Wolf Optimizer Research Articles

A comprehensive Analysis of Approaches and Difficulties for Cybersecurity Threats- Article Review

This study explores a broad range of cybersecurity research projects, including emerging fields like IoT and linked auto security as well as more established threats like malware and Distributed Denial of Service (DDoS) assaults. Scholars utilize a range of approaches, such as deep learning and machine learning, while paying close attention to clear dataset descriptions and the consequences of false positives and negatives. Accuracy and contextual awareness are critical, especially for Internet of Things security. Rapid threat identification relies on automation and effectiveness, and a commitment to progress is demonstrated by the incorporation of cutting-edge methods like Genetic, Wolf Optimization and new models. Finding a balance between feature selection, precision, and execution time presents a significant challenge. Similar examinations are made simpler by the accessibility of shared benchmark datasets. The final goal represented by reinforce bunch guards and advance computerized trust. This review gives data and courses to explore the continuously changing network protection scene.

A comprehensive Analysis of Approaches and Difficulties for Cybersecurity Threats- Article Review

This study explores a broad range of cybersecurity research projects, including emerging fields like IoT and linked auto security as well as more established threats like malware and Distributed Denial of Service (DDoS) assaults. Scholars utilize a range of approaches, such as deep learning and machine learning, while paying close attention to clear dataset descriptions and the consequences of false positives and negatives. Accuracy and contextual awareness are critical, especially for Internet of Things security. Rapid threat identification relies on automation and effectiveness, and a commitment to progress is demonstrated by the incorporation of cutting-edge methods like Genetic, Wolf Optimization and new models. Finding a balance between feature selection, precision, and execution time presents a significant challenge. Similar examinations are made simpler by the accessibility of shared benchmark datasets. The final goal represented by reinforce bunch guards and advance computerized trust. This review gives data and courses to explore the continuously changing network protection scene.

Improving the hearing aid system using optimized variable bandwidth filter based on wolf optimization

Improving the hearing aid system using optimized variable bandwidth filter based on wolf optimization

Biomimicry in Radiation Therapy: Optimizing Patient Scheduling for Improved Treatment Outcomes

In the realm of medical science, the pursuit of enhancing treatment efficacy and patient outcomes continues to drive innovation. This study delves into the integration of biomimicry principles within the domain of Radiation Therapy (RT) to optimize patient scheduling, ultimately aiming to augment treatment results. RT stands as a vital medical technique for eradicating cancer cells and diminishing tumour sizes. Yet, the manual scheduling of patients for RT proves both laborious and intricate. In this research, the focus is on automating patient scheduling for RT through the application of optimization methodologies. Three bio-inspired algorithms are employed for optimization to tackle the complex online stochastic scheduling problem. These algorithms include the Genetic Algorithm (GA), Firefly Optimization (FFO), and Wolf Optimization (WO). These algorithms are harnessed to address the intricate challenges of online stochastic scheduling. Through rigorous evaluation, involving the scrutiny of convergence time, runtime, and objective values, the comparative performance of these algorithms is determined. The results of this study unveil the effectiveness of the applied bio-inspired algorithms in optimizing patient scheduling for RT. Among the algorithms examined, WO emerges as the frontrunner, consistently delivering superior outcomes across various evaluation criteria. The optimization approach showcased in this study holds the potential to streamline processes, reduce manual intervention, and ultimately improve treatment outcomes for patients undergoing RT.

Precise Pulse Processing Discrimination between Alpha and Neutron Radiation Using Grey Wolf and Whale Optimization Algorithms

Precise Pulse Processing Discrimination between Alpha and Neutron Radiation Using Grey Wolf and Whale Optimization Algorithms

A novel on high voltage conversion ratio DC‐DC converter for EV‐fed fuel cell application with hybrid MPPT controller

SummaryAs of now, nonrenewable energy sources are not used for automotive industrial applications because their disadvantages are high installation costs, more maintenance costs, and less adaptability all over the places in the world. Also, these sources produce highly flammable gasses in the environment. So, in this work, a Proton Exchange Membrane Fuel Stack (PEMFS) is used as an input source for the proposed system. The PEMFS generated voltage value is very low which is improved by integrating the proposed Single Switch High Voltage Conversion Ratio (SSHVCR) DC‐DC converter. The properties of this SSHVCR converter are less component utilization for reducing the ripple ratio, more flexibility, less voltage stress, and high voltage conversion ratio. In the second objective, a Differential Step‐Grey Wolf Optimization (DSGWO) with Adaptive Fuzzy Logic (AFL) controller is used for generating switching pulses to the DC‐DC converter. The merits of DSGWO‐AFL controller are quick dynamic response, moderate design complexity, good adaptability, and needed few number iterations for identifying the MPP of PEMFS. The overall proposed system is studied by applying the MATLAB/Simulink tool at rapid changes in the operating temperature of the fuel cell stack.

