computation loads. The mobile robots also need reliable and efficient radio communication with the network hosting edge/cloud servers. The resource assignment aims at minimizing the total latency and delay caused by the use of radio links and computation nodes. This minimization is a nonlinear integer programming problem, with high complexity. In this paper, we present reduced-complexity algorithms that allow to jointly optimize the available radio and computation resources. The original problem is reformulated and simplified, so that it can be solved by also selfish and greedy algorithms. For comparison purposes, a genetic algorithm (GA) is used as the baseline for the proposed optimization techniques. Simulation results in several scenarios show that the proposed sequential minimization (SM) algorithm achieves an almost optimal solution with significantly reduced complexity with respect to GA. [ABSTRACT FROM AUTHOR]

Accurate detection of gastrointestinal (GI) diseases is critical for effective medical intervention. Existing methods often lack accuracy and efficiency, emphasizing the need for more advanced approaches. The complexity and diversity of medical image data, such as those found in GI diseases, can pose challenges for a single model to comprehensively represent all essential features. In such scenarios, an ensemble learning approach becomes important. In this paper, we propose an innovative ensemble learning approach for GI disease prediction. We leverage the power of three transfer learning models, DenseNet169, InceptionV3, and MobileNet, as base learners along with additional layers to effectively learn data-specific features. We implement a weighted averaging ensemble strategy to merge predictions from individual base models and fine-tune the weights using the cuckoo search (CS) with levy flight algorithm. This approach results in more accurate predictions compared to individual models, leveraging the diverse strengths of the base learners for enhanced performance in GI disease prediction. This study is notably the pioneer in introducing a metaheuristics-based optimized model for the detection of GI diseases. We assess the presented model using a publicly accessible endoscopic image dataset that consists of 6,000 images. The results demonstrate exceptional predictive accuracy, with the ensemble achieving an outstanding accuracy of 99.75%. Through Grad-CAM analysis, we gain valuable insights into the decision-making process of the individual base models, enabling us to identify areas of strength and improvement. Our proposed ensemble model outperforms traditional weight assignment methods and existing state-of-the-art methods, showcasing its superiority in GI disease prediction. Our approach has transformative potential in medical image analysis, promising enhanced patient care and diagnostic accuracy in gastroenterology. [ABSTRACT FROM AUTHOR]

The shape of the blade strongly influences the aerodynamic behavior of wind turbines; therefore, it is essential to optimize its design to maximize the energy harvested from the wind. Some works address this optimized design problem using CFD, a tool that requires a lot of computational resources and time and starts from scratch. This work describes a new automated design method to generate aerodynamic profiles of wind turbines using existing blades as a base, which speeds up the design process. The optimization is performed using heuristic techniques, and the aim is to improve the characteristics of the blade shape which impact resilience and durability. Specifically, the glide ratio is maximized to capture maximum energy while ensuring specific design parameters, such as maximum thickness or optimal angle of attack. This methodology can obtain results more quickly and with lower computational cost, in addition to integrating these two design parameters into the optimization process, aspects that have been largely neglected in previous works. The analytical model of the blades is described by a class of two-dimensional shapes suitable for representing airfoils. The drag and lift coefficients are estimated, and a metaheuristic optimization technique, genetic algorithm, is applied to maximize the glide ratio while reducing the difference from the desired design parameters. Using this methodology, three new airfoils have been generated and compared with the existing starting models, S823, NACA 2424, and NACA 64418, achieving improvements in the maximum lift and maximum glide ratio of up to 13.8% and 39%, respectively. For validation purposes, a small 10 kW horizontal-axis wind turbine is simulated using the best design of the blades. The comparison with the existing blades focuses on the calculation of the generated power, the power coefficient, torque, and torque coefficient. For the new airfoils, improvements of 6.7% in the power coefficient and 5.5% in the torque coefficient were achieved. This validates the methodology for optimizing the blade airfoils. [ABSTRACT FROM AUTHOR]

