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This manuscript introduces an improved Cuckoo Search (CS) algorithm, known as BASCS, designed to address the inherent limitations of CS, including insufficient search space coverage, premature convergence, low search accuracy, and slow search speed. The proposed improvements encompass four main areas: the integration of tent chaotic mapping and random migration in population initialization to reduce the impact of random errors, the guidance of Levy flight by the directional determination strategy of the Beetle Antennae Search (BAS) algorithm during the global search phase to improve search accuracy and convergence speed, the adoption of the Sine Cosine Algorithm for local exploitation in later iterations to enhance local optimization and accuracy, and the adaptive adjustment of the step-size factor and elimination probability throughout the iterative process to convergence. The performance of BASCS is validated through ablation experiments on 10 benchmark functions, comparative experiments with the original CS and its four variants, and application to a robot path planning problem. The results demonstrate that BASCS achieves higher convergence accuracy and exhibits faster convergence speed and superior practical applicability compared to other algorithms. [ABSTRACT FROM AUTHOR]

The whale optimization algorithm (WOA) is constructed on a whale's bubble-net scavenging pattern and emulates encompassing prey, bubble-net devouring prey, and stochastic capturing for prey to establish the global optimal values. Nevertheless, the WOA has multiple deficiencies, such as restricted precision, sluggish convergence acceleration, insufficient population variety, easy premature convergence, and restricted operational efficiency. The sine cosine algorithm (SCA) constructed on the oscillation attributes of the cosine and sine coefficients in mathematics is a stochastic optimization methodology. The SCA upgrades population variety, amplifies the search region, and accelerates international investigation and regional extraction. Therefore, a hybrid nonlinear WOA with SCA (SCWOA) is emphasized to estimate benchmark functions and engineering designs, and the ultimate intention is to investigate reasonable solutions. Compared with other algorithms, such as BA, CapSA, MFO, MVO, SAO, MDWA, and WOA, SCWOA exemplifies a superior convergence effectiveness and greater computation profitability. The experimental results emphasize that the SCWOA not only integrates investigation and extraction to avoid premature convergence and realize the most appropriate solution but also exhibits superiority and practicability to locate greater computation precision and faster convergence speed. [ABSTRACT FROM AUTHOR]

In a deregulated power system, managing congestion is crucial for effective operation and control. The goal of congestion management is to alleviate transmission line congestion while adhering to system constraints at minimal cost. This research proposes a hybrid Harris Hawk Optimization–Sine Cosine Algorithm (hHHO-SCA) for an efficient generation rescheduling approach to achieve the lowest possible congestion cost. The hybridization has been performed by introducing the features of SCA in the HHO to boost the exploration and exploitation steps of HHO, providing an efficient global solution and effectively optimizing rescheduled power output. The effectiveness of this methodology is evaluated using IEEE 30 and IEEE 118-bus test systems, taking into account system parameters. The potency of the proposed method is analyzed by comparing the results of the hHHO-SCA with those from other recent optimization techniques. The findings show that the hHHO-SCA outperforms other methods by avoiding local optima and demonstrating promising convergence characteristics. [ABSTRACT FROM AUTHOR]

This work uses the vector-valued Bernoulli equation to build a nonlinear multi-variable grey Bernoulli model, which is available to describe the nonlinear relationship between the output variables. By using P a d e ´ approximation, the proposed model can be implemented with high time efficiency. Additionally, the Sine Cosine Algorithm is employed to determine the Bernoulli exponent, thereby enhancing prediction accuracy. To evaluate the predictive performance of the proposed model, three case studies using three real-world data sets with different features of predicting per capita household income, fuel prices and crude oil prices are carried out. The results are compared with three existing grey multi-input multi-output models. Experimental results demonstrate that the proposed model excels in handling nonlinear relationships between variables and has strong robustness against noise, consistently delivering lower error values, demonstrating superior predictive performance. • A nonlinear multi-variable grey Bernoulli model is proposed based on the Hadamard product. • Linear parameter estimation is carried out using the least squares method or the least norm method. • P a d e ´ approximation is used to compute the matrix exponential to improve the time efficiency. • The model is optimized using the Sine Cosine Algorithm. • The proposed model shows superior prediction performance compared with three other typical models. [ABSTRACT FROM AUTHOR]

This paper explores advanced machine learning approaches to enhance the prediction accuracy of compressive strength (CoS) in geopolymer composites (GePC). Geopolymers, as sustainable alternatives to Ordinary Portland Cement (OPC), offer significant environmental benefits by utilizing industrial by-products such as fly ash and ground granulated blast furnace slag (GGBS). The accurate prediction of their compressive strength is crucial for optimizing their mix design and reducing experimental efforts. We present a comparative analysis of two hybrid models, Harris Hawks Optimization with Random Forest (HHO-RF) and Sine Cosine Algorithm with Random Forest (SCA-RF), against traditional regression methods and classical models like the Extreme Learning Machine (ELM), General Regression Neural Network (GRNN), and Radial Basis Function (RBF). Using a comprehensive dataset derived from various scientific publications, we focus on key input variables including the fine aggregate, GGBS, fly ash, sodium hydroxide (NaOH) molarity, and others. Our results indicate that the SCA-RF model achieved a superior performance with a root mean square error (RMSE) of 1.562 and a coefficient of determination (R2) of 0.987, compared to the HHO-RF model, which obtained an RMSE of 1.742 and an R2 of 0.982. Both hybrid models significantly outperformed traditional methods, demonstrating their higher accuracy and reliability in predicting the compressive strength of GePC. This research underscores the potential of hybrid machine learning models in advancing sustainable construction materials through precise predictive modeling, paving the way for more environmentally friendly and efficient construction practices. [ABSTRACT FROM AUTHOR]

Sine cosine algorithm (SCA) is a recently developed meta-heuristic method based on the characteristics of sine and cosine functions. However, SCA algorithm may suffer premature convergence in solving complex optimization problems. To mitigate this limitation, a modified SCA (MSCA) is developed to achieve an effective balance between exploration and exploitation. Specifically, the conversion parameter can be configured adaptively by using the individual fitness information in the evolution process to guide the individual search behavior. After that, an inertia weight is embedded in the position updating equation to further improve the search accuracy and accelerate the convergence speed. The performance of MSCA algorithm is investigated on 13 benchmark functions and the chaotic time series prediction problem. Experimental results demonstrate that MSCA algorithm is more effective than the other optimization methods. [ABSTRACT FROM AUTHOR]

Artificial neural networks (ANNs) is widely adopted by researchers for classification tasks due to their simplicity and superior performance. This study offerings the ANN and it variant such as Elman Neural Network (NN) model to address its strengths, although it faces with issues like local minima and slow convergence. This study presents a comprehensive evaluation of four distinct algorithms for classification tasks, focusing on their performance on both training and testing datasets. These algorithms such as Sine Cosine Algorithm is integrated with Artificial Neural Networks (SCA_ANN), Back Propagation Neural Networks (SCA_BP), Elman Neural Networks (SCA_ElmanNN), and Elman Neural Networks (ElmanNN). The evaluation employs two key performance metrics: Accuracy (ACC) and Mean Squared Error (MSE). The training dataset, representing 70% of the data, is used for algorithm training, and the testing dataset, constituting the remaining 30%, assesses the algorithms' ability to generalize to new, unseen data. Results indicate that SCA_ElmanNN in both training and testing datasets, achieving high accuracy and minimal MSE, showcasing its proficiency in classification and prediction precision. SCA_BP and SCA_ANN also demonstrate robust performance. Conversely, ElmanNN, while relatively accurate, exhibits a slightly higher MSE on the testing data, indicating some variability in its predictions. These findings offer valuable insights for researchers in selecting the most appropriate algorithm for specific classification tasks. [ABSTRACT FROM AUTHOR]