Comparative study of artificial intelligence based multi‐modelling approach and optimization of photoreactor

AbstractThis study presents a generic methodology for modelling and optimizing a reactor with complex and poorly understood kinetics. Here, a photoreactor is considered which performs photodegradation of sodium oxalate salt in spent Bayer liquor. Multiple data‐driven modelling methods, including artificial neural networks (ANN), genetic programming (GP), hybrid genetic programming‐grey wolf optimization (GP‐GWO), and multi‐gene genetic programming (MGGP), were used to model the reactor's performance based on experimental data. The input parameters considered for modelling were initial solution pH, power of the lamp, total organic carbon, and catalyst loading. The models were evaluated based on their predictability, explainability, complexity, and adherence to the reactor's phenomenology. The MGGP model was found to be the most effective and was used to generate surface plots showing the parity between experimental results and model predictions. Additionally, the MGGP model was optimized using GWO to determine the process conditions that maximize the reaction rate.

Fixed‐time prescribed performance optimization control for the speed and tension system of the cold strip rolling mill with output constraints

SummaryFor the speed and tension system of the reversible cold strip rolling mill with output constraints, parameter perturbations and load disturbance, a fixed‐time prescribed performance optimization control method is proposed based on disturbance observers in this article. First, the disturbance observers are constructed to estimate the system's unmatched uncertainties, and the observer errors can converge in fixed time. Second, a time‐varying logarithmic barrier Lyapunov function (TLBLF) is given and combined with the command filtered backstepping approach, the fixed‐time control and the prescribed performance control to complete the controller designs for the speed and tension system of the cold strip rolling mill, which make the system states converge in fixed time and are always constrained within predefined ranges. Third, particle swarm optimization‐gray wolf optimization (PSO_GWO) hybrid intelligent algorithm is used to optimize the main control parameters of the designed controllers, which further improves the convergence speed and steady‐state accuracy of the rolling mill system. Theoretical analysis proves that the proposed control method can ensure the closed‐loop system is stable in fixed time. Finally, the simulation comparison study is carried out by using the field actual data of a reversible cold strip mill system, and the simulation results verify the effectiveness of the proposed control method.

A data driven approach in less expensive robust transmitting coverage and power optimization

This paper aims the development of a new reduced-cost algorithm for a multi-objective robust transmitter placement under uncertainty. Toward this end, we propose a new hybrid Kriging/Grey Wolf Optimizer (GWO) approach combined with robust design optimization to estimate the set of Pareto frontier by searching robustness as well as accuracy (lower objective function) in a design space. We consider minimization of the energy power consumption for transmitting as well as maximization of signal coverage in a multi-objective robust optimization model. The reliability of the model to control signal overlap for multiple transmitting antennas is also provided. To smooth computational cost, the proposed method instead of evaluating all receiver test points in each optimization iteration approximates signal coverages using Kriging interpolation to obtain optimal transmitter positions. The results demonstrate the utility and the efficiency of the proposed method in rendering the robust optimal design and analyzing the sensitivity of the transmitter placement problem under practically less-expensive computational efforts (350% and 320% less than computational time elapsed using standalone GWO and NSGAII respectively).

Optimal Coordinated Control of DC Microgrid Based on Hybrid PSO–GWO Algorithm

Microgrids (MGs) are capable of playing an important role in the future of intelligent energy systems. This can be achieved by allowing the effective and seamless integration of distributed energy resources (DERs) loads, besides energy-storage systems (ESS) in the local area, so they are gaining attraction worldwide. In this regard, a DC MG is an economical, flexible, and dependable solution requiring a trustworthy control structure such as a hierarchical control strategy to be appropriately coordinated and used to electrify remote areas. Two control layers are involved in the hierarchy control strategy, including local- and global-control levels. However, this research focuses mainly on the issues of DC MG’s local control layer under various load interruptions and power-production fluctuations, including inaccurate power-sharing among sources and unregulated DC-bus voltage of the microgrid, along with a high ripple of battery current. Therefore, this work suggests developing local control levels for the DC MG based on the hybrid particle swarm optimization/grey wolf optimizer (HPSO–GWO) algorithm to address these problems. The key results of the simulation studies reveal that the proposed control scheme has achieved significant improvement in terms of voltage adjustment and power distribution between photovoltaic (PV) and battery technologies accompanied by a supercapacitor, in comparison to the existing control scheme. Moreover, the settling time and overshoot/undershoot are minimized despite the tremendous load and generation variations, which proves the proposed method’s efficiency.