Rockburst is a major challenge to hard rock engineering at great depth. Accurate and timely assessment of rockburst risk can avoid unnecessary casualties and property losses. Despite the existence of various methods for rockburst assessment, there remains an urgent need for a comprehensive and reliable criterion that is easy to both apply and interpret. Developing a new rockburst criterion based on simple parameters can potentially fill this gap. With its advantages, this criterion can facilitate a more effective and efficient prediction of rockburst potential, thereby contributing significantly to enhancing safety measures. In this paper, combined with the internal and external factors of rockburst, four control variables (i.e., integrity index, stress index, brittleness index, and elastic energy index) were selected to be incorporated into a comprehensive rockburstability index (RBSI). Based on 116 sets of rockburst cases, the rockburst potential was accurately quantified and predicted using the categorical boosting (CatBoost) model and the nature-inspired metaheuristic African vultures optimization algorithm (AVOA). In its performance validation, the criterion achieved the highest accuracy of 90.48%, verifying the reliability and effectiveness of the proposed RBSI criterion. Additionally, an interpretive method was applied to analyze the variable influence on the criterion, facilitating the explanation of predictions and the analysis of the formula's robustness under different conditions. In general, compared with existing criterion methods involving relevant indicators, the newly proposed RBSI criterion enhances the accuracy of rockburst potential prediction, and it can effectively and swiftly evaluate the preliminary risk of rockburst. Lastly, a graphical user interface was developed to provide a clear visualization of the assessment of rockburst potential. [ABSTRACT FROM AUTHOR]

The prediction of ultrasound-assisted extraction (UAE) for total phenolic content (TPC) and total flavonoid content (TFC) from Carthamus caeruleus L. rhizomes was conducted using a Gaussian process regression model (GPR) with a multi-objective Grey Wolf optimization approach (MOGWO). A central composite design (CCD) was employed first, examining ethanol concentration, temperature, time, and solvent-to-solid ratio as independent variables. TPC and TFC responses were analyzed under various conditions, revealing significant quadratic and interaction effects (p < 0.05). The GPR was then utilized to predict TPC and TFC, showing high accuracy with correlation coefficients near 1 and minimal root mean square error (RMSE) values. To simultaneously maximize TPC and TFC, the MOGWO was used in a multi-objective framework. Validation through CCD and GPR highlighted GPR's superior predictive accuracy. Optimal conditions (10 % ethanol, 40°C, 20 minutes sonication, and 50 mL g−1 solvent to solid ratio) showed significant discrepancies in CCD predictions but high accuracy in GPR predictions. An interactive tool predicts TPC and TFC using CCD and GPR models. Users input extraction parameters and receive predictions, with a GWO-based optimization module for optimal conditions. The interface enables model comparison, improves process understanding, and optimizes bioactive compound extraction. [Display omitted] • Applied Gaussian process to predict TPC and TFC from C. caeruleus L. • Employed a Central Composite Design with four extraction parameters. • Significant quadratic and interaction effects observed in extraction conditions. • GPR achieved high accuracy with high correlation coefficients and low RMSE. • Interactive tool with GWO-based optimization for efficient extraction [ABSTRACT FROM AUTHOR]

In this paper, it is revealed that random exploration and attraction of the best (REAB) are two underlying procedures in many swarm intelligence algorithms. This is particularly shown in two of the most known swarm algorithms: the particle swarm optimization (PSO) and gray wolf optimizer (GWO) algorithms. From this observation, it is here proposed that instead of building algorithms based on a narrative derived from observing some animal behavior, it is more convenient to focus on algorithms that perform REAB procedures; that is, to build algorithms to make a wide and efficient explorations of the search space and then gradually make that the best-evaluated search agent to attract the rest of the swarm. Following this general idea, two REAB-based algorithms are proposed; one derived from the PSO and one derived from the GWO, called REAB-PSO and REAB-GWO, respectively. To easily and succinctly express both algorithms, variable-sized open balls are employed. A comparison of proposed procedures in this paper and the original PSO and GWO using a controller tuning problem as a test bench show a significant improvement of the REAB-based algorithms over their original counterparts. Ideas here exposed can be used to derive new swarm intelligence algorithms. [ABSTRACT FROM AUTHOR]