Artificial neural networks (ANNs) is widely adopted by researchers for classification tasks due to their simplicity and superior performance. This study offerings the ANN and it variant such as Elman Neural Network (NN) model to address its strengths, although it faces with issues like local minima and slow convergence. This study presents a comprehensive evaluation of four distinct algorithms for classification tasks, focusing on their performance on both training and testing datasets. These algorithms such as Sine Cosine Algorithm is integrated with Artificial Neural Networks (SCA_ANN), Back Propagation Neural Networks (SCA_BP), Elman Neural Networks (SCA_ElmanNN), and Elman Neural Networks (ElmanNN). The evaluation employs two key performance metrics: Accuracy (ACC) and Mean Squared Error (MSE). The training dataset, representing 70% of the data, is used for algorithm training, and the testing dataset, constituting the remaining 30%, assesses the algorithms' ability to generalize to new, unseen data. Results indicate that SCA_ElmanNN in both training and testing datasets, achieving high accuracy and minimal MSE, showcasing its proficiency in classification and prediction precision. SCA_BP and SCA_ANN also demonstrate robust performance. Conversely, ElmanNN, while relatively accurate, exhibits a slightly higher MSE on the testing data, indicating some variability in its predictions. These findings offer valuable insights for researchers in selecting the most appropriate algorithm for specific classification tasks.

Abstract Researchers are increasingly focusing on renewable energy due to its high reliability, energy independence, efficiency, and environmental benefits. This paper introduces a novel multi-objective framework for the short-term scheduling of microgrids (MGs), which addresses the conflicting objectives of minimizing operating expenses and reducing pollution emissions. The core contribution is the development of the Chaotic Self-Adaptive Sine Cosine Algorithm (CSASCA). This algorithm generates Pareto optimal solutions simultaneously, effectively balancing cost reduction and emission mitigation. The problem is formulated as a complex multi-objective optimization task with goals of cost reduction and environmental protection. To enhance decision-making within the algorithm, fuzzy logic is incorporated. The performance of CSASCA is evaluated across three scenarios: (1) PV and wind units operating at full power, (2) all units operating within specified limits with unrestricted utility power exchange, and (3) microgrid operation using only non-zero-emission energy sources. This third scenario highlights the algorithm’s efficacy in a challenging context not covered in prior research. Simulation results from these scenarios are compared with traditional Sine Cosine Algorithm (SCA) and other recent optimization methods using three test examples. The innovation of CSASCA lies in its chaotic self-adaptive mechanisms, which significantly enhance optimization performance. The integration of these mechanisms results in superior solutions for operation cost, emissions, and execution time. Specifically, CSASCA achieves optimal values of 590.45 €ct for cost and 337.28 kg for emissions in the first scenario, 98.203 €ct for cost and 406.204 kg for emissions in the second scenario, and 95.38 €ct for cost and 982.173 kg for emissions in the third scenario. Overall, CSASCA outperforms traditional SCA by offering enhanced exploration, improved convergence, effective constraint handling, and reduced parameter sensitivity, making it a powerful tool for solving multi-objective optimization problems like microgrid scheduling.

Demand response (DR) refers to the customers' active reaction with respect to the changes of market pricing or incentive policies. DR plays an important role in improving network reliability, minimizing operational cost and increasing end users' benefits. Hence, the integration of DR in the microgrid (MG) management is gaining increasing popularity nowadays. This paper proposes a day-ahead MG scheduling framework in conjunction with DR and investigates the impact of DR in optimizing load profile and reducing overall power generation costs. A linear responsive model considering time of use (TOU) price and incentive is developed to model the active reaction of customers' consumption behaviors. Thereafter, a novel multi-swarm sine cosine algorithm (MSCA) is proposed to optimize the total power generation costs in the framework. In the proposed MSCA, several sub-swarms search for better solutions simultaneously which is beneficial for improving the population diversity. A cooperative learning scheme is developed to realize knowledge dissemination in the population and a competitive substitution strategy is proposed to prevent local optima stagnation. The simulation results obtained by the proposed MSCA are compared with other meta-heuristic algorithms to show its effectiveness in reducing overall generation costs. The outcomes with and without DR suggest that the DR program can effectively reduce the total generation costs and improve the stability of the MG network. [ABSTRACT FROM AUTHOR]

Cancer of the breast is a deadly disease that can take a person's life in many different ways. Predicting breast cancer at an early stage is crucial in the fight to end the disease. The usage of deep learning and blockchain technology has been implemented with the intention of integrating optimal prediction with clinical diagnostics and protecting private health information. Patients' medical records are encrypted and stored in the blockchain for maximum safety. As a result, a large portion of time and energy is spent on feature engineering, a tedious process that requires prior expert domain knowledge of the data to develop effective features, and is crucial to the success of most conventional classification systems. Deep learning, on the other hand, can arrange the discriminative information in the data without the need for a domain expert to develop feature extractors. The research community and industry have paid attention to deep, feedforward networks like convolutional neural networks (CNNs) because of their empirical results in areas including speech recognition, signal processing, object recognition, natural language processing, and transfer learning. For the best breast cancer prediction, a new method called a "Hybrid CNN" combining the Sine Cosine Algorithm (SCA) with a transfer learning algorithm has been presented. Mini-batch size and drop-out rate are just two of the factors that the SCA algorithm may fine-tune. To stop the model from overfitting, we employ a transfer learning strategy. The hyperparameters found using sine cosine algorithm is used in Visual geometry Group (VGG 16) architecture. ImageNet is used to pretrain the network and last three convolutional layers are trained using transfer learning. The integration of sine cosine algorithm and transfer learning together increases the accuracy thereby preventing the model from overfitting. The experimentation is performed in Google Colab and the proposed Hybrid CNN is compared with existing methodologies such as K-NN, SVM. Also, the proposed Hybrid CNN is compared with CNN without transfer learning and CNN without SCA. The metrics taken into account for comparison are accuracy, sensitivity, specificity, F-score. The proposed Hybrid CNN achieves 96.9% accuracy that shows the effectiveness of the integration of SCA and transfer learning. [ABSTRACT FROM AUTHOR]

