Wolf Research Articles

Osprey optimization algorithm for MPPT of PV system with forward DC to DC converter under partial shading conditions

Abstract Electric power is required anywhere for multiple purposes to do various tasks. Production of electricity from renewable sources such solar energy is feasible solution for many problems. Photovoltaic modules are generally used to produce electricity through solar irradiance. However, a maximum power point tracking device (MPPTD) must be incorporated to Photovoltaic system to ensure its maximum utilization or best efficiency. A forward DC to DC circuit is selected to work as MPPTD in this paper. Many conventional algorithms including incremental conductance, perturb & observe are available to work effectively for MPPTD under uniform solar irradiances. However, non-uniform irradiances will be received by Photovoltaic modules where many are connected to form a power generation system due to trees, shading, dust, birds, clouds, etc Such phenomenon drags the system into Partial Shading Condition (PSC). Under PSC, conventional algorithms cannot identify the best location to harvest more energy. Hence, an optimization technique is required to operate Photovoltaic system at its outfit utilization during PSC. An optimization technique namely ‘Osprey Optimization Algorithm (OOA)’ is developed in this paper on MPPTD to harvest more energy during PSC. The comparison among OOA, Grey Wolf Optimization (GWO), Modified Invasive Weed Optimization (MIWO) and Whale Optimization Algorithm (WOA) is also included in this paper. The Hardware—in the—Loop is establish by using OPAL-RT technology to collect various results for presenting analysis under different operating conditions.

Corrosion prediction model for long-distance pipelines based on NLFE-NGO-ELM

Abstract To improve the accuracy of buried pipeline corrosion rate prediction, we developed a Northern Goshawk Optimization-based Extreme Learning Machine (NLFE-NGO-ELM) model enhanced by Non-linear Feature Expansion. Feature selection was used to expand the small dataset and reduce the dimensionality of factors affecting pipeline corrosion. The Northern Goshawk algorithm was used, and 106 sets of pipeline external corrosion detection data were simulated in MATLAB as a case study. The ELM optimizer was analyzed using Grey Wolf Optimization (GWO-ELM), Northern Goshawk Optimization (NGO-ELM), and BP optimization as comparison models. Using NLFE-NGO-ELM significantly improved the model's prediction accuracy and speed. The NGO-ELM model was applied to predict the corrosion rates of 21 pipelines, achieving an absolute error of only 2.40% and an R value of 0.97255, surpassing the predictive performance of NGO-BP and GWO-ELM models. These results indicate that the current model exhibits superior learning performance and stronger fitting capabilities.

Chain hybrid feature selection algorithm based on improved Grey Wolf Optimization algorithm.

Hybrid feature selection algorithm is a strategy that combines different feature selection methods aiming to overcome the limitations of a single feature selection method and improve the effectiveness and performance of feature selection. In this paper, we propose a new hybrid feature selection algorithm, to be named as Tandem Maximum Kendall Minimum Chi-Square and ReliefF Improved Grey Wolf Optimization algorithm (TMKMCRIGWO). The algorithm consists of two stages: First, the original features are filtered and ranked using the bivariate filter algorithm Maximum Kendall Minimum Chi-Square (MKMC) to form a subset of candidate features S1; Subsequently, S1 features are filtered and sorted to form a candidate feature subset S2 by using ReliefF in tandem, and finally S2 is used in the wrapper algorithm to select the optimal subset. In particular, the wrapper algorithm is an improved Grey Wolf Optimization (IGWO) algorithm based on random disturbance factors, while the parameters are adjusted to vary randomly to make the population variations rich in diversity. Hybrid algorithms formed by combining filter algorithms with wrapper algorithms in tandem show better performance and results than single algorithms in solving complex problems. Three sets of comparison experiments were conducted to demonstrate the superiority of this algorithm over the others. The experimental results show that the average classification accuracy of the TMKMCRIGWO algorithm is at least 0.1% higher than the other algorithms on 20 datasets, and the average value of the dimension reduction rate (DRR) reaches 24.76%. The DRR reached 41.04% for 12 low-dimensional datasets and 0.33% for 8 high-dimensional datasets. It also shows that the algorithm improves the generalization ability and performance of the model.