Abstract This paper proposes a new optimization technique to make an integration between the Optimal Reactive Power Dispatch (ORPD) problem and Electric Vehicles (EV). Here, a modified metaheuristic algorithm, called the Promoted Osprey Optimizer (POO) is used for this purpose. Inspired by the hunting behavior of ospreys, a predatory bird species, the POO algorithm employs various strategies like diving, soaring, and gliding to effectively explore the search space and avoid local optima. To evaluate its performance, the POO-based model has been applied to the IEEE 118-bus and IEEE 57-bus systems, considering different scenarios of EV penetration. The experimental findings demonstrate that the POO algorithm can effectively optimize the reactive power dispatch problem with EV integration, achieving significant reductions in active power losses and voltage deviations toward several existing metaheuristic optimization techniques in different terms. The POO algorithm demonstrates a significant reduction in power loss, achieving up to 22.2% and 16.2% in the 57-bus and 118-bus systems, respectively. This improvement is accompanied by reductions in voltage deviation of up to 20.6% and 15.7%. In the 57-bus system, power loss is reduced from 2.35 MW to 1.93 MW, while voltage deviation decreases from 0.034 p.u. to 0.027 p.u. For the 118-bus system, power loss is lowered from 4.21 MW to 3.53 MW, and voltage deviation is reduced from 0.051 p.u. to 0.043 p.u. Furthermore, the POO algorithm surpasses other optimization methods in minimizing voltage deviation, achieving reductions of up to 0.056 p.u. in the 57-bus system and up to 0.163 p.u. in the 118-bus system. Consequently, the POO algorithm holds great potential as a valuable tool for power system operators and planners to optimize reactive power dispatch and enhance power system performance with EV integration.

Abstract This paper introduces the Walrus Optimization Algorithm (WaOA) to address load frequency control and automatic voltage regulation in a two-area interconnected power systems. The load frequency control and automatic voltage regulation are critical for maintaining power quality by ensuring stable frequency and voltage levels. The parameters of fractional order Proportional-Integral-Derivative (FO-PID) controller are optimized using WaOA, inspired by the social and foraging behaviors of walruses, which inhabit the arctic and sub-arctic regions. The proposed method demonstrates faster convergence in frequency and voltage regulation and improved tie-line power stabilization compared to recent optimization algorithms such as salp swarm, whale optimization, crayfish optimization, secretary bird optimization, hippopotamus optimization, brown bear optimization, teaching learning optimization, artificial gorilla troop optimization, and wild horse optimization. MATLAB simulations show that the WaOA-tuned FO-PID controller improves frequency regulation by approximately 25%, and exhibits a considerable faster settling time. Bode plot analyses confirm the stability with gain margins of 5.83 dB and 9.61 dB, and phase margins of 10.8 degrees and 28.6 degrees for the two areas respectively. The system modeling and validation in MATLAB showcases the superior performance and reliability of the WaOA-tuned FO-PID controller in enhancing power system stability and quality under step, random step load disturbance, with nonlinearities like GDC and GDB, and system parameter variations.

Abstract Experimental test is conducted to find a relation between soil electrical conductivity with some soil properties. Five input parameters namely water content (w), dry density of soil (γd), degree of saturation (Sr), porosity (η) and voltage (V) are considered to compute electrical conductivity of soil (SEC). The effect of w, γd, Sr and η on SEC are analyzed. These datasets are used in soft computing technique to predict SEC. Five hybrid models namely ANN-GA, ANN-PSO, ANN-FA, ANN-GWO and ANN-MFO are used to predict SEC. To check the performance of these hybrid model, numerous statistical performance parameters (R2, a-10 index, VAF, LMI, RMSE, MAE, MAD and U95) are used. On the basis of these performance parameters, ANN-GA performs better (Due to higher value of R2, VAF, a-10 index and LMI and lower value of RMSE, MAE, MAD and U95) as compare to the other proposed model. The model's performance is also examined using rank analysis, regression curve, Williams plot, error matrix, objective function (OBJ) criterion, and Akaike information criterion (AIC). By applying all the above criteria, it has been observed that ANN-GA model outperforms than the other model to predict SEC. This was recognized to its maximum R2 = 0.9998 and the lowest RMSE = 0.0041 during the training phase, as well as R2 = 0.9975 and RMSE = 0.0112 during the testing phase. The model's reliability index (β) is calculated using the first-order second moment (FOSM) approach, and the result is compared to its actual condition. Additionally, a sensitivity analysis is carried out to ascertain the impact of each input parameter on the output (SEC).Based on the results, the hybrid model of ANN i.e., ANN-GA is potential to assist engineers to predict SEC in the design phase of high voltage buried power cables, ground modification techniques or in the field of agriculture.