The Sine–Cosine algorithm (SCA) is efficient but faces challenges in exploitative abilities, slow convergence, and exploration–exploitation balance. This study proposes a novel optimization method, the learning-based sine–cosine algorithm (L-SCA), to solve the optimal power flow (OPF) problem. The basic SCA has been modified with a learning phase operator inspired by TLBO. The SCA handles global exploration, while the learner phase of teaching–learning based optimization (TLBO) offers strong local search capabilities, which can be utilized to enhance the solution neighborhood space provided by the SCA technique. The L-SCA and original SCA algorithms address OPF in IEEE 57-bus, Algerian 59-bus, and IEEE 118-bus power systems, considering twelve cases with a focus on cost savings, voltage stability, voltage profile, emissions, and power losses. The comparative study shows that the proposed L-SCA consistently outperforms standard SCA and other reported methods in all cases for varied-scale standard test systems as well as for a practical power system, within reasonable execution times. For instance, L-SCA in the Algerian 59-bus system cut fuel costs by around 13.13% compared to initial case, equating to annual savings of $2.2 million, while in the IEEE-118 bus system, power loss is significantly reduced to 17.881 MW, marking an 86.5% reduction compared to the base case. [ABSTRACT FROM AUTHOR]

As a significant branch of production scheduling problem, the Flexibility Job Shop Scheduling Problem (FJSP) is a typical NP-hard problem. Most conventional flexible workshop scheduling primarily focuses on performance aspects involving production efficiency such as time and quality. In recent years, due to increased energy costs and environmental pollution, 'low-carbon scheduling' has garnered attention as a new scheduling paradigm among scholars and engineers. This paper investigates a low-carbon flexible Job shop scheduling problem, proposing a Grey Wolf Optimization algorithm (SC-GWO), aiming to minimize the sum of carbon emission costs and makespan costs. This algorithm employs the Grey Wolf Algorithm (GWO) as the fundamental optimization method, adaptively choosing between global and local searches based on the dispersion degree of individuals. Firstly, integrating the Sine Cosine Algorithm (SCA), the sinusoidal cosine search mechanism is applied to GWO to enhance its local search capability. Secondly, a new leader selection mechanism is introduced to prevent leaders from falling into local optima, thus improving the algorithm's global exploration capability. Utilizing a nonlinear convergence factor strategy controls the global exploration and local exploitation capabilities in different algorithm stages, enhancing optimization accuracy and accelerating convergence, achieving a dynamic balance between the two. Finally, validation of the SC-GWO algorithm's ability to solve low-carbon scheduling problems in flexible job shop scheduling instances is conducted. Experimental results demonstrate the superior performance of SC-GWO in solving low-carbon flexible workshop scheduling instances. Comparative experiments against four other advanced algorithms on 22 classic benchmark test functions confirm SC-GWO's better convergence. Through standard test functions like Bandimarte instances applied to solve FJSP, experimental results showcase the excellent optimization performance of SC-GWO. Compared to HGWO and GWO, the makespan time is reduced by 22.25% and 39.27%, respectively. The proposed SC-GWO algorithm demonstrates favorable solving effects on flexible job shop scheduling instances, meeting actual production scheduling needs. [ABSTRACT FROM AUTHOR]

The sine cosine algorithm (SCA) and multi-verse optimizer (MVO) are the recognized optimization strategies frequently employed in numerous scientific areas. However, both SCA and MVO grapple with optimizing the transition between the exploitation and exploration mechanisms. Furthermore, MVO exhibits constraints in its exploitation capabilities. To tackle these limitations, this paper introduces a hybrid model termed SMVO, combining the advantages of both SCA and MVO. This hybrid approach seeks to harmonize exploitation and exploration stages by leveraging the unique advantages of each parent algorithm. The efficacy of SMVO was assessed using 23 benchmark test functions, revealing its competitive performance against not only SCA and MVO but also the ant lion optimization (ALO) and the dragonfly algorithm (DA). Additionally, SMVO's applicability was further validated by successfully addressing three distinct engineering optimization challenges, underscoring its stability and promise as a global optimization tool. [ABSTRACT FROM AUTHOR]

The health of rolling bearing is of great importance for the normal operation of rotating machinery. The fault diagnosis process can be roughly summarized as signal processing, feature extraction, and fault classification. In this paper, a novel feature extraction and fault diagnosis method with fractional order back-propagation neural network is put forward. The new sine cosine algorithm optimized variational mode decomposition is performed on vibration signals, and the fault feature vectors are selected and built by singular value decomposition. Inspired by the fractional order calculus, a fractional order back-propagation neural network is employed to realize fault classification. The capability of the developed fault diagnosis algorithm is comprehensively evaluated via benchmark bearing data. The experimental results demonstrate that the designed method substantially extracts bearing defect features, increases classification accuracy and efficiency, and also outperforms existing algorithms. [ABSTRACT FROM AUTHOR]

Particle Swarm Optimization (PSO) is facing more challenges in solving high-dimensional global optimization problems. In order to overcome this difficulty, this paper proposes a novel PSO variant of the hybrid Sine Cosine Algorithm (SCA) strategy, named Velocity Four Sine Cosine Particle Swarm Optimization (VFSCPSO). The introduction of the SCA strategy in the velocity formulation ensures that the global optimal solution is found accurately. It increases the flexibility of PSO. A series of experiments are conducted on the CEC2005 test suite with compositional algorithms, algorithmic variants, and good intelligent algorithms. The experimental results show that the algorithm effectively improves the overall performance of compositional algorithms; the Friedman test proves that the algorithm has good competitiveness. The algorithm also performs better in PID parameter tuning. Therefore, the VFSCPSO is able to solve the high-dimensional global optimization problems in a better way. [ABSTRACT FROM AUTHOR]

An IDS (Intrusion Detection System) is essential for network security experts, as it allows one to identify and respond to abnormal traffic present in a network. An IDS can be utilized for evaluating the various types of malicious attacks. Hence, detecting intrusions has become a significant research area in the contemporary era, especially with the evolution of technologies. With the progress of ML (Machine Learning)-based algorithms, researchers have striven to perform optimal ID. However, most of these studies lag in accordance with their accuracy rate. Thus, to attain a high accuracy rate in ID, the present study proposes ML-based meta-heuristic algorithms, as these approaches possess innate merits of determining near-optimal solutions in limited time and are capable of dealing with multi-dimensional data. The study proposes OWSA (Optimal Whale Sine Algorithm) for selecting suitable and relevant features. With an exclusive optimization process using the SCA (Sine Cosine Algorithm), this study proposes to combine SCA with WOA (Whale Optimization Algorithm) for mitigating the demerits of both, with its hybridization thereby achieving OWSA. Following this, AWRF (Artificial Neural Network Weighted Random Forest) is proposed for classification. The main intention of this process is to propose a weight-updating process for discrete trees in the RF model. The proposed approach is motivated by avoiding overfitting and attaining stability and flexibility. This approach is assessed with regard to performance via a comparative analysis, so as to uncover the best performance of this proposed technique in ID. [ABSTRACT FROM AUTHOR]

