Sine Cosine Optimization Research Articles

Reducing Operational Costs in Sulselbar’s 150 kV System with Electromagnetic Field and Sine-Cosine Optimization

Dynamic economic dispatch (DED) is a crucial task in modern power systems, requiring efficient optimization to minimize generation costs while satisfying operational constraints. This study introduces a Hybrid Electromagnetic Field Optimization with Sine-Cosine Algorithm (EMFO-SCA) tailored to address the unique challenges of the Sulselbar 150 kV power system. Specifically, the algorithm is designed to handle non-linear cost functions, complex constraints, and dynamic load variations across a 24-hour scheduling period. EMFO-SCA achieves a balanced integration of global exploration (via Electromagnetic Field Optimization) and local exploitation (via the Sine-Cosine Algorithm), resulting in robust optimization performance. Applied to a system with seven active generator buses, EMFO-SCA demonstrates an average operational cost reduction of 0.27% compared to the Kho-Kho Algorithm (KKA). This improvement translates to measurable cost savings while maintaining strict adherence to generation limits, ramp rate constraints, and power balance at all intervals. For instance, during peak demand at 521.52 MW (hour 12), the method effectively minimizes costs without compromising operational reliability. The dual-phase design of EMFO-SCA enables faster convergence and higher accuracy than conventional methods, making it a scalable solution for real-world DED challenges. By optimizing power generation schedules dynamically and reliably, this study establishes EMFO-SCA as a significant advancement in energy system optimization, with clear potential for practical deployment in similar power systems.

EfficientDense-ViT: APT Detection via Hybrid Deep Learning Framework with Hybrid Dipper Throated Sine Cosine Optimization Algorithm (HDT-SCO)

Advanced Persistent Threats (APT) are intelligent, sophisticated cyberattacks that frequently evade detection by gradually interfering with vital systems or focusing on sensitive data. It is proposed herein the new approach of the Hybrid Dipper Throated Sine Cosine Optimization Algorithm (HDT-SCO) for APT detection in association with the EfficientDense-ViT model. It handles the class imbalance issue with advanced processing Adaptive Synthetic Minority Oversampling Technique (ADASYN), including min-max scaling for normalization, and median imputation for missing values. In terms of feature engineering, ResNet-152 and Symbolic Aggregate Approximation (SAX) are adopted for statistical, deep, and time series feature extraction. HDT-SCO optimizes the selection of relevant features to refine by integrating into it the three approaches: PCA, RFE, RF Feature Importance, and L1 Regularization (Lasso). Compared to current detection techniques, the best detection model shows high performance and efficiency through the hybrid deep learning model known as EfficientDense-ViT, which is a combination of EfficientNet, DenseNet, and Vision Transformers (ViT) that can detect APTs reliably. This method shows considerable improvement in both accuracy (0.98741 for the 7030 split and 0.99143 for the 8020 split) and efficiency as compared to existing models in the detection of APTs in cybersecurity.

Advancements in evaporation prediction: introducing the Gated Recurrent Unit–Multi-Kernel Extreme Learning Machine (MKELM)–Gaussian Process Regression (GPR) model

Predicting evaporation is an essential topic in water resources management. It is critical to plan irrigation schedules, optimize hydropower production, and accurately calculate the overall water balance. Thus, researchers have developed many prediction models for predicting evaporation. Despite the development of these models, there are still unresolved challenges. These challenges include selecting the most important input parameters, handling nonstationary data, extracting critical information from data, and quantifying the uncertainty of predicted values. Thus, the main aim of this study is to address these challenges by developing a new prediction model. The new prediction model, named Gated Recurrent Unit–Multi-Kernel Extreme Learning Machine (MKELM)–Gaussian Process Regression (GPR), was used to predict one-month ahead evaporation in the Kashafrood basin, Iran. This model was executed in multiple stages. First, a feature selection algorithm was used to determine the most critical input parameters. A data processing technique was then employed to decompose nonstationary data into stationary intrinsic mode functions (IMFs). The GRU model then processed these components to extract their essential information. In the following step, the extracted information was inserted into the MKELM model to predict evaporation. Finally, the GPR model quantified the uncertainty of predicted values. Our research also introduces a new optimizer called the Salp Swarm Optimization Algorithm–Sine Cosine Optimization Algorithm. This algorithm was used to tune the model parameters. This algorithm's performance and the prediction models’ accuracy were evaluated using several error indices. According to the study results, the GRU–MKELM–GPR model performed better than other models in predicting monthly evaporation. It improved the training and testing mean absolute error values of the other models by 21%-43% and 8.2–33%, respectively. Moreover, the new model improved the R2 (R-squared or coefficient of determination) values of other models by 5–12%. Generally, the main findings of this paper included the superior performance of the new model in predicting evaporation data and the superior performance of a new optimizer in adjusting model parameters. These findings highlighted the effectiveness of the suggested model in addressing the challenges associated with evaporation prediction.