Modern civilization is heavily dependent on energy, which burdens the energy sector. Therefore, a highly accurate energy consumption forecast is essential to provide valuable information for efficient energy distribution and storage. This study proposed a hybrid deep learning model, called I-CNN-JS, by incorporating a jellyfish search (JS) algorithm into an ImageNetwinning convolutional neural network (I-CNN) to predict weekahead energy consumption. First, numerical data were encoded into grayscale images for input of the proposed model, showcasing the novelty of using image data for analysis. Second, a newly developed metaheuristic optimization algorithm, JS, was used to improving model accuracy. Results showed that the proposed method outperformed conventional numerical input methods. The optimized model yielded a mean absolute percentage error improvement of 0.5% compared to the default models, indicating that JS is a promising method for achieving the optimal hyperparameters. Sensitivity analysis further evaluated the impact of image pixel orientation on performance model. [ABSTRACT FROM AUTHOR]

This paper proposes a new optimization technique to make an integration between the Optimal Reactive Power Dispatch (ORPD) problem and Electric Vehicles (EV). Here, a modified metaheuristic algorithm, called the Promoted Osprey Optimizer (POO) is used for this purpose. Inspired by the hunting behavior of ospreys, a predatory bird species, the POO algorithm employs various strategies like diving, soaring, and gliding to effectively explore the search space and avoid local optima. To evaluate its performance, the POO-based model has been applied to the IEEE 118-bus and IEEE 57-bus systems, considering different scenarios of EV penetration. The experimental findings demonstrate that the POO algorithm can effectively optimize the reactive power dispatch problem with EV integration, achieving significant reductions in active power losses and voltage deviations toward several existing metaheuristic optimization techniques in different terms. The POO algorithm demonstrates a significant reduction in power loss, achieving up to 22.2% and 16.2% in the 57-bus and 118-bus systems, respectively. This improvement is accompanied by reductions in voltage deviation of up to 20.6% and 15.7%. In the 57-bus system, power loss is reduced from 2.35 MW to 1.93 MW, while voltage deviation decreases from 0.034 p.u. to 0.027 p.u. For the 118-bus system, power loss is lowered from 4.21 MW to 3.53 MW, and voltage deviation is reduced from 0.051 p.u. to 0.043 p.u. Furthermore, the POO algorithm surpasses other optimization methods in minimizing voltage deviation, achieving reductions of up to 0.056 p.u. in the 57-bus system and up to 0.163 p.u. in the 118-bus system. Consequently, the POO algorithm holds great potential as a valuable tool for power system operators and planners to optimize reactive power dispatch and enhance power system performance with EV integration. [ABSTRACT FROM AUTHOR]

This paper introduces the Walrus Optimization Algorithm (WaOA) to address load frequency control and automatic voltage regulation in a two-area interconnected power systems. The load frequency control and automatic voltage regulation are critical for maintaining power quality by ensuring stable frequency and voltage levels. The parameters of fractional order Proportional-Integral-Derivative (FO-PID) controller are optimized using WaOA, inspired by the social and foraging behaviors of walruses, which inhabit the arctic and sub-arctic regions. The proposed method demonstrates faster convergence in frequency and voltage regulation and improved tie-line power stabilization compared to recent optimization algorithms such as salp swarm, whale optimization, crayfish optimization, secretary bird optimization, hippopotamus optimization, brown bear optimization, teaching learning optimization, artificial gorilla troop optimization, and wild horse optimization. MATLAB simulations show that the WaOA-tuned FO-PID controller improves frequency regulation by approximately 25%, and exhibits a considerable faster settling time. Bode plot analyses confirm the stability with gain margins of 5.83 dB and 9.61 dB, and phase margins of 10.8 degrees and 28.6 degrees for the two areas respectively. The system modeling and validation in MATLAB showcases the superior performance and reliability of the WaOA-tuned FO-PID controller in enhancing power system stability and quality under step, random step load disturbance, with nonlinearities like GDC and GDB, and system parameter variations. [ABSTRACT FROM AUTHOR]

Chloride corrosion significantly impacts the durability of reinforced concrete (RC) structures. Traditional evaluation methods are time-consuming and expensive. Machine Learning (ML) offers a promising alternative, providing efficient and accurate predictions. This review explores recent ML advancements in assessing corrosion in RC structures. Various algorithms, such as Artificial Neural Networks (ANNs), Gene Expression Programming (GEP), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM) and Ensemble Learning, have shown potential in estimating corrosion processes, predicting material properties, and evaluating structural durability. Future research should focus on integrating ML with physical models to enhance robustness and reliability in service life prediction. This review summarizes current trends, challenges, and the future potential of ML in predicting chloride ingress and its impact on concrete durability. [ABSTRACT FROM AUTHOR]

