Search Results (16)

To address the issue of uncertainty in renewable energy and its impact on the safe and stable operation of power systems, this paper proposes a transient stability constrained optimal power flow (TSCOPF) calculation method that takes into account the uncertainty of wind power and load. First, a non-parametric kernel density estimation method is used to construct the probability density function of wind power, while the load uncertainty model is based on a normal distribution. Second, a TSCOPF model incorporating the critical clearing time (CCT) evaluation metric is constructed, and corresponding probabilistic constraints are established using opportunity constraint theory, thereby establishing a TSCOPF model that accounts for wind power and load uncertainties; then, a semi-invariant probabilistic flow calculation method based on de-randomized Halton sequences is used to convert opportunity constraints into deterministic constraints, and the improved sooty tern optimization algorithm (ISTOA) is employed for solution. Finally, the superiority and effectiveness of the proposed method are validated through simulation analysis of case studies. Full article

DNA molecules, as a storage medium, possess unique advantages. Not only does DNA storage exhibit significantly higher storage density compared to electromagnetic storage media, but it also features low energy consumption and extremely long storage times. However, the integration of DNA storage into daily life remains distant due to challenges such as low storage density, high latency, and inevitable errors during the storage process. Therefore, this paper proposes constructing a DNA storage coding set based on the Levy Sooty Tern Optimization Algorithm (LSTOA) to achieve an efficient random-access DNA storage system. Firstly, addressing the slow iteration speed and susceptibility to local optima of the Sooty Tern Optimization Algorithm (STOA), this paper introduces Levy flight operations and propose the LSTOA. Secondly, utilizing the LSTOA, this paper constructs a DNA storage encoding set to facilitate random access while meeting combinatorial constraints. To demonstrate the coding performance of the LSTOA, this paper consists of analyses on 13 benchmark test functions, showcasing its superior performance. Furthermore, under the same combinatorial constraints, the LSTOA constructs larger DNA storage coding sets, effectively reducing the read–write latency and error rate of DNA storage. Full article

Circular antenna arrays (CAAs) find extensive utility in a range of cutting-edge communication applications such as 5G networks, the Internet of Things (IoT), and advanced beamforming technologies. In the realm of antenna design, the side lobes levels (SLL) in the radiation pattern hold significant importance within communication systems. This is primarily due to its role in mitigating signal interference across the entire radiation pattern’s side lobes. In order to suppress the subsidiary lobe, achieve the required primary lobe orientation, and improve directivity, an optimization problem is used in this work. This paper introduces a method aimed at enhancing the radiation pattern of CAA by minimizing its SLL using a Hybrid Sooty Tern Naked Mole-Rat Algorithm (STNMRA). The simulation results show that the hybrid optimization method significantly reduces side lobes while maintaining reasonable directivity compared to the uniform array and other competitive metaheuristics. Full article

As one of the key components of an aeroengine, turbine blisk endures complex coupling loads under a harsh operational environment so that the reliability of turbine blisk directly influences the safe operation of aeroengine. It is urgent to precisely perform the reliability estimation of a complex blisk structure. To address this issue, an enhanced Moving Neural Network Framework (MNNF) is proposed by integrating compact support region theory, improve sooty tern optimization algorithm (ISTOA), and Bayesian regularization strategy into artificial neural network. The compact support region theory is applied to select the efficient samples for modeling from the training samples set, the ISTOA is to determine the optimal compact support region, and Bayesian regularization thought is utilized to improve the generalization ability of neural network model. The operational reliability assessment of aeroengine blisk is performed with the consideration of transient loads to verify the proposed MNNF method. It is shown that the reliability degree of turbine blisk stain is 0.9984 when the allowable value is 5.2862 × 10⁻³ m. In line with the comparison of methods, the developed MNNF approach has 0.99738 in root means square error, 3.1634 × 10⁻⁴ m in goodness of fit, 0.423 s in modeling time, 99.99% in simulation precision, and 0.496 s in simulation time under 10,000 simulations, which are superior to all other methods (i.e., 99.96%, 99.91%, 99.93%, 99.97%, and 99.97% in simulation precision and 16.27%, 4.82%, 30.07%, 39.87%, and 23.59% in simulation efficiency, for the response surface method (RSM), Kriging, support vector machine (SVM), back propagation-artificial neural network (BP-NN), and BP-NN based on particle swarm optimization (BP-PSO) methods, respectively). It is demonstrated that the MNNF method holds excellent modeling and simulation performances. The efforts of this study provide promising tools and insights into the reliability design of complex structures, and enrich and develop reliability theory. Full article