Class imbalance is a significant study problem that is biased, exhibiting excellent performance toward the majority classes in the dataset while showing inferior performance toward minority classes. When dealing with real-world issues, this kind of biased nature affects classification accuracy. The Improved Binary Sine Cosine Algorithm (IBSCA) has been used in this work to identify a subset of the majority class in the best possible way. The proposed IBSCA makes some enhancements over the conventional Binary Sine Cosine Algorithm (BSCA) to address the issue of premature convergence with local optimal solutions. To improve classification accuracy for unbalanced datasets, the proposed IBSCA seeks to identify the optimal collection of instances from the majority class. The advised IBSCA makes use of the alpha agent, beta agent, and random agent’s location, which tends to devote considerable time to exploration to find the best possible set of instances. By using the geometric mean (G-mean) and F-score to describe the fitness function, the proposed IBSCA aims to solve the multi-objective optimization issue. On 18 datasets with different imbalance ratios taken from the KEEL repository, experimentation is conducted. Comparisons are made between the suggested IBSCA and the traditional Binary Sine Cosine Algorithm, Binary Particle Swarm Optimization (BPSO), and Binary Grey Wolf Optimization (BGWO). Additionally, the performance of the suggested IBSCA is evaluated against the top outcomes from different research papers. Metrics like sensitivity, F-score, G-mean, and area under curve (AUC) show that the suggested IBSCA outperforms the state-of-the-art algorithms. The statistical findings using the Wilcoxon signed rank test and Friedman test also demonstrate that the suggested IBSCA is more efficient than the other conventional algorithms.

This manuscript introduces an improved Cuckoo Search (CS) algorithm, known as BASCS, designed to address the inherent limitations of CS, including insufficient search space coverage, premature convergence, low search accuracy, and slow search speed. The proposed improvements encompass four main areas: the integration of tent chaotic mapping and random migration in population initialization to reduce the impact of random errors, the guidance of Levy flight by the directional determination strategy of the Beetle Antennae Search (BAS) algorithm during the global search phase to improve search accuracy and convergence speed, the adoption of the Sine Cosine Algorithm for local exploitation in later iterations to enhance local optimization and accuracy, and the adaptive adjustment of the step-size factor and elimination probability throughout the iterative process to convergence. The performance of BASCS is validated through ablation experiments on 10 benchmark functions, comparative experiments with the original CS and its four variants, and application to a robot path planning problem. The results demonstrate that BASCS achieves higher convergence accuracy and exhibits faster convergence speed and superior practical applicability compared to other algorithms.

The whale optimization algorithm (WOA) is constructed on a whale’s bubble-net scavenging pattern and emulates encompassing prey, bubble-net devouring prey, and stochastic capturing for prey to establish the global optimal values. Nevertheless, the WOA has multiple deficiencies, such as restricted precision, sluggish convergence acceleration, insufficient population variety, easy premature convergence, and restricted operational efficiency. The sine cosine algorithm (SCA) constructed on the oscillation attributes of the cosine and sine coefficients in mathematics is a stochastic optimization methodology. The SCA upgrades population variety, amplifies the search region, and accelerates international investigation and regional extraction. Therefore, a hybrid nonlinear WOA with SCA (SCWOA) is emphasized to estimate benchmark functions and engineering designs, and the ultimate intention is to investigate reasonable solutions. Compared with other algorithms, such as BA, CapSA, MFO, MVO, SAO, MDWA, and WOA, SCWOA exemplifies a superior convergence effectiveness and greater computation profitability. The experimental results emphasize that the SCWOA not only integrates investigation and extraction to avoid premature convergence and realize the most appropriate solution but also exhibits superiority and practicability to locate greater computation precision and faster convergence speed.

This paper explores advanced machine learning approaches to enhance the prediction accuracy of compressive strength (CoS) in geopolymer composites (GePC). Geopolymers, as sustainable alternatives to Ordinary Portland Cement (OPC), offer significant environmental benefits by utilizing industrial by-products such as fly ash and ground granulated blast furnace slag (GGBS). The accurate prediction of their compressive strength is crucial for optimizing their mix design and reducing experimental efforts. We present a comparative analysis of two hybrid models, Harris Hawks Optimization with Random Forest (HHO-RF) and Sine Cosine Algorithm with Random Forest (SCA-RF), against traditional regression methods and classical models like the Extreme Learning Machine (ELM), General Regression Neural Network (GRNN), and Radial Basis Function (RBF). Using a comprehensive dataset derived from various scientific publications, we focus on key input variables including the fine aggregate, GGBS, fly ash, sodium hydroxide (NaOH) molarity, and others. Our results indicate that the SCA-RF model achieved a superior performance with a root mean square error (RMSE) of 1.562 and a coefficient of determination (R2) of 0.987, compared to the HHO-RF model, which obtained an RMSE of 1.742 and an R2 of 0.982. Both hybrid models significantly outperformed traditional methods, demonstrating their higher accuracy and reliability in predicting the compressive strength of GePC. This research underscores the potential of hybrid machine learning models in advancing sustainable construction materials through precise predictive modeling, paving the way for more environmentally friendly and efficient construction practices.

In a deregulated power system, managing congestion is crucial for effective operation and control. The goal of congestion management is to alleviate transmission line congestion while adhering to system constraints at minimal cost. This research proposes a hybrid Harris Hawk Optimization–Sine Cosine Algorithm (hHHO-SCA) for an efficient generation rescheduling approach to achieve the lowest possible congestion cost. The hybridization has been performed by introducing the features of SCA in the HHO to boost the exploration and exploitation steps of HHO, providing an efficient global solution and effectively optimizing rescheduled power output. The effectiveness of this methodology is evaluated using IEEE 30 and IEEE 118-bus test systems, taking into account system parameters. The potency of the proposed method is analyzed by comparing the results of the hHHO-SCA with those from other recent optimization techniques. The findings show that the hHHO-SCA outperforms other methods by avoiding local optima and demonstrating promising convergence characteristics.

Chaotic maps are sources of randomness formed by a set of rules and chaotic variables. They have been incorporated into metaheuristics because they improve the balance of exploration and exploitation, and with this, they allow one to obtain better results. In the present work, chaotic maps are used to modify the behavior of the binarization rules that allow continuous metaheuristics to solve binary combinatorial optimization problems. In particular, seven different chaotic maps, three different binarization rules, and three continuous metaheuristics are used, which are the Sine Cosine Algorithm, Grey Wolf Optimizer, and Whale Optimization Algorithm. A classic combinatorial optimization problem is solved: the 0-1 Knapsack Problem. Experimental results indicate that chaotic maps have an impact on the binarization rule, leading to better results. Specifically, experiments incorporating the standard binarization rule and the complement binarization rule performed better than experiments incorporating the elitist binarization rule. The experiment with the best results was STD_TENT, which uses the standard binarization rule and the tent chaotic map. [ABSTRACT FROM AUTHOR]

To address the shortcomings of the sine cosine algorithm such as the low search accuracy, slow convergence speed, and easily falling into local optimality, a sine cosine algorithm for elite individual collaborative search was proposed. Firstly, tent chaotic mapping was used to initialize the population and the hyperbolic tangent function was applied non-linearly to adjust the parameters of the sine cosine algorithm, which enhanced the uniformity of population distribution and balanced the global exploration and local exploitation ability. Secondly, the search method of the sine cosine algorithm was improved by combining the search strategy of the sine cosine algorithm, the m-neighborhood locally optimal individual-guided search strategy, and the global optimal individual-guided search strategy, and, then, the three search strategies were executed alternately, which achieved collaboration, improved the convergence accuracy, and prevented the algorithm from falling into local optima. Finally, a greedy selection strategy was employed to select the best individuals for the population, which accelerated the convergence speed of the sine cosine algorithm. The simulation results illustrated that the sine cosine algorithm for elite individual collaborative search demonstrated a better optimization performance than the sine cosine algorithm, the other improved sine cosine algorithms, the other chaos-based algorithms, and other intelligent optimization algorithms. In addition, the feasibility and applicability of the sine cosine algorithm for elite individual collaborative search were further demonstrated by two mechanical optimization design experiments. [ABSTRACT FROM AUTHOR]