Hybrid intrusion detection models based on GWO optimized deep learning

In the rapidly evolving landscape of network communication systems, the need for robust security measures has become paramount due to increased vulnerability to cyber threats. Traditional Intrusion Detection Systems (IDSs) face challenges in efficiently handling redundant features, leading to increased computational complexity. This research addresses these challenges by proposing two optimized IDSs leveraging Grey Wolf Optimization (GWO) combined with deep learning (DL) models. The first system integrates Gated Recurrent Unit (GRU) with GWO (GRU-GWO), while the second utilizes Long Short-Term Memory (LSTM) with GWO (LSTM-GWO). These systems aim to enhance feature selection, reducing dimensionality and improving detection accuracy. The NSL-KDD and UNSW-NB15 datasets, representative of contemporary network environments, were employed to evaluate the proposed systems. Experimental results demonstrate significant improvements in intrusion detection accuracy and computational efficiency, underscoring the efficacy of the DL-GWO approach in enhancing network security. The first approach (GRU-GWO-FS) increased accuracy to 90% and 79% for anomaly and signature-based detection on the UNSW-NB15 dataset, compared to 80% and 77% with all features. The second approach (LSTM-GWO-FS) achieved 93% and 79%, compared to 82% and 77%. On the NSL-KDD dataset, GRU-GWO-FS improved accuracy to 94% and 92%, and LSTM-GWO-FS to 94% and 92% for anomaly and signature-based detection, respectively.

Discrete Grey Wolf Optimizer for Solving Urban Traffic Light Scheduling Problem

Discrete Grey Wolf Optimizer for Solving Urban Traffic Light Scheduling Problem

Comparative Evaluation of Traditional and Advanced Algorithms for PV Systems in Partial Shading Conditions

The optimization of photovoltaic (PV) systems is vital for enhancing efficiency and economic viability, especially under Partial Shading Conditions (PSCs). This study focuses on the development and comparison of traditional and advanced algorithms, including Perturb and Observe (P&O), Incremental Conductance (IC), Fuzzy Logic Control (FLC), Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), and Artificial Neural Networks (ANN), for efficient Maximum Power Point Tracking (MPPT). Simulations conducted in the MATLAB/Simulink software package evaluated these algorithms’ performances under various shading scenarios. The results indicate that, while traditional methods like P&O and IC are effective under uniform conditions, advanced techniques, particularly ANN-based MPPT, exhibit superior efficiency and faster convergence under PSCs. This study concludes that integrating Artificial Intelligence (AI) and Machine Learning (ML) into MPPT algorithms significantly enhances the reliability and efficiency of PV systems, paving the way for a broader adoption of solar energy technologies in diverse environmental conditions. These findings contribute to advancing renewable energy technology and supporting green energy transition.

Orthopedic disease classification based on breadth-first search algorithm

Orthopedic diseases are widespread worldwide, impacting the body’s musculoskeletal system, particularly those involving bones or hips. They have the potential to cause discomfort and impair functionality. This paper aims to address the lack of supplementary diagnostics in orthopedics and improve the method of diagnosing orthopedic diseases. The study uses binary breadth-first search (BBFS), binary particle swarm optimization (BPSO), binary grey wolf optimizer (BGWO), and binary whale optimization algorithm (BWAO) for feature selections, and the BBFS makes an average error of 47.29% less than others. Then we apply six machine learning models, i.e., RF, SGD, NBC, DC, QDA, and ET. The dataset used contains 310 instances and six distinct features. Through experimentation, the RF model led to optimal outcomes during comparison to the remaining models, with an accuracy of 91.4%. The parameters of the RF model were optimized using four optimization algorithms: BFS, PSO, WAO, and GWO. To check how well the optimized RF works on the dataset, this paper uses prediction evaluation metrics such as accuracy, sensitivity, specificity, F-score, and the AUC curve. The results showed that the BFS-RF can improve the performance of the original classifier compared with others with 99.41% accuracy.