Industry is the primary application for induction machines. As such, it is essential to calculate the induction devices’ electrical properties accurately. With DC testing, no-load rotor tests, and locked rotor tests, one may empirically evaluate the electrical variables of induction motors. These tests are expensive and difficult to conduct, however. The information supplied by machine makers can also be used to accurately approximate the equivalent variables of the circuits in induction machines. This article has successfully predicted motor reactance (Xm) for both double- and single-cage models using artificial neural networks (ANN). Although ANNs have been investigated in the literature, the ANN structures were trained to use unmemorized training. Besides ANN, six other approaches have been suggested to address this issue: heap-based optimization (HBO), leagues championship algorithm (LCA), multi-verse optimization (MVO), osprey optimization algorithm (OOA), cuckoo optimization algorithm (COA), and sooty tern optimization algorithm (STOA). The efficaciousness of the suggested approaches was compared with each another. Regarding the obtained outcomes, the suggested MVO- multi-layer perceptron (MLP) technique performed better than the other five methods regarding reactance prediction, with R² of 0.99598 and 0.9962, and RMSE of 20.31492 and 20.80626 in the testing and training phases, respectively. For the projected model, the suggested ANNs have produced great results. The novelty lies in the mentioned methods’ ability to tackle the complexities and challenges associated with induction motor reactance optimization, providing innovative approaches to finding optimal or near-optimal solutions. As researchers continue to explore and refine these techniques, their impact on motor design and efficiency will likely grow, driving advancements in electrical engineering. Full article

A original strategy for optimizing the inversion of concrete dam parameters based on the multi-strategy improved Sooty Tern Optimization algorithm (MSSTOA) is proposed to address the issues of low efficiency, low accuracy, and poor optimizing performance. First, computational strategies to improve the traditional Sooty tern algorithm, such as chaos mapping to improve the initial position of the population, a new nonlinear convergence factor, the LIMIT threshold method, and Gaussian perturbation to update the optimal individual position, are adopted to enhance its algorithmic optimization seeking ability. Then, the measured and finite element data are combined to create the optimization inversion fitness function. Based on the MSSTOA, the intelligent optimization inversion model is constructed, the inversion efficiency is improved by parallel strategy, and the optimal parameter inversion is searched. The inversion strategy is validated through test functions, hypothetical arithmetic examples, and concrete dam engineering examples and compared with the inversion results of the traditional STOA and other optimization algorithms. The results show that the MSSTOA is feasible and practical, the test function optimization results and computational time are better than the STOA and other algorithms, the example inversion of the elastic modulus is more accurate than the traditional STOA calculation, and the results of the MSSSTOA inversion are reasonable in the engineering example. Compared with other algorithms, the local extremes are skipped, and the time consumption is reduced by at least 48%. The finite element hydrostatic components calculated from the inversion results are well-fitted to the statistical model with minor errors. The intelligent inversion strategy has good application in concrete dam inverse analysis. Full article

Cloud computing (CC) is an internet-enabled environment that provides computing services such as networking, databases, and servers to clients and organizations in a cost-effective manner. Despite the benefits rendered by CC, its security remains a prominent concern to overcome. An intrusion detection system (IDS) is generally used to detect both normal and anomalous behavior in networks. The design of IDS using a machine learning (ML) technique comprises a series of methods that can learn patterns from data and forecast the outcomes consequently. In this background, the current study designs a novel multi-objective seagull optimization algorithm with a deep learning-enabled vulnerability detection (MOSOA-DLVD) technique to secure the cloud platform. The MOSOA-DLVD technique uses the feature selection (FS) method and hyperparameter tuning strategy to identify the presence of vulnerabilities or attacks in the cloud infrastructure. Primarily, the FS method is implemented using the MOSOA technique. Furthermore, the MOSOA-DLVD technique uses a deep belief network (DBN) method for intrusion detection and its classification. In order to improve the detection outcomes of the DBN algorithm, the sooty tern optimization algorithm (STOA) is applied for the hyperparameter tuning process. The performance of the proposed MOSOA-DLVD system was validated with extensive simulations upon a benchmark IDS dataset. The improved intrusion detection results of the MOSOA-DLVD approach with a maximum accuracy of 99.34% establish the proficiency of the model compared with recent methods. Full article

In this paper, a support vector regression (SVR) adaptive optimization rolling composite model with a sooty tern optimization algorithm (STOA) has been proposed for temperature prediction. Firstly, aiming at the problem that the algorithm tends to fall into the local optimum, the model introduces an adaptive Gauss–Cauchy mutation operator to effectively increase the population diversity and search space and uses the improved algorithm to optimize the key parameters of the SVR model, so that the SVR model can mine the linear and nonlinear information in the data well. Secondly, the rolling prediction is integrated into the SVR prediction model, and the real-time update and self-regulation principles are used to continuously update the prediction, which greatly improves the prediction accuracy. Finally, the optimized STOA-SVR rolling forecast model is used to predict the final temperature. In this study, the global mean temperature data set from 1880 to 2022 is used for empirical analysis, and a comparative experiment is set up to verify the accuracy of the model. The results show that compared with the seasonal autoregressive integrated moving average (SARIMA), feedforward neural network (FNN) and unoptimized STOA-SVR-LSTM, the prediction performance of the proposed model is better, and the root mean square error is reduced by 6.33–29.62%. The mean relative error is reduced by 2.74–47.27%; the goodness of fit increases by 4.67–19.94%. Finally, the global mean temperature is predicted to increase by about 0.4976 °C in the next 20 years, with an increase rate of 3.43%. The model proposed in this paper not only has a good prediction accuracy, but also can provide an effective reference for the development and formulation of meteorological policies in the future. Full article