This paper presents an improved Sine Cosine Algorithm (ISCA) towards optimization of a DC-DC buck converter with employment of the proportional-integral-derivative (PID) controller. Limitations of the conventional Sine Cosine Algorithm (SCA) was hereby overcome through two separate alterations which pioneered a synergized employment of nonlinear equation to instrumental mechanism in revising the average location. Primary alteration tackled the issue of local optima by proposed instrumental function towards revision of average location. Secondary alteration then coordinated disproportional exploration and exploitation phases of the conventional SCA by application of a nonlinear equation against the algorithm's decreasing position-updated mechanism. Robustness of the proposed ISCA-PID approach was studied against preceding algorithm-based PID for DC-DC buck converter with respect to their step response, statistics regarding the analyzed objective function, time-domain integral-error performance, reaction to frequency, and resistance to disturbance and parametric uncertainties. Generated findings subsequently uncovered overshadowing efficacy of the proposed method over its algorithmic predecessors towards exceptionally enhanced transitory response of the DC-DC buck converter.

Abstract The sine cosine algorithm (SCA) is widely recognized for its efficacy in solving optimization problems, although it encounters challenges in striking a balance between exploration and exploitation. To improve these limitations, a novel model, termed the novel sine cosine algorithm (nSCA), is introduced. In this advanced model, the roulette wheel selection (RWS) mechanism and opposition-based learning (OBL) techniques are integrated to augment its global optimization capabilities. A meticulous evaluation of nSCA performance has been carried out in comparison with state-of-the-art optimization algorithms, including multi-verse optimizer (MVO), salp swarm algorithm (SSA), moth-flame optimization (MFO), grasshopper optimization algorithm (GOA), and whale optimization algorithm (WOA), in addition to the original SCA. This comparative analysis was conducted across a wide array of 23 classical test functions and 29 CEC2017 benchmark functions, thereby facilitating a comprehensive assessment. Further validation of nSCA utility has been achieved through its deployment in five distinct engineering optimization case studies. Its effectiveness and relevance in addressing real-world optimization issues have thus been emphasized. Across all conducted tests and practical applications, nSCA was found to outperform its competitors consistently, furnishing more effective solutions to both theoretical and applied optimization problems.

Electroencephalography (EEG) serves as a diagnostic technique for measuring brain waves and brain activity. Despite its precision in capturing brain electrical activity, certain factors like environmental influences during the test can affect the objectivity and accuracy of EEG interpretations. Challenges associated with interpretation, even with advanced techniques to minimize artifact influences, can significantly impact the accurate interpretation of EEG findings. To address this issue, artificial intelligence (AI) has been utilized in this study to analyze anomalies in EEG signals for epilepsy detection. Recurrent neural networks (RNNs) are AI techniques specifically designed to handle sequential data, making them well-suited for precise time-series tasks. While AI methods, including RNNs and artificial neural networks (ANNs), hold great promise, their effectiveness heavily relies on the initial values assigned to hyperparameters, which are crucial for their performance for concrete assignment. To tune RNN performance, the selection of hyperparameters is approached as a typical optimization problem, and metaheuristic algorithms are employed to further enhance the process. The modified hybrid sine cosine algorithm has been developed and used to further improve hyperparameter optimization. To facilitate testing, publicly available real-world EEG data is utilized. A dataset is constructed using captured data from healthy and archived data from patients confirmed to be affected by epilepsy, as well as data captured during an active seizure. Two experiments have been conducted using generated dataset. In the first experiment, models were tasked with the detection of anomalous EEG activity. The second experiment required models to segment normal, anomalous activity as well as detect occurrences of seizures from EEG data. Considering the modest sample size (one second of data, 158 data points) used for classification models demonstrated decent outcomes. Obtained outcomes are compared with those generated by other cutting-edge metaheuristics and rigid statistical validation, as well as results' interpretation is performed. [ABSTRACT FROM AUTHOR]

Agriculture assisted by Internet of Things (IoT) is termed as smart agriculture, which offers an increase in precision farming. Soil monitoring with IoT technology helps in the increase of agriculture by growing the yield through measuring accurate soil content information, like temperature, nutrition content, humidity, potential of hydrogen (PH), moisture and so on. In this research, the soil moisture and heat level is measured through an optimized deep learning technique namely, Sine Cosine Horse Herd optimization-based Deep Recurrent Neural Network (SCHHO-based Deep RNN). Here, the moisture and heat level is predicted using Deep RNN in which its weights are trained using SCHHO. In order to progress the effectiveness of prediction, the feature selection is done prior to prediction for choosing the appropriate features using weighted correlation coefficient. In addition, the gathered soil information is transmitted to the IoT nodes using SCHHO routing algorithm by considering fitness measures. Besides, the experimental outcome proves that the SCHHO-based Deep RNN algorithm provides better performance with the accuracy and precision of 0.918 and 0.908, respectively. [ABSTRACT FROM AUTHOR]

In this work, an approach is proposed to solve binary combinatorial problems using continuous metaheuristics. It focuses on the importance of binarization in the optimization process, as it can have a significant impact on the performance of the algorithm. Different binarization schemes are presented and a set of actions, which combine different transfer functions and binarization rules, under a selector based on reinforcement learning is proposed. The experimental results show that the binarization rules have a greater impact than transfer functions on the performance of the algorithms and that some sets of actions are statistically better than others. In particular, it was found that sets that incorporate the elite or elite roulette binarization rule are the best. Furthermore, exploration and exploitation were analyzed through percentage graphs and a statistical test was performed to determine the best set of actions. Overall, this work provides a practical approach for the selection of binarization schemes in binary combinatorial problems and offers guidance for future research in this field. [ABSTRACT FROM AUTHOR]

Particle Swarm Optimization (PSO) is facing more challenges in solving high-dimensional global optimization problems. In order to overcome this difficulty, this paper proposes a novel PSO variant of the hybrid Sine Cosine Algorithm (SCA) strategy, named Velocity Four Sine Cosine Particle Swarm Optimization (VFSCPSO). The introduction of the SCA strategy in the velocity formulation ensures that the global optimal solution is found accurately. It increases the flexibility of PSO. A series of experiments are conducted on the CEC2005 test suite with compositional algorithms, algorithmic variants, and good intelligent algorithms. The experimental results show that the algorithm effectively improves the overall performance of compositional algorithms; the Friedman test proves that the algorithm has good competitiveness. The algorithm also performs better in PID parameter tuning. Therefore, the VFSCPSO is able to solve the high-dimensional global optimization problems in a better way.