Medical imaging has experienced significant development in contemporary medicine and can now record a variety of biomedical pictures from patients to test and analyze the illness and its severity. Computer vision and artificial intelligence may outperform human diagnostic ability and uncover hidden information in biomedical images. In healthcare applications, fast prediction and reliability are of the utmost importance parameters to assure the timely detection of disease. The existing systems have poor classification accuracy, and higher computation time and the system complexity is higher. Low-quality images might impact the processing method, leading to subpar results. Furthermore, extensive preprocessing techniques are necessary for achieving accurate outcomes. Image contrast is one of the most essential visual parameters. Insufficient contrast may present many challenges for computer vision techniques. Traditional contrast adjustment techniques may not be adequate for many applications. Occasionally, these technologies create photos that lack crucial information. The primary contribution of this work is designing a Big Data Architecture (BDA) to improve the dependability of medical systems by producing real-time warnings and making precise forecasts about patient health conditions. A BDA-based Bio-Medical Image Classification (BDA-BMIC) system is designed to detect the illness of patients using Metaheuristic Optimization (Genetic Algorithm) and Gradient Approximation to improve the biomedical image classification process. Extensive tests are conducted on publicly accessible datasets to demonstrate that the suggested retrieval and categorization methods are superior to the current methods. The suggested BDA-BMIC system has average detection accuracy of 94.6% and a sensitivity of 97.3% in the simulation analysis. [ABSTRACT FROM AUTHOR]

This study explores the realm of time series forecasting, focusing on the utilization of Recurrent Neural Networks (RNN) to detect abnormal cardiovascular rhythms in Electrocardiogram (ECG) signals. The principal objective is to optimize RNN performance by finely tuning hyperparameters, a complex task with known NP-hard complexity. To address this challenge, the study employs metaheuristic algorithms, specialized problem-solving techniques crafted for navigating intricate and non-deterministic optimization landscapes. Additionally, a refined algorithm is introduced to overcome limitations inherent in the original approach. This modified algorithm exhibits significant improvements, surpassing its predecessor in identifying anomalous cardiovascular rhythms within ECG signals. The most successful optimized model achieves an accuracy of 99.26%, outperforming models optimized by other contemporary metaheuristics assessed in the study. Further experimentation extends the initial inquiry by exploring the capabilities of Long Short-Term Memory (LSTM) models augmented by attention layers. In this extension, the best models demonstrate an accuracy of 99.83%, surpassing the original RNN models. These findings underscore the crucial importance of refining machine learning models and emphasize the potential for substantial advancements in healthcare through innovative algorithmic approaches. [ABSTRACT FROM AUTHOR]

PURPOSE: Crop diseases can cause significant reductions in yield, subsequently impacting a country's economy. The current research is concentrated on detecting diseases in three specific crops – tomatoes, soybeans, and mushrooms, using a real-time dataset collected for tomatoes and two publicly accessible datasets for the other crops. The primary emphasis is on employing datasets with exclusively categorical attributes, which poses a notable challenge to the research community. METHODS: After applying label encoding to the attributes, the datasets undergo four distinct preprocessing techniques to address missing values. Following this, the SMOTE-N technique is employed to tackle class imbalance. Subsequently, the pre-processed datasets are subjected to classification using three ensemble methods: bagging, boosting, and voting. To further refine the classification process, the metaheuristic Ant Lion Optimizer (ALO) is utilized for hyper-parameter tuning. RESULTS: This comprehensive approach results in the evaluation of twelve distinct models. The top two performers are then subjected to further validation using ten standard categorical datasets. The findings demonstrate that the hybrid model II-SN-OXGB, surpasses all other models as well as the current state-of-the-art in terms of classification accuracy across all thirteen categorical datasets. II utilizes the Random Forest classifier to iteratively impute missing feature values, employing a nearest features strategy. Meanwhile, SMOTE-N (SN) serves as an oversampling technique particularly for categorical attributes, again utilizing nearest neighbors. Optimized (using ALO) Xtreme Gradient Boosting OXGB, sequentially trains multiple decision trees, with each tree correcting errors from its predecessor. CONCLUSION: Consequently, the model II-SN-OXGB emerges as the optimal choice for addressing classification challenges in categorical datasets. Applying the II-SN-OXGB model to crop datasets can significantly enhance disease detection which in turn, enables the farmers to take timely and appropriate measures to prevent yield losses and mitigate the economic impact of crop diseases. [ABSTRACT FROM AUTHOR]