Puzzle sine cosine optimization-based secure communication and brain tumor classification in IoT‐healthcare system

A brain tumor (BT) refers to an irregular accumulation of cells within the brain that proliferates uncontrollably, resulting in the formation of a mass. The accurate classification and early detection are important for effective treatment. In previous researches, the BT exhibited diverse features in terms of size, shape, and location. Moreover, the images used for segmentation, which suffered from image noise, low contrast, and shifting intensities within tissues. These issues are overcome by developing an effective method in this paper named Puzzle Sine Cosine Optimization enabled Deep Kronecker Network (PSCO-DKN) for classifying BT in the Internet of Things (IoT) healthcare system. Firstly, an IoT network is simulated, where the IoT device is used to capture the patient’s Magnetic Resonance Imaging (MRI) images. Further, the images are routed to the Base Station (BS) by employing PSCO. The routing is accomplished by contemplating several fitness parameters including delay, energy, and distance. At the BS, the process for BT classification is implemented as follows. Initially, the pre-processing is done by utilizing the median filter. Afterwards, the segmentation process is done by applying Spatial Attention U-Net (SA-Unet). After that, Statistical features and Shape Local Binary Texture (SLBT) are extracted. At last, BT classification is performed by utilizing the DKN, which is structurally optimized by using PSCO developed by the hybridization of Puzzle Optimization Algorithm (POA) and Sine Cosine Algorithm (SCA). Finally, PSCO-DKN attained superior outcomes of True Negative Rate (TNR) at 90.9 %, True Positive Rate (TPR) at 92.6 %, and accuracy at 87.7 %.

Robustness estimation for state-of-charge of a lithium-ion battery based on feature fusion

State of charge (SOC) of the lithium-ion battery stands as one of the crucial parameters of the battery management system. To enhance the robustness and accuracy of data-driven methods for estimating SOC, in this study, a novel framework based on feature selection and closed-loop estimation framework is proposed for estimating the SOC of a battery. Firstly, the maximum mutual information coefficient is used to correlate the data collected from the battery experiments. Secondly, an improved least squares support vector machine model (LSSVM) is proposed for SOC estimation using the sine cosine optimization algorithm for parameter optimization of LSSVM. Furthermore, based on the Sage-Husa adaptive Kalman filter, this study constructed a closed-loop estimation model. Finally, we conduct experiments on batteries with variable temperatures and working conditions, to verify its robustness and generalization. The results indicate that the proposed method has great SOC estimation accuracy compared to the control group. Even under simulated measurement noise conditions, the proposed method achieves the root mean square error 1.28 % and 1.12 % respectively, demonstrating strong robustness.

A hybrid deep recurrent artificial neural network with a simple exponential smoothing feedback mechanism

Both classical forecasting methods and machine learning approaches are used to solve forecasting problems. Deep artificial neural networks, one of the machine learning methods, are widely used today and give very good results. Recurrent neural networks, a type of deep neural network, are very important in forecasting problems. Simple recurrent artificial neural networks, which are the simplest deep recurrent neural networks, are often preferred in solving forecasting problems due to the small number of parameters they use. Simple exponential smoothing, one of the classical forecasting methods, attracts attention with its performance in solving forecasting problems. The motivation of the study is to create a new forecasting method by combining a classical and simple forecasting method with a deep recurrent artificial neural network in an architecture. In this, a new hybrid deep recurrent artificial neural network with a simple exponential smoothing feedback mechanism is proposed. The architecture of the proposed method is created as a combination of simple recurrent artificial neural networks and simple exponential smoothing methods. In the training of the proposed method, two training algorithms based on sine cosine optimization and particle swarm optimization algorithms are proposed. In these training algorithms, two different solution strategies such as restarting, and early stopping rule are used to avoid overfitting and local optimum problems. The performance of the proposed method is analysed using stock market datasets and compared with both different deep and shallow artificial neural networks and classical forecasting methods. As a result of the analyses, it is concluded that the proposed method is successful in one step ahead of forecasting performance.