A novel hybrid forecasting system based on data augmentation and deep learning neural network for short-term wind speed forecasting

As a clean, economical, and renewable energy source, wind energy plays a very important role in easing the shortage of fossil energy, environmental population, and climate change. However, due to the strong intermittency, volatility, and randomness of wind speed, the large-scale connection of wind energy into the power grid is restricted. Therefore, constructing a reliable prediction model to achieve high-accuracy wind speed prediction is necessary. For this purpose, a novel hybrid model for short-term wind speed prediction is proposed in this paper. First, empirical mode decomposition is employed to decompose the raw wind speed time series into a set of subseries. Then, a data augmentation technique is first used to generate more training data to avoid overfitting of the prediction model. Furthermore, a new predictor based on a convolutional neural network (CNN), a long short-term memory (LSTM) network, and an extreme learning machine (ELM) is proposed for deterministic wind speed prediction, where a fuzzy entropy-based partition strategy is implemented to assign subseries to the CNN-LSTM and ELM. To improve the prediction performance, a synchronous optimization method based on an improved hybrid particle swarm optimization/gray wolf optimizer is proposed for feature selection and parameter optimization. Afterward, kernel density estimation is used to estimate the wind speed probability density function for probabilistic prediction. Finally, the performance of the proposed model is compared with seven other models by using three wind speed datasets from four aspects: point prediction, interval prediction, probability prediction comprehensive performance, and prediction reliability. The experimental results show that the proposed method achieves excellent performance on wind speed time series prediction.

Opposition based red wolf algorithm for solving optimal reactive power problem

<p>This paper presents an opposition based red wolf optimization (ORWO) algorithm for solving optimal reactive power problem. Each red wolf has a flag vector in the algorithm, and length is equivalent to the whole sum of numbers which features in the dataset of the wolf optimization (WO). In this proposed algorithm, red wolf optimization algorithm has been intermingled with opposition-based learning (OBL). By this amalgamate procedure the convergence speed of the proposed algorithm will be increased. To discover an improved candidate solution, the concurrent consideration of a probable and its corresponding opposite are estimated which is closer to the global optimum than an arbitrary candidate solution. Proposed algorithm has been tested in standard IEEE 14-bus and 300-bus test systems. The simulation results show that the proposed algorithm reduced the real power loss considerably.</p>

Two bio-inspired algorithms for solving optimal reactive power problem

<span>In this work two ground-breaking algorithms called; Sperm Motility (SM) algorithm & Wolf Optimization (WO) algorithm is used for solving reactive power problem. In sperm motility approach spontaneous movement of the sperm is imitated & species chemo attractant, sperms are enthralled in the direction of the ovum. In wolf optimization algorithm the deeds of wolf is imitated in the formulation & it has a flag vector also length is equivalent to the whole sum of numbers in the dataset the optimization. Both the projected algorithms have been tested in standard IEEE 57,118, 300 bus test systems. Simulated outcomes reveal about the reduction of real power loss & with variables are in the standard limits. Almost both algorithms solved the problem efficiently, yet wolf optimization has slight edge over the sperm motility algorithm in reducing the real power loss.</span>

A novel strategy based on salp swarm algorithm for extracting the maximum power of proton exchange membrane fuel cell

Cell temperature and water content of the membrane have a significant effect on the performance of fuel cells. The current-power curve of the fuel cell has a maximum power point (MPP) that is needed to be tracked. This study presents a novel strategy based on a salp swarm algorithm (SSA) for extracting the maximum power of proton-exchange membrane fuel cell (PEMFC). At first, a new formula is derived to estimate the optimal voltage of PEMFC corresponding to MPP. Then the error between the estimated voltage at MPP and the actual terminal voltage of the fuel cell is fed to a proportional-integral-derivative controller (PID). The output of the PID controller tunes the duty cycle of a boost converter to maximize the harvested power from the PEMFC. SSA determines the optimal gains of PID. Sensitivity analysis is performed with the operating fuel cell at different cell temperature and water content of the membrane. The obtained results through the proposed strategy are compared with other programmed approaches of incremental resistance method, Fuzzy-Logic, grey antlion optimizer, wolf optimizer, and mine-blast algorithm. The obtained results demonstrated high reliability and efficiency of the proposed strategy in extracting the maximum power of the PEMFC.