A Sustainable Approach-based Optimization of Internal Diamond Burnishing Operation

The related works of the diamond burnishing processes focused on improvements in surface quality. The study aims to optimize burnishing factors, including the spraying distance of the nozzle (S), the inlet pressure of the cold air (I), and the quantity of the liquid CO2 (L) of the cool and cryogenic-assisted diamond burnishing operation for minimizing energy consumed (EC) and arithmetical mean surface height roughness (Sa). Burnishing responses are modeled based on the radial basis function network and full factorial data. The entropy method, improved grey wolf optimizer, non-dominated sorting genetic algorithm II, and technique for order of preference by similarity to ideal solution were implemented to calculate the weights, produce solutions, and select the best outcome. As a result, the optimal data of the S, I, and L were 15.0 mm, 3.0 bar, and 11.0 L/min, respectively. The Sa and EC were reduced by 20.4% and 3.8%, respectively, at the optimality. The optimized outcomes could be employed to improve energy efficiency and machining quality for the internal diamond burnishing process. The optimizing technique could be used to solve complicated issues for different burnishing operations. The cool and cryogenic-assisted diamond burnishing process could be utilized for machining different internal holes.

Secure and efficient trust enabled routing in mobile ad hoc network using game theory and grey wolf optimisation techniques

AbstractMobile Ad hoc Networks (MANETs) are crucial wireless networks for military, corporate, and emergency use, yet they are vulnerable to disruptions from malicious nodes. The presence of malicious nodes can lead to message transmission and routing disorganisation, and network performance is effectively compromised. Game theory‐based fuzzy secure clustering (GTFSC) improves performance metrics in low‐scale and high‐scale networks. This protocol's novel ability to dynamically scale performance measures as nodes expand improves efficiency and adaptability. While improving performance metrics, the proposed algorithm also efficiently identifies malicious nodes and re‐routes the transmission, excluding the found malicious nodes. For any MANET system, secure and successful data transmission is paramount. The proposed protocol integrates various algorithms to fulfil its aim of customised EGT, GWO, and fuzzy clustering. Black hole attacks, grey hole attacks, Sybil attacks, and data tampering attacks are all considered to provide comprehensive attacks on MANET. Every node is assigned trust values, which get updated on data transmission. Fuzzy Clustering is employed to identify malicious nodes. Evolutionary Game Theory (EGT) optimises network organisation by designating cluster heads and clusters as nodes. Additionally, the proposed protocol leverages the Grey Wolf Optimisation Routing Algorithm (GWO), which establishes efficient routes from the source to the sink node. The analysis result shows maximum performance with a packet delivery ratio of around 98%, throughput of 90% end‐to‐end delay reduced by 15%, and energy consumption reduced by 18%, respectively, compared to an existing protocol.

Optimization of Synchronous Control Parameters Based on Improved Sinusoidal Gray Wolf Algorithm

High precision control is often accompanied by many control parameters, which are interrelated and difficult to adjust directly. It is difficult to convert the system control effect directly into mathematical expression, so it is difficult to optimize it by intelligent algorithm. To solve this problem, we propose an improved sinusoidal gray wolf optimization algorithm (ISGWO). In this algorithm, a particle crossing processing mechanism based on the symmetry idea is introduced to maximize the retention of the position information of the optimal individual and improve the search accuracy of the algorithm. In addition, a differential cross-perturbation strategy is adopted to help the algorithm jump out of the local optimal solution in time, which enhances the development capability of ISGWO. Meanwhile, the position update formula with improved sinusoidal can better balance the development and exploration of ISGWO. The ISGWO algorithm is compared with three improved Gray Wolf algorithms on the CEC2017 test set as well as the synchronization controller. The experimental results show that the ISGWO algorithm has better selectivity, speed and robustness.