Optimal reactive power dispatch with renewable energy sources using hybrid whale and sine cosine optimization algorithm

The optimization of reactive power dispatch entails the complex challenge of controlling and managing the flow of reactive power in power networks to maintain desired voltage levels across many buses. Nowadays, there is a rising preference for employing renewable energy sources rather than traditional thermal generators. This change presents both challenges and possibilities for power system operators and managers. This paper addresses the Optimal Reactive Power Dispatch (ORPD) problem by presenting a novel approach that incorporates solar and wind power plants into existing power networks using the Hybrid Whale and Sine Cosine Optimisation Algorithm (HWSCOA). Solar and wind power plants are established at bus 5 and bus 8 respectively to replace traditional thermal generators in a specific case study using the IEEE 30-bus system. To handle uncertainties associated with load demand changes and the intermittent nature of renewable energy generation, the study employs probability density functions and a variety of scenarios. The primary goal is to minimize power losses in transmission cables while also lowering voltage changes throughout the network. To address uncertainty in load demands and renewable energy output, a scenario-based methodology is used, generating 30 different scenarios to cover all conceivable outcomes. By presenting the ORPD challenge as an optimization problem, the study hopes to achieve considerable reductions in power losses and voltage variations from nominal levels. The findings of this study reveal encouraging results, including significant reductions in power losses and optimized voltage stability even under shifting conditions.

ALRN-RCS: Advanced Approach to Network Intrusion Detection Using Attention Long-Term Recurrent Networks and Chaotic Optimization

Detecting intrusions within a network is essential for protecting digital environments; it encompasses the observation and analysis of network traffic to recognize and counteract unauthorized or malicious activities. Conventional methods in network intrusion detection face several challenges such as polymorphic and evasive attacks, scalability issues, and anomaly-based complexity. To address these complexities, this paper proposed a novel method named Attention Long-term Recurrent Network-based Random Chaotic Sine (ALRN-RCS) algorithm for network intrusion detection. In this study, Long Short-Term Memory (LSTM) is utilized to capture complex patterns in network traffic and identify anomalous activities. Also, the attention-based Recurrent Neural Network (RNN) is employed to focus on relevant features within network traffic and enable precise intrusion detection. In this paper, we have incorporated the Chaotic Chimp Sine Cosine optimization algorithm, employing a random update strategy, to optimize hyperparameters and improve the overall efficacy of the proposed approach and the study conducted experiments on the datasets namely the UNSW NB-15, Network Intrusion Detection dataset, and the Segmented Image-based Network Intrusion Detection (SIDD) dataset. Diverse assessment criteria, including accuracy, F1-score, recall, AUC-ROC, and precision, are employed to assess the effectiveness of the ALRN-RCS method and to draw comparisons with established methodologies. The experimental results depict the effectiveness of the ALRN-RCS method for addressing the dynamic and sophisticated nature of modern cyber threats.

Multiple UAVs Networking Oriented Consistent Cooperation Method Based on Adaptive Arithmetic Sine Cosine Optimization

With the rapid development of the Internet of Things, the Internet of Vehicles (IoV) has quickly drawn considerable attention from the public. The cooperative unmanned aerial vehicles (UAVs)-assisted vehicular networks, as a part of IoV, has become an emerging research spot. Due to the significant limitations of the application and service of a single UAV-assisted vehicular networks, efforts have been put into studying the use of multiple UAVs to assist effective vehicular networks. However, simply increasing the number of UAVs can lead to difficulties in information exchange and collisions caused by external interference, thereby affecting the security of the entire cooperation and networking. To address the above problems, multiple UAV cooperative formation is increasingly receiving attention. UAV cooperative formation can not only save energy loss but also achieve synchronous cooperative motion through information communication between UAVs, prevent collisions and other problems between UAVs, and improve task execution efficiency. A multi-UAVs cooperation method based on arithmetic optimization is proposed in this work. Firstly, a complete mechanical model of unmanned maneuvering was obtained by combining acceleration limitations. Secondly, based on the arithmetic sine and cosine optimization algorithm, the mathematical optimizer was used to accelerate the function transfer. Sine and cosine strategies were introduced to achieve a global search and enhance local optimization capabilities. Finally, in obtaining the precise position and direction of multi-UAVs to assist networking, the cooperation method was formed by designing the reference controller through the consistency algorithm. Experimental studies were carried out for the multi-UAVs’ cooperation with the particle model, combined with the quadratic programming problem-solving technique. The results show that the proposed quadrotor dynamic model provides basic data for cooperation position adjusting, and our simplification in the model can reduce the amount of calculations for the feedback and the parameter changes during the cooperation. Moreover, combined with a reference controller, the UAVs achieve the predetermined cooperation by offering improved navigation speed, task execution efficiency, and cooperation accuracy. Our proposed multi-UAVs cooperation method can improve the quality of service significantly on the UAV-assisted vehicular networks.