An IDS (Intrusion Detection System) is essential for network security experts, as it allows one to identify and respond to abnormal traffic present in a network. An IDS can be utilized for evaluating the various types of malicious attacks. Hence, detecting intrusions has become a significant research area in the contemporary era, especially with the evolution of technologies. With the progress of ML (Machine Learning)-based algorithms, researchers have striven to perform optimal ID. However, most of these studies lag in accordance with their accuracy rate. Thus, to attain a high accuracy rate in ID, the present study proposes ML-based meta-heuristic algorithms, as these approaches possess innate merits of determining near-optimal solutions in limited time and are capable of dealing with multi-dimensional data. The study proposes OWSA (Optimal Whale Sine Algorithm) for selecting suitable and relevant features. With an exclusive optimization process using the SCA (Sine Cosine Algorithm), this study proposes to combine SCA with WOA (Whale Optimization Algorithm) for mitigating the demerits of both, with its hybridization thereby achieving OWSA. Following this, AWRF (Artificial Neural Network Weighted Random Forest) is proposed for classification. The main intention of this process is to propose a weight-updating process for discrete trees in the RF model. The proposed approach is motivated by avoiding overfitting and attaining stability and flexibility. This approach is assessed with regard to performance via a comparative analysis, so as to uncover the best performance of this proposed technique in ID.

Electroencephalography (EEG) serves as a diagnostic technique for measuring brain waves and brain activity. Despite its precision in capturing brain electrical activity, certain factors like environmental influences during the test can affect the objectivity and accuracy of EEG interpretations. Challenges associated with interpretation, even with advanced techniques to minimize artifact influences, can significantly impact the accurate interpretation of EEG findings. To address this issue, artificial intelligence (AI) has been utilized in this study to analyze anomalies in EEG signals for epilepsy detection. Recurrent neural networks (RNNs) are AI techniques specifically designed to handle sequential data, making them well-suited for precise time-series tasks. While AI methods, including RNNs and artificial neural networks (ANNs), hold great promise, their effectiveness heavily relies on the initial values assigned to hyperparameters, which are crucial for their performance for concrete assignment. To tune RNN performance, the selection of hyperparameters is approached as a typical optimization problem, and metaheuristic algorithms are employed to further enhance the process. The modified hybrid sine cosine algorithm has been developed and used to further improve hyperparameter optimization. To facilitate testing, publicly available real-world EEG data is utilized. A dataset is constructed using captured data from healthy and archived data from patients confirmed to be affected by epilepsy, as well as data captured during an active seizure. Two experiments have been conducted using generated dataset. In the first experiment, models were tasked with the detection of anomalous EEG activity. The second experiment required models to segment normal, anomalous activity as well as detect occurrences of seizures from EEG data. Considering the modest sample size (one second of data, 158 data points) used for classification models demonstrated decent outcomes. Obtained outcomes are compared with those generated by other cutting-edge metaheuristics and rigid statistical validation, as well as results’ interpretation is performed.

The present paper deals with the chattering attenuation analysis in Variable Structure Control (VSC) for Automatic Voltage Regulators (AVR). As the VSC suffers from the chattering phenomenon, the performances of the soft switching functions are investigated. Signum, saturation, tangent hyperbolic, and fractional approximation functions are used in the proposed controller, and optimum controller parameters are found out separately with Sine Cosine Algorithm. In the definition of the objective function, the overshoot of the step response is restricted by 0.5%. Since every real system has input constraints, the control input and the exciter of the linear AVR model are limited by the direction of IEEE recommendations. It is observed that the calculated control inputs are cropped at the upper and lower limits of the control input. Identical robustness measurements to the previous studies are performed, and the results are compared to each other. While the signum-based switching control exhibits undesirable oscillation, the smooth functions contribute highly acceptable control input. Among the soft switching functions, the tangent hyperbolic and saturation functions are more prominent concerning the disturbance rejection and parameter uncertainties.

As one of recently proposed optimization algorithm, sine cosine algorithm (SCA) suffers from the problems of skipping over true solutions, stagnating in local optima and premature convergence. Therefore, an improved sine cosine algorithm with heterogeneous subpopulations (HSISCA) is developed in this paper. The population of HSISCA is divided into two subpopulations which comply with different search mechanisms and information sharing rules. One subpopulation is specified to enhance exploration by incorporating Levy flight and random search guidance into SCA, while the other is specified to enhance exploitation by adaptive differential mutation operator. The exploration subpopulation is denied access to the information of exploitation subpopulation, but the exploitation subpopulation is allowed access to the information of exploration subpopulation. Moreover, the greedy selection is applied to each individual to preserve useful information. HSISCA is tested on the CEC2014 and CEC2017 benchmark functions, and is used for designing fractional order PID (FOPID) controller. The results confirm the better performance of HSISCA compared to other competitive algorithms, and demonstrate the effectiveness of HSISCA in designing FOPID controller for complex systems. [ABSTRACT FROM AUTHOR]

In recent years, the sine cosine algorithm (SCA) has become one of the popular swarm intelligence algorithms due to its simple and convenient structure. However,the standard SCA tends to fall into the local optimum when solving complex multimodal tasks, leading to unsatisfactory results. Therefore, this study presents the SCA with communication and quality enhancement, called CCEQSCA. The proposed algorithm includes two enhancement strategies: the communication and collaboration strategy (CC) and the quality enhancement strategy (EQ). In the proposed algorithm, CC strengthens the connection of SCA populations by guiding the search agents closer to the range of optimal solutions. EQ improves the quality of candidate solutions to enhance the exploitation of the algorithm. Furthermore, EQ can explore potential candidate solutions in other scopes, thus strengthening the ability of the algorithm to prevent trapping in the local optimum. To verify the capability of CCEQSCA, 30 functions from the IEEE CEC2017 are analyzed. The proposed algorithm is compared with 5 advanced original algorithms and 10 advanced variants. The outcomes indicate that it is dominant over other comparison algorithms in global optimization tasks. The work in this paper is also utilized to tackle three typical engineering design problems with excellent optimization capabilities. It has been experimentally demonstrated that CCEQSCA works as an effective tool to tackle real issues with constraints and complex search space. [ABSTRACT FROM AUTHOR]

This paper proposes new improved binary versions of the Sine Cosine Algorithm (SCA) for the Feature Selection (FS) problem. FS is an essential machine learning and data mining task of choosing a subset of highly discriminating features from noisy, irrelevant, high-dimensional, and redundant features to best represent a dataset. SCA is a recent metaheuristic algorithm established to emulate a model based on sine and cosine trigonometric functions. It was initially proposed to tackle problems in the continuous domain. The SCA has been modified to Binary SCA (BSCA) to deal with the binary domain of the FS problem. To improve the performance of BSCA, three accumulative improved variations are proposed (i.e., IBSCA1, IBSCA2, and IBSCA3) where the last version has the best performance. IBSCA1 employs Opposition Based Learning (OBL) to help ensure a diverse population of candidate solutions. IBSCA2 improves IBSCA1 by adding Variable Neighborhood Search (VNS) and Laplace distribution to support several mutation methods. IBSCA3 improves IBSCA2 by optimizing the best candidate solution using Refraction Learning (RL), a novel OBL approach based on light refraction. For performance evaluation, 19 real-wold datasets, including a COVID-19 dataset, were selected with different numbers of features, classes, and instances. Three performance measurements have been used to test the IBSCA versions: classification accuracy, number of features, and fitness values. Furthermore, the performance of the last variation of IBSCA3 is compared against 28 existing popular algorithms. Interestingly, IBCSA3 outperformed almost all comparative methods in terms of classification accuracy and fitness values. At the same time, it was ranked 15 out of 19 in terms of number of features. The overall simulation and statistical results indicate that IBSCA3 performs better than the other algorithms. [ABSTRACT FROM AUTHOR]