Martingales-Based ALOHA-Type Grant-Free Access Algorithms for Multi-Channel Networks With mMTC/URLLC Terminals Co-Existence

As a simple single-phase transmission strategy, grant-free access is believed to be an effective way to guarantee the stringent quality of service (QoS) requirements for ultra-reliable low-latency communications (URLLCs). However, unless a theory-based fine evaluation on dynamic delay, we cannot hope to overcome the natural defects of random access and so effectively utilize the time-frequency resources. In this paper, we propose a novel multi-channel ALOHA-type (M-ALOHA) grant-free access algorithm for heterogeneous machine type communication (MTC) networks with URLLC-type terminals and delay-tolerant massive MTC (mMTC)-type terminals co-existence. Firstly, we construct a statistical service model characterizing the transmission rate of each terminal with joint consideration of the features of M-ALOHA access scheme, short packet transmissions and frequency-selective fading channel. Then, taking the great advantages of service-martingales theory in random queuing analysis, we present an ingenious delay analysis and obtain the martingales-based formulation of delay-bound violation probability, where the sporadic feature of MTC traffic arrival is carefully addressed. Finally, the M-ALOHA algorithm is formulated as a system throughput maximization problem subject to martingales-based statistical delay-QoS and the total bandwidth of system. The problem is solved by the proposed bi-objective multi-variable-grey wolf optimizer (BOMV-GWO) algorithm. As a result, we obtain the access probability for each terminal and the optimal parameters for the system design, including the number of sub-channels, the bandwidth for each sub-channel and the packets transmission rate. Simulation results demonstrate that the performance of our M-ALOHA algorithm is favorable.

ACTIVE POWER LOSS DIMINUTION & VOLTAGE STABILITY ENHANCEMENT BY RED WOLF OPTIMIZATION ALGORITHM

In this paper optimal reactive power dispatch problem (ORPD), has been solved by Enriched Red Wolf Optimization (ERWO) algorithm. Projected ERWO algorithm hybridizes the wolf optimization (WO) algorithm with swarm based algorithm called as particle swarm optimization (PSO) algorithm. In the approach each Red wolf has a flag vector, and length is equivalent to the whole sum of numbers which features in the dataset of the wolf optimization (WO). Exploration capability of the projected Red wolf optimization algorithm has been enriched by hybridization of both WO with PSO. Efficiency of the projected Enriched Red wolf optimization (ERWO) evaluated in standard IEEE 30 bus test system. Simulation study indicates Enriched Red wolf optimization (ERWO) algorithm performs well in tumbling the actual power losses& particularly voltage stability has been enriched.

BANKS CAPACITOR COMPENSATION FOR CRITICAL NODAL DETECTION BY AUGMENTED RED WOLF OPTIMIZATION ALGORITHM

In this paper Banks Capacitor Compensation for Critical Nodal Detections by Augmented Red Wolf Optimization Algorithm has been worked out. Projected ERWO algorithm hybridizes the wolf optimization (WO) algorithm with swarm based algorithm called as particle swarm optimization (PSO) algorithm. In the approach each Red wolf has a flag vector, and length is equivalent to the whole sum of numbers which features in the dataset of the wolf optimization (WO). Exploration capability of the projected Red wolf optimization algorithm has been enriched by hybridization of both WO with PSO. Efficiency of the projected Enriched Red wolf optimization (ERWO) is tested in standard IEEE 57 bus test system. Simulation study indicates Enriched Red wolf optimization (ERWO) algorithm performs well in tumbling the actual power losses.

Automatic generation control of two-area electrical power systems via optimal fuzzy classical controller

The interconnected large-scale power systems are liable to performance degradation under the presence of sudden small load demands, parameter ambiguity and structural changes. Due to this, to supply reliable electric power with good quality, robust and intelligent control strategies are extremely requisite in automatic generation control (AGC) of power systems. Hence, this paper presents an output scaling factor (SF) based fuzzy classical controller to enrich AGC conduct of two-area electrical power systems. An implementation of imperialist competitive algorithm (ICA) is made to optimize the output SF of fuzzy proportional integral (FPI) controller employing integral of squared error criterion. Initially the study is conducted on a well accepted two-area non-reheat thermal system with and without considering the appropriate generation rate constraint (GRC). The advantage of the proposed controller is illustrated by comparing the results with fuzzy controller and bacterial foraging optimization algorithm (BFOA)/genetic algorithm (GA)/particle swarm optimization (PSO)/hybrid BFOA-PSO algorithm/firefly algorithm (FA)/hybrid FA-pattern search (hFA-PS) optimized PI/PID controller prevalent in the literature. The proposed approach is further extended to a newly emerged two-area reheat thermal-PV system. The superiority of the method is depicted by contrasting the results of GA/FA tuned PI controller. The proposed control approach is also implemented on a multi-unit multi-source hydrothermal power system and its advantage is established by Correlating its results with GA/hFA-PS tuned PI, hFA-PS/grey wolf optimization (GWO) tuned PID and BFOA tuned FPI controllers. Finally, a sensitivity analysis is performed to demonstrate the robustness of the proposed method to broad changes in the system parameters and size and/or location of step load perturbation.