A great understanding of the fracture behavior of rocks is very important for ensuring the successful and efficient design, implementation, completion, and structural stability of important large-scale mining, tunneling, oil and gas, and civil engineering projects. This study employed four soft computing models of an artificial neural network trained using the Levenberg–Marquardt algorithm (ANN–LM), grasshopper optimization algorithm-optimized ANN (ANN–GOA), salp swarm algorithm-optimized ANN (ANN–SSA), and arithmetic operation algorithm-optimized ANN (ANN–AOA) to predict the Mode-I fracture toughness (KIc) of rock. For this purpose, a database comprising 121 experimental datasets obtained from the KIc test on a semi-circular bend (SCB) rock samples were used to train and validate the models. Four important parameters affecting KIc, namely, the uniaxial tensile strength, disc specimen radius and thickness, and notch or crack length, were selected as the input parameters. The ANN–GOA 4-9-1 model was adjudged to be the optimum of the generated KIc models as determined by the error metrics used to evaluate model performance. The ANN–GOA 4-9-1 had the lowest error metrics and highest coefficient of correlation for the overall dataset, with R = 0.98498, MSE = 0.0036, VAF = 97.02%, and a20-index = 0.96694. To ensure easy implementation of the optimum ANN–GOA 4-9-1, the model was transformed into a tractable closed-form explicit equation. Furthermore, the impact of each of the four KIc effective parameters on predicted KIc is evaluated and the Brazilian tensile strength and rock specimen radius are determined to be the most sensitive parameters to KIc. Hence, the proposed models can provide a robust and functional reliable alternative to the laborious and costly experimental method for the determination of KIc of rocks. Highlights: An ANN-based model for KIc of SCB specimen prediction is presented. 121 experimental data of SCB specimen was used for model development. Transformation of the optimal ANN-based model into closed-form equation. Variable importance analysis was performed to evaluate the impacts of predictors. [ABSTRACT FROM AUTHOR]

Polynomials of a degree higher than 8th used for the rise phase of displacement curve of a cam profile were optimized for gaining minimal jerk using the search and rescue optimization algorithm. The algorithm was used to obtain the polynomial coefficients for the polynomials of the higher degree that cannot be calculated by using the analytical approach without adding additional boundary conditions other than displacement, velocity and acceleration in characteristic positions. The degree of the polynomial from which the optimal value of jerk using the optimization algorithm can be obtained was determined, as well as the degree after which the results of the optimization give worse values of the jerk. [ABSTRACT FROM AUTHOR]

Shear strength parameters play a crucial role in the design and construction of rock slopes, underground openings, tunnels, excavations, and foundations. Determination of cohesion (c) and angle of internal friction (φ) through laboratory tests (e.g., triaxial tests) and in situ tests is time-consuming and expensive; therefore, it is valuable to accurately predict the shear strength parameters of rock materials using less expensive and more reliable methods. In this study, 4 artificial intelligence models, namely group method of data handling (GMDH), gene expression programming (GEP), bidirectional long short-term memory network (BILSTM), and random forest (RF), are proposed to predict the rock shear strength parameters c and φ. A database of 199 sets of experimental data from 4 rock types was used to construct the proposed models. The results show that the accuracy of the RF model is superior to the other three models, with coefficients of determination of 0.9933 (c) and 0.9727 (φ) on all datasets, respectively. To further improve the prediction accuracy of the RF model, six metaheuristic algorithms, namely particle swarm optimization (PSO), bald eagle search (BES), marine predators algorithm (MPA), northern goshawk optimization (NGO), golden jackal optimization (GJO), and dung beetle optimizer (DBO), were used to optimize the hyperparameters of the model. The results show that the accuracy of the six hybrid RF models is higher than that of the single RF model. Among the 6 hybrid RF models, the hybrid DBO–RF model is superior to the other hybrid RF models. In addition, the factors affecting the shear strength of rock materials are analyzed through the parameter sensitivity analysis. Highlights: Four artificial intelligence models are used to predict the shear strength parameters of rock materials. Six metaheuristic algorithms are used to optimize the hyperparameters of the random forest model. The prediction performance of the six hybrid random forest models is compared. The factors influencing the shear strength of rock materials are analyzed by parameter sensitivity analysis. [ABSTRACT FROM AUTHOR]