In the modern era, Internet of Things (IoT) has been a popular research topic and it focuses on interconnecting numerous sensor-based devices primarily for tracking applications and collecting data. Wireless Sensor Networks (WSN) becomes a significant element in IoT platforms since its inception and turns out to be the most ideal platform for deploying various smart city application zones namely disaster management, home automation, intelligent transportation, smart buildings, and other IoT-enabled applications. Clustering techniques were commonly used energy-efficient methods with the main purpose that is to balance the energy between Sensor Nodes (SN). Routing and clustering are Non-Polynomial (NP) hard issues where bio-inspired approaches were used for a known time to solve these issues. This study introduces a Hybrid Sine-Cosine Black Widow Spider Optimization based Route Selection Protocol (HSBWSO-RSP) for Mulithop Communication in IoT assisted WSN. The presented HSBWSO-RSP technique aims to properly determine the routes to destination for multihop communication. Moreover, the HSBWSO-RSP approach enables the integration of variance perturbation mechanism into the traditional BWSO algorithm. Furthermore, the selection of routes takes place by a fitness function comprising Residual Energy (RE) and distance (DIST). The experimental result analysis of the HSBWSO-RSP technique is tested using a series of experimentations and the results are studied under different measures. The proposed methodology achieves 100% packet delivery ratio, no packet loss and 2.33 secs end to end delay. The comparison study revealed the betterment of the HSBWSO-RSP technique over existing routing techniques.

Directed against the disadvantages of relatively short life-cycle and unbalanced energy utilization among nodes in WSN, a clustering routing algorithm combining sine cosine algorithm and Lévy mutation is developed. During the cluster head election stage, the amount of cluster heads is dynamically calculated according to the surviving nodes for keeping it at a reasonable value; taking full account of the current energy of nodes, only nodes with high energy can be candidate cluster heads; the fitness function is constructed according to intra-cluster distance, so that the distribution structure within the cluster are relatively uniform; the Sine Cosine Algorithm with improved step size search factor is used for cluster head election, and Lévy mutation is introduced to realize the variation of population. The group of individuals with the lowest fitness function value is used as final election scheme for current round. In the data transmission phase, for the sake of avoiding long-distance transmission, the relay node is designed to forward data. The proposed algorithm effectively extends network life-cycle and well equalizes the load of network nodes.

The Sine Cosine Algorithm (SCA) is a well-known optimization technique that utilizes sine and cosine functions to identify optimal solutions. Despite its popularity, the SCA has limitations in terms of low diversity, stagnation in local optima, and difficulty in achieving global optimization, particularly in complex large-scale problems. In response, we propose a novel approach named the Improved Weight and Strategy Sine Cosine Algorithm (IWSCA). The IWSCA achieves this by introducing novel self-adaptive weight and social strategies that enable the algorithm to efficiently search for optimal solutions in complex large-scale problems. The performance of the IWSCA is evaluated with 23 benchmark test functions and the IEEE CEC 2019 benchmark suite, compare it to a state-of-the-art heuristic algorithm and two improved versions of the SCA. Our experimental results demonstrate that the IWSCA outperforms existing methods in terms of convergence precision and robustness.

Future search algorithm imitates the person living life. If one person finds that his life is not good, he will try to change his living life, and he will imitate a more successful person. To overcome insufficient performances of the basic Future search algorithm, this paper proposed an improved Future search algorithm based on the sine cosine algorithm (FSASCA). The proposed algorithm uses sine cosine algorithm to loop-progressive find the best solution. The searching method of the sine cosine algorithm can make the feasible solution to be re-positioned around another feasible solutions, which can make the proposed algorithm have a strong exploitation ability. Four coefficient factors are added in the basic FSA, and new update methods are introduced in the searching phase. To verify the searching and optimization performances of the proposed algorithm in this paper, this paper also gives data calculation results, Wilcoxon rank sum test, iteration figures, box plot figures, and searching path figures. Experimental results showed that FSASCA has a better iteration speed, the convergence precision, the solving accuracy, the strong competitive, and the high balance.

To address the position control problem of a six degrees of freedom autonomous underwater vehicle (AUV), the disturbance is observed by an observer and compensated by a controller in this paper. To ensure the vehicle reaches the specified position quickly, the sine cosine algorithm with individual memory is used to find suitable parameters for different controllers. The simulation results show that the observer can extract the system's internal and external uncertainty information in real-time, reduce the phase lag and improve the stability of AUV position control. For a system with complex nonlinearity such as AUV, the observer proposed in this paper has stronger applicability and better robustness.

Vaccinationis the best approach in curbing the spread of a pandemic.However, during pandemic one of the challenge is limited number of vaccine due to limited manufacturing capacity and high demand. Therefore, optimal vaccine distribution is neededto ensure maximum effectiveness in decreasingthe total infections in the population.In this paper, the vaccine distribution is optimized usingsine cosine algorithm improved withmutation (SCAmut). The SEIR model of H1N1 pandemic in 2009 is used as the case problem here. The effectiveness of SCAmut vaccine deployment is studied usingtwofactors, which are vaccine coverage percentage andvaccine releasing time. The algorithm's result is compared withthree traditional methodsand original SCA without mutation.The findings suggestedthat the proposed SCAmutis able toprovide more effective vaccination distributionsbetter than the three traditional methods and also the original SCA.

In this article, a method for the selection of buses in a radial distribution system (RDS) to locate different types of distributed generation (DG) is proposed. The buses have been identified based on the loss sensitivity factor. Line flow constraints have been considered using the load flow program. Type-I DG, that is, solar and fuel cell and Type-III DG, that is, small hydro turbine, cogeneration, and gas turbine have been considered for the study. Determination of optimum capacity for Type-I and Type-III DGs to minimize active power losses is selected as an optimization function. Metaphor-less RAO-3 algorithm has been used to evaluate the optimum DG capacity and results have been validated with sine cosine algorithm. Results are also compared with already existing algorithms in literature like hybrid technique, novel heuristic approach, etc. The statistical inference has been provided and results obtained for IEEE-33 and -69 bus RDS using RAO-3 algorithm is found better and has fast convergence characteristic. [ABSTRACT FROM AUTHOR]