Synthesis of concentric circular antenna array for reducing the sidelobe level by employing sine cosine optimization algorithm

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

Adaptive Load Frequency Control in Microgrids Considering PV Sources and EVs Impacts: Applications of Hybrid Sine Cosine Optimizer and Balloon Effect Identifier Algorithms

The negative impacts of microgrids (µGs) on the load frequency highlight the importance of implementing a robust, efficient, and adaptable controller to ensure stability. This work introduces an adaptive load frequency control (LFC) for an isolated µG that includes a PV system and electric vehicles (EVs), which have a significant impact on frequency. This control utilizes a combination of sine cosine optimization (SCO) and balloon effect identifier (BEI) algorithms. The controller presented in this work transforms the LFC process into an optimization problem that is highly compatible with various random situations encountered in the control process. The suggested control method is a novel approach by utilizing SCO+BEI for adaptive LFC application, resulting in a highly efficient response. The effectiveness of the proposed adaptive controller is assessed under the conditions of 17 MW variable load, system parameters uncertainties, and installed PV systems of 6 MW. MATLAB / Simulink package is rummage-sale as a digital test environment. According to simulation results, the proposed adaptive controller succeeds in regulating the frequency and power of an islanded µG. To measure the efficiency of the proposed control scheme, a comparison between other control techniques (such as adaptive controller using Jaya+BEI and classical integral controller) is done. The findings of the studied scenarios assured that the not compulsory control method using (SCO+BEI) has an obvious superiority over other control methods in terms of frequency solidity in case of random load instabilities and parameter uncertainties. Finally, it can be said that the proposed controller can better ensure the safe operation of the µGs.

Control of DVR using enhanced transfer delay frequency-locked loop using sine–cosine optimization

Control of DVR using enhanced transfer delay frequency-locked loop using sine–cosine optimization

An Application of Hybrid Sine Cosine Optimization for Developing Sustainable Agriculture Distribution Feeders with Optimal Photovoltaic Systems

An Application of Hybrid Sine Cosine Optimization for Developing Sustainable Agriculture Distribution Feeders with Optimal Photovoltaic Systems

An optimal and smart E-waste collection using neural network based on sine cosine optimization

An optimal and smart E-waste collection using neural network based on sine cosine optimization

Boosting and Comparing Performance of Machine Learning Classifiers with Meta-heuristic Techniques to Detect Code Smell

Background: Continuous modifications, suboptimal software design practices, and stringent project deadlines contribute to the proliferation of code smells. Detecting and refactoring these code smells are pivotal to maintaining complex and essential software systems. Neglecting them may lead to future software defects, rendering systems challenging to maintain, and eventually obsolete. Supervised machine learning techniques have emerged as valuable tools for classifying code smells without needing expert knowledge or fixed threshold values. Further enhancement of classifier performance can be achieved through effective feature selection techniques and the optimization of hyperparameter values. Aim: Performance measures of multiple machine learning classifiers are improved by fine tuning its hyperparameters using various type of meta-heuristic algorithms including swarm intelligent, physics, math, and bio-based etc. Their performance measures are compared to find the best meta-heuristic algorithm in the context of code smell detection and its impact is evaluated based on statistical tests. Method: This study employs sixteen contemporary and robust meta-heuristic algorithms to optimize the hyperparameters of two machine learning algorithms: Support Vector Machine (SVM) and k-nearest Neighbors (k-NN). The No Free Lunch theorem underscores that the success of an optimization algorithm in one application may not necessarily extend to others. Consequently, a rigorous comparative analysis of these algorithms is undertaken to identify the best-fit solutions for code smell detection. A diverse range of optimization algorithms, encompassing Arithmetic, Jellyfish Search, Flow Direction, Student Psychology Based, Pathfinder, Sine Cosine, Jaya, Crow Search, Dragonfly, Krill Herd, Multi-Verse, Symbiotic Organisms Search, Flower Pollination, Teaching Learning Based, Gravitational Search, and Biogeography-Based Optimization, have been implemented. Results: In the case of optimized SVM, the highest attained accuracy, AUC, and F-measure values are 98.75%, 100%, and 98.57%, respectively. Remarkably, significant increases in accuracy and AUC, reaching 32.22% and 45.11% respectively, are observed. For k-NN, the best accuracy, AUC, and F-measure values are all perfect at 100%, with noteworthy hikes in accuracy and ROC-AUC values, amounting to 43.89% and 40.83%, respectively. Conclusion: Optimized SVM exhibits exceptional performance with the Sine Cosine Optimization algorithm, while k-NN attains its peak performance with the Flower Optimization algorithm. Statistical analysis underscores the substantial impact of employing meta-heuristic algorithms for optimizing machine learning classifiers, enhancing their performance significantly. Optimized SVM excels in detecting the God Class, while optimized k-NN is particularly effective in identifying the Data Class. This innovative fusion automates the tuning process and elevates classifier performance, simultaneously addressing multiple longstanding challenges.