Artificial neural networks are amongst the artificial intelligence techniques with their ability to provide machines with some functionalities such as decision making, comparison, and forecasting. They are known for having the capability of forecasting issues in real-world problems. Their acquired knowledge is stored in the interconnection strengths or weights of neurons through an optimization system known as learning. Several limitations have been identified with commonly used gradient-based optimization algorithms, including the risk of premature convergence, the sensitivity of initial parameters and positions, and the potential for getting trapped in local optima. Various meta-heuristics are proposed in the literature as alternative training algorithms to mitigate these limitations. Therefore, the primary aim of this study is to combine a feed-forward artificial neural network (ANN) with a cultural algorithm (CA) as a meta-heuristic, aiming to establish an efficient and dependable training system in comparison to existing methods. The proposed artificial neural network system (ANN-CA) evaluated its performance on classification tasks over nine benchmark datasets: Iris, Pima Indians Diabetes, Thyroid Disease, Breast Cancer Wisconsin, Credit Approval, Glass Identification, SPECT Heart, Wine and Balloon. The overall experimental results indicate that the proposed method outperforms other methods included in the comparative analysis by approximately 12% in terms of classification error and approximately 7% in terms of accuracy. [ABSTRACT FROM AUTHOR]

Offering Product-Service Systems (PSS) becomes an established strategy for companies to increase the provided customer value and ensure their competitiveness. Designing PSS business models, however, remains a major challenge. One reason for this is the fact that PSS business models are characterized by a long-term nature. Decisions made in the development phase must take into account possible scenarios in the operational phase. Risks must already be anticipated in this phase and mitigated with appropriate measures. Another reason for the design phase being a major challenge is the size of the solution space for a possible business model. Developers are faced with a multitude of possible business models and have the challenge of selecting the best one. In this article, a simheuristic optimization approach is developed to test and evaluate PSS business models in the design phase in order to select the best business model configuration beforehand. For optimization, a proprietary evolutionary algorithm is developed and tested. The results validate the suitability of the approach for the design phase and the quality of the algorithm for achieving good results. This could even be transferred to already established PSS. [ABSTRACT FROM AUTHOR]

In the last few years, the use of convolutional neural networks (CNNs) in intrusion detection domains has attracted more and more attention. However, their results in this domain have not lived up to expectations compared to the results obtained in other domains, such as image classification and video analysis. This is mainly due to the datasets used, which contain preprocessed features that are not compatible with convolutional neural networks, as they do not allow a full exploit of all the information embedded in the original network traffic. With the aim of overcoming these issues, we propose in this paper a new efficient convolutional neural network model for network intrusion detection based on raw traffic data (pcap files) rather than preprocessed data stored in CSV files. The novelty of this paper lies in the proposal of a new method for adapting the raw network traffic data to the most suitable format for CNN models, which allows us to fully exploit the strengths of CNNs in terms of pattern recognition and spatial analysis, leading to more accurate and effective results. Additionally, to further improve its detection performance, the structure and hyperparameters of our proposed CNN-based model are automatically adjusted using the self-adaptive differential evolution (SADE) metaheuristic, in which symmetry plays an essential role in balancing the different phases of the algorithm, so that each phase can contribute in an equal and efficient way to finding optimal solutions. This helps to make the overall performance more robust and efficient when solving optimization problems. The experimental results on three datasets, KDD-99, UNSW-NB15, and CIC-IDS2017, show a strong symmetry between the frequency values implemented in the images built for each network traffic and the different attack classes. This was confirmed by a good predictive accuracy that goes well beyond similar competing models in the literature. [ABSTRACT FROM AUTHOR]

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This paper presents an optimal vehicle routing model for an efficient waste collection process using the Ogun State Waste Management Agency (OGWAMA) as a case study. Just like in many cases, the current manual predetermined routing method used by OGWAMA is inefficient and contributes to excessive fuel usage. These challenges, in addition to the small instances reported in most literature, inspire this research to propose an improved routing scheme that takes into account real-time costs and eventually develops a novel instance based on OGWAMA's operation mode. The developed model was optimized using a new discrete smell agent optimization (SAO) algorithm and compared to firefly algorithm (FA) and particle swarm optimization (PSO). The SAO outperformed FA and PSO, achieving 3.92 % and 19.38 % improvements in service cost (SC) and 2.65 % and 14.96 % improvements in total travel distance (TTD), respectively. The convergence rates of the algorithms were also compared; using the Optimized Depot (OD) techniques and results shows the acceptability of the proposed approaches.