Diagnostic and predictive models of disease have been growing rapidly due to developments in the field of healthcare. Accurate and early diagnosis of COVID-19 is an underlying process for controlling the spread of this deadly disease and its death rates. The chest radiology (CT) scan is an effective device for the diagnosis and earlier management of COVID-19, meanwhile, the virus mainly targets the respiratory system. Chest X-ray (CXR) images are extremely helpful in the effective diagnosis of COVID-19 due to their rapid outcomes, cost-effectiveness, and availability. Although the radiological image-based diagnosis method seems faster and accomplishes a better recognition rate in the early phase of the epidemic, it requires healthcare experts to interpret the images. Thus, Artificial Intelligence (AI) technologies, such as the deep learning (DL) model, play an integral part in developing automated diagnosis process using CXR images. Therefore, this study designs a sine cosine optimization with DL-based disease detection and classification (SCODL-DDC) for COVID-19 on CXR images. The proposed SCODL-DDC technique examines the CXR images to identify and classify the occurrence of COVID-19. In particular, the SCODL-DDC technique uses the EfficientNet model for feature vector generation, and its hyperparameters can be adjusted by the SCO algorithm. Furthermore, the quantum neural network (QNN) model can be employed for an accurate COVID-19 classification process. Finally, the equilibrium optimizer (EO) is exploited for optimum parameter selection of the QNN model, showing the novelty of the work. The experimental results of the SCODL-DDC method exhibit the superior performance of the SCODL-DDC technique over other approaches. [ABSTRACT FROM AUTHOR]

Polycyclic aromatic hydrocarbons (PAHs) refer to a group of several hundred compounds, among which 16 are identified as priority pollutants, due to their adverse health effects, frequency of occurrence, and potential for human exposure. This study is focused on benzo(a)pyrene, being considered an indicator of exposure to a PAH carcinogenic mixture. For this purpose, we have applied the XGBoost model to a two-year database of pollutant concentrations and meteorological parameters, with the aim to identify the factors which were mostly associated with the observed benzo(a)pyrene concentrations and to describe types of environments that supported the interactions between benzo(a)pyrene and other polluting species. The pollutant data were collected at the energy industry center in Serbia, in the vicinity of coal mining areas and power stations, where the observed benzo(a)pyrene maximum concentration for a study period reached 43.7 ng m − 3 . The metaheuristics algorithm has been used to optimize the XGBoost hyperparameters, and the results have been compared to the results of XGBoost models tuned by eight other cutting-edge metaheuristics algorithms. The best-produced model was later on interpreted by applying Shapley Additive exPlanations (SHAP). As indicated by mean absolute SHAP values, the temperature at the surface, arsenic, PM 10 , and total nitrogen oxide (NOx) concentrations appear to be the major factors affecting benzo(a)pyrene concentrations and its environmental fate. [ABSTRACT FROM AUTHOR]

Due to the vast amounts of textual data available in various forms such as online content, social media comments, corporate data, public e-services and media data, text clustering has been experiencing rapid development. Text clustering involves categorizing and grouping similar content. It is a process of identifying significant patterns from unstructured textual data. Algorithms are being developed globally to extract useful and relevant information from large amounts of text data. Measuring the significance of content in documents to partition the collection of text data is one of the most important obstacles in text clustering. This study suggests utilizing an improved metaheuristics algorithm to fine-tune the K-means approach for text clustering task. The suggested technique is evaluated using the first 30 unconstrained test functions from the CEC2017 test-suite and six standard criterion text datasets. The simulation results and comparison with existing techniques demonstrate the robustness and supremacy of the suggested method. [ABSTRACT FROM AUTHOR]

The development of many engineering and scientific models depends on the solution of nonlinear systems of equations (NSEs), and the progress of these fields depends on their efficient resolution. Due to the disadvantages in solving them with classical methods, NSEs are amenable to modeling as an optimization issue. The purpose of this work is to propose the chaotic search sine cosine algorithm (CSSCA), a new optimization approach for solving NSEs. CSSCA will be set up so that it employs a chaotic search to get over the limitations of optimization techniques like a lack of diversity in solutions, exploitation's unfair advantage over exploration, and the gradual convergence of the optimal solution. A chaotic logistic map has been employed by many studies and has demonstrated its effectiveness in raising the quality of solutions and offering the greatest performance. So, it is used as a local search strategy. Three kinds of test functions—unimodal, multimodal, and composite test functions—as well as numerous NSEs—combustion problems, neurophysiology problems, arithmetic application, and nonlinear algebraic equations—were employed to assess CSSCA. To demonstrate the significance of the changes made in CSSCA, the results of the recommended algorithm are contrasted with those of the original SCA, where CSSCA's average improvement rate was roughly 12.71, demonstrating that it is very successful at resolving NSEs. Finally, outcomes demonstrated that adding a chaotic search to the SCA improves results by modifying the chaotic search's parameters, enabling better outcomes to be attained. [ABSTRACT FROM AUTHOR]

An ever increasing number of electronic devices integrated into the Internet of Things (IoT) generates vast amounts of data, which gets transported via network and stored for further analysis. However, besides the undisputed advantages of this technology, it also brings risks of unauthorized access and data compromise, situations where machine learning (ML) and artificial intelligence (AI) can help with detection of potential threats, intrusions and automation of the diagnostic process. The effectiveness of the applied algorithms largely depends on the previously performed optimization, i.e., predetermined values of hyperparameters and training conducted to achieve the desired result. Therefore, to address very important issue of IoT security, this article proposes an AI framework based on the simple convolutional neural network (CNN) and extreme machine learning machine (ELM) tuned by modified sine cosine algorithm (SCA). Not withstanding that many methods for addressing security issues have been developed, there is always a possibility for further improvements and proposed research tried to fill in this gap. The introduced framework was evaluated on two ToN IoT intrusion detection datasets, that consist of the network traffic data generated in Windows 7 and Windows 10 environments. The analysis of the results suggests that the proposed model achieved superior level of classification performance for the observed datasets. Additionally, besides conducting rigid statistical tests, best derived model is interpreted by SHapley Additive exPlanations (SHAP) analysis and results findings can be used by security experts to further enhance security of IoT systems.

Due to uncertain load deviations in the unified power system, it becomes more challenging to uphold the system frequency at a constant desired value. The secondary controller has a significant role in damping the oscillations of frequency and interline power at a faster rate. Here a novel PI/PD dual mode controller (DMC) is suggested to regulate the frequency of unified system comprising renewable and conventional sources of power generation. The various sources of generation instigated in the dual-area system are hydro, diesel, thermal, PV, wind and battery energy storage system (BESS). The gains of the PI/PD DMC are suitably obtained by the novel application of quasi opposition based Sine Cosine Algorithm (QOBSCA) algorithm. The dynamic outcomes of the model are examined with the application of abrupt load deviation of 0.02 per unit in control area 2. During this deviated loading condition QOBSCA algorithm is utilized to achieve the appropriate gains of PID and PI/PD dual mode controller in view of a time based fitness function denoted as integral time absolute error (ITAE). Considering few response evaluative indices like overshoots, settling time and undershoots the ascendency of the projected PI/PD dual mode controller is recognized over popular PID controller during the abnormal loading condition. Additionally, the efficacy of the mentioned PI/PD dual mode controller is verified by applying arbitrary load pattern in control area 2. The robustness of the mentioned control schemes is also evidenced by confirming the stable response during system parameter deviations.