Sine cosine optimization algorithm combined with balloon effect for adaptive position control of a cart forced by an armature-controlled DC motor.

For a car that is propelled by an armature-controlled DC motor This study proposes an adjustable linear positioning control. In this paper, to optimize the parameters of the car's position controller the sine cosine optimization algorithm (SCA) is utilized, with support from the Balloon effect (BE), The BE is incorporated to enhance the responsiveness of the traditional sine cosine optimization algorithm when faced with external disturbances and variations in system parameters. In the proposed approach, the determined value of the open loop transfer function of the motor and the updated values of the controller gains serve as the basis for the modified sine cosine algorithm's objective function (OF). Under the influence of changes in motor parameters and step load disturbances, the system using the suggested controller is evaluated. Results from simulations and experiments show that the proposed adaptive controller, which implements the modified sine cosine algorithm, enhances the system's overall performance in the presence of load disturbances and parameter uncertainties.

Research on MEC computing offload strategy for joint optimization of delay and energy consumption.

The decision-making process for computational offloading is a critical aspect of mobile edge computing, and various offloading decision strategies are strongly linked to the calculated latency and energy consumption of the mobile edge computing system. This paper proposes an offloading scheme based on an enhanced sine-cosine optimization algorithm (SCAGA) designed for the "edge-end" architecture scenario within edge computing. The research presented in this paper covers the following aspects: (1) Establishment of computational resource allocation models and computational cost models for edge computing scenarios; (2) Introduction of an enhanced sine and cosine optimization algorithm built upon the principles of Levy flight strategy sine and cosine optimization algorithms, incorporating concepts from roulette wheel selection and gene mutation commonly found in genetic algorithms; (3) Execution of simulation experiments to evaluate the SCAGA-based offloading scheme, demonstrating its ability to effectively reduce system latency and optimize offloading utility. Comparative experiments also highlight improvements in system latency, mobile user energy consumption, and offloading utility when compared to alternative offloading schemes.

Machine learning-aided modeling for predicting freshwater production of a membrane desalination system: A long-short-term memory coupled with election-based optimizer

Membrane desalination (MD) is an efficient process for desalinating saltwater, combining the uniqueness of both thermal and separation distillation configurations. In this context, the optimization strategies and sizing methodologies are developed from the balance of the system’s energy demand. Therefore, robust prediction modeling of the thermodynamic behavior and freshwater production is crucial for the optimal design of MD systems. This study presents a new advanced machine-learning model to obtain the permeate flux of a tubular direct contact membrane distillation unit. The model was established by optimizing a long-short-term memory (LSTM) model by an election-based optimization algorithm (EBOA). The model inputs were the temperatures of permeate and the feed flow, and the rate and salinity of the feed flow. The optimized model was compared with other optimized LSTM models by sine–cosine optimization algorithm (SCA), artificial ecosystem optimizer (AEO), and grey wolf optimization algorithm (GWO). All models were trained, tested, and evaluated using different accuracy measures. LSTM-EBOA outperformed other models in predicting the permeate flux based on different accuracy measures. LSTM-EBOA had the highest coefficient of determination of 0.998 and 0.988 and the lowest root mean square error of 1.272 and 4.180 for training and test, respectively. It can be recommended that this paper provide a useful pathway for sizing parameters selection and predicting the performance of MD systems that makes an optimally designed model for predicting the freshwater production rates without costly experiments.

WITHDRAWN: A Novel Sine Cosine Optimization with Stacked Long Short-term Memory-enabled Stock Price Prediction

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A new algorithm to generate aes-like substitution boxes based on sine cosine optimization algorithm

A new algorithm to generate aes-like substitution boxes based on sine cosine optimization algorithm