This research utilized the sooty tern optimization algorithm–variational mode decomposition (STOA-VMD) optimization algorithm to extract the acoustic emission (AE) signal associated with damage in fiber-reinforced composite materials. The effectiveness of this optimization algorithm was validated through a tensile experiment on glass fiber/epoxy NOL-ring specimens. To solve the problems of a high degree of aliasing, high randomness, and a poor robustness of AE data of NOL-ring tensile damage, the signal reconstruction method of optimized variational mode decomposition (VMD) was first used to reconstruct the damage signal and the parameters of VMD were optimized by the sooty tern optimization algorithm. The optimal decomposition mode number K and penalty coefficient α were introduced to improve the accuracy of adaptive decomposition. Second, a typical single damage signal feature was selected to construct the damage signal feature sample set and a recognition algorithm was used to extract the feature of the AE signal of the glass fiber/epoxy NOL-ring breaking experiment to evaluate the effectiveness of the damage mechanism recognition. The results showed that the recognition rates of the algorithm in matrix cracking, fiber fracture, and delamination damage were 94.59%, 94.26%, and 96.45%, respectively. The damage process of the NOL-ring was characterized and the findings indicated that it was highly efficient in the feature extraction and recognition of polymer composite damage signals. Full article

Microgrid operation planning is crucial for ensuring the safe and efficient output of distributed energy resources (DERs) and stable operation of the microgrid power system. The integration of hydrogen fuel cells into microgrids can increase the absorption rate of renewable energy, while the incorporation of lithium batteries facilitates the adjustment of microgrid power supply voltage and frequency, ensuring the three-phase symmetry of the system. This paper proposes an economic scheduling method for a grid-connected microgrid that considers demand response and combines hydrogen and electricity. Based on the operating costs of renewable energy, maintenance and operation costs of nonrenewable energy, interaction costs between the microgrid and main grid, and pollution control costs, an optimization model for dispatching a hydrogen–electric hybrid microgrid under grid-connected mode is established. The primary objective is to minimize the operating cost, while the secondary objective is to minimize the impact on the user’s power consumption comfort. Therefore, an improved demand response strategy is introduced, and an enhanced sparrow search algorithm (ISSA) is proposed, which incorporates a nonlinear weighting factor and improves the global search capability based on the sparrow search algorithm (SSA). The ISSA is used to solve the optimal operation problem of the demand-response-integrated microgrid. After comparison with different algorithms, such as particle swarm optimization (PSO), whale optimization algorithm (WOA), sooty tern optimization algorithm (STOA), and dingo optimization algorithm (DOA), the results show that the proposed method using demand response and ISSA achieves the lowest comprehensive operating cost for the microgrid, making the microgrid’s operation safer and with minimum impact on user satisfaction. Therefore, the feasibility of the demand response strategy is demonstrated, and ISSA is proved to have better performance in solving optimal operation planning problems for hydrogen–electric hybrid microgrids. Full article

Lung cancer is one of the most common causes of cancer deaths in the modern world. Screening of lung nodules is essential for early recognition to facilitate treatment that improves the rate of patient rehabilitation. An increase in accuracy during lung cancer detection is vital for sustaining the rate of patient persistence, even though several research works have been conducted in this research domain. Moreover, the classical system fails to segment cancer cells of different sizes accurately and with excellent reliability. This paper proposes a sooty tern optimization algorithm-based deep learning (DL) model for diagnosing non-small cell lung cancer (NSCLC) tumours with increased accuracy. We discuss various algorithms for diagnosing models that adopt the Otsu segmentation method to perfectly isolate the lung nodules. Then, the sooty tern optimization algorithm (SHOA) is adopted for partitioning the cancer nodules by defining the best characteristics, which aids in improving diagnostic accuracy. It further utilizes a local binary pattern (LBP) for determining appropriate feature retrieval from the lung nodules. In addition, it adopts CNN and GRU-based classifiers for identifying whether the lung nodules are malignant or non-malignant depending on the features retrieved during the diagnosing process. The experimental results of this SHOA-optimized DNN model achieved an accuracy of 98.32%, better than the baseline schemes used for comparison. Full article

A brain tumor (BT) is an abnormal development of brain cells that causes damage to the nerves and blood vessels. An accurate and early diagnosis of BT is important to prevent future complications. Precise segmentation of the BT provides a basis for surgical and planning treatment to physicians. Manual detection utilizing MRI images is computationally difficult. Due to significant variation in their structure and location, viz., ambiguous boundaries and irregular shapes, computerized tumor diagnosis is still a challenging task. The application of a convolutional neural network (CNN) helps radiotherapists categorize the types of BT from magnetic resonance images (MRI). This study designs an evolutional algorithm with a deep learning-driven brain tumor MRI image classification (EADL-BTMIC) model. The presented EADL-BTMIC model aims to accurately recognize and categorize MRI images to identify BT. The EADL-BTMIC model primarily applies bilateral filtering (BF) based noise removal and skull stripping as a pre-processing stage. In addition, the morphological segmentation process is carried out to determine the affected regions in the image. Moreover, sooty tern optimization (STO) with the Xception model is exploited for feature extraction. Furthermore, the attention-based long short-term memory (ALSTM) technique is exploited for the classification of BT into distinct classes. To portray the increased performance of the EADL-BTMIC model, a series of simulations were carried out on the benchmark dataset. The experimental outcomes highlighted the enhancements of the EADL-BTMIC model over recent models. Full article

Photovoltaic module parameter estimation is a critical step in observing, analyzing, and optimizing the efficiency of solar power systems. To find the best value for unknown parameters, an efficient optimization strategy is required. This paper presents the implementation of the sooty tern optimization (STO) algorithm for parameter assessment of a solar cell/module. The simulation findings were compared to four pre-existing optimization algorithms: sine cosine (SCA) algorithm, gravitational search algorithm (GSA), hybrid particle swarm optimization and gravitational search algorithm (PSOGSA), and whale optimization (WOA). The convergence rate and root mean square error evaluations show that the STO method surpasses the other studied optimization techniques. Additionally, the statistical results show that the STO method is superior in average resilience and accuracy. The superior performance and reliability of the STO method are further validated by the Friedman ranking test. Full article

This paper presents an analysis and optimization of an isolated hybrid renewable power system to operate in the Alrashda village in the Dakhla Oasis, which is situated in the New Valley Governorate in Egypt. The proposed hybrid system is designed to integrate a biomass system with a photovoltaic (PV), wind turbine (WT) and battery storage system (Bat). Four different cases are proposed and compared for analyzing and optimizing. The first case is a configuration of PV and WT with a biomass system and battery bank. The second case is the integration of PV with a biomass system and battery bank. The third case is WT integrated with biomass and a battery bank, and the fourth case is a conventional PV, WT, and battery bank as the main storage unit. The optimization is designed to reduce component oversizing and ensure the dependable control of power supplies with the objective function of reducing the levelized cost of energy and loss of power supply probability. Four optimization algorithms, namely Heap-based optimizer (HBO), Franklin’s and Coulomb’s algorithm (CFA), the Sooty Tern Optimization Algorithm (STOA), and Grey Wolf Optimizer (GWO) are utilized and compared with each other to ensure that all load demand is met at the lowest energy cost (COE) for the proposed hybrid system. The obtained results revealed that the HBO has achieved the best optimal solution for the suggested hybrid system for case one and two, with the minimum COE 0.121171 and 0.1311804 $/kWh, respectively, and with net present cost ($\mathrm{NPC}$) of $3,559,143 and $3,853,160, respectively. Conversely, STOA has achieved the best optimal solution for case three and four, with a COE of 0.105673 and 0.332497 $/kWh, and an NPC of $3,103,938 and $9,766,441, respectively. Full article

This paper introduces a novel metaheuristic approach of sooty terns optimization algorithm (STOA) to determine the optimum parameters of model predictive control (MPC)-based deregulated load frequency control (LFC). The system structure consists of three interconnected plants with nonlinear multisources comprising wind turbine, photovoltaic model with maximum power point tracker, and superconducting magnetic energy storage under deregulated environment. The proposed objective function is the integral time absolute error (ITAE) of the deviations in frequencies and powers in tie-lines. The analysis aims at determining the optimum parameters of MPC via STOA such that ITAE is minimized. Moreover, the proposed STOA-MPC is examined under variation of the system parameters and random load disturbance. The time responses and performance specifications of the proposed STOA-MPC are compared to those obtained with MPC optimized via differential evolution, intelligent water drops algorithm, stain bower braid algorithm, and firefly algorithm. Furthermore, a practical case study of interconnected system comprising the Kuraymat solar thermal power station is analyzed based on actual recorded solar radiation. The obtained results via the proposed STOA-MPC-based deregulated LFC confirmed the competence and robustness of the designed controller compared to the other algorithms. Full article