Adaptive fuzzy coordinated control design for wind turbine using gray wolf optimization algorithm

Due to the randomness and intermittency of wind speed, the actual output power curve of a wind turbine (WT) deviates greatly from the theoretical power curve, thereby reducing the power generation capacity of the WT. An adaptive fuzzy coordinated control (AFCC) of WT is presented in this study to improve the power generation of WT. Firstly, a multi-objective optimization model (MOOM) for WT output power, generator speed and pitch angle is established, and its optimal solution set is used as the input eigenvector of a novel effective wind speed soft sensor (NEWSSS) model, which is modeled with kernel extreme learning machine (KELM). Secondly, a novel improved gray wolf optimization (NIGWO) algorithm is presented by improving the convergence factor and adaptive weights, which is used to solve MOOM and optimize the parameters of KELM. A variable pitch control (VPC) is designed by estimating the effective wind speed. Finally, an adaptive fuzzy control (AFC) is presented for WT. Based on the AFC and VPC, an AFCC for pitch angle and generator torque is designed for WT. The high measuring precision of NEWSSS and the good robustness and dynamic performance of AFCC are demonstrated by the simulation results.

An optimized two stage U-Net approach for segmentation of pancreas and pancreatic tumor

The segmentation of pancreas and pancreatic tumor remain a persistent challenge for radiologists. Consequently, it is essential to develop automated segmentation methods to address this task. U-Net based models are most often used among various deep learning-based techniques in tumor segmentation. This paper introduces an innovative hybrid two-stage U-Net model for segmenting both the pancreas and pancreatic tumors. The optimization technique, used in this approach, involves a combination of meta-heuristic optimization algorithms namely, Grey Wolf Border Collie Optimization (GWBCO) technique, combining the Grey Wolf Optimization algorithm and the Border Collie Optimization algorithm. Our approach is evaluated using key parameters, such as Dice Similarity Coefficient (DSC), Jaccard Index (JI), sensitivity, specificity and precision to assess its effectiveness and achieves a DSC of 93.33 % for pancreas segmentation. Additionally, the model also achieves high DSC of 91.46 % for pancreatic tumor segmentation. This method helps in improving the diagnostic accuracy and assists medical professionals to provide treatment at an early stage with precise intervention. The method offers•Two-stage U-Net model addresses both pancreas and tumor segmentation.•Combination of two metaheuristic optimization algorithms, Grey Wolf and Border Collie for enhanced performance.•High dice similarity coefficient for pancreas and tumor segmentation.

A comparative performance assessment of artificial intelligence based classifiers and optimized feature reduction technique for breast cancer diagnosis

Breast cancer (BC) is a catastrophic global health concern that causes numerous fatalities worldwide. Early detection of breast cancer may mitigate death rates; however, the prevailing diagnostic procedure for the malignancy necessitates numerous multifaceted laboratory tests that must be performed by medical professionals. In this article machine learning, a branch of Artificial Intelligence (AI), has been employed to improve cancer diagnosis, prognoses and survival rates while reducing the vulnerability of humans. Support Vector Machine (SVM), K-nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF) and Grey Wolf Optimizer (GWO) are implemented to prognosticate breast cancer. Comprehensive insights into the efficacy of these approaches for breast cancer prognosis are provided by the performance assessment that is accomplished using the confusion matrix, Receiver Operating Characteristic (ROC) curves and parallel coordinate plots. Both UCI (University of California Irvine) and SEER (Surveillance, Epidemiology and End Results) datasets have been utilized to confirm the investigation's findings and ensure their generalizability across diverse data sources. The results conclusively demonstrate that SVM is the cohort's most accurate classifier. With a stupendous accuracy rate of 99.1 %, the GWO-SVM compares favorably to all other algorithms. Furthermore, feature reduction approaches such as Minimum Redundancy Maximum Relevance (mRMR), ReliefF and Principal Component Analysis (PCA) are utilized. ReliefF has demonstrated exceptional effectiveness with a maximum accuracy of 98.2 %.

Optimization of laser cladding powder ratio and process parameters based on MOGWO algorithm

Optimizing the process parameters is crucial to obtaining a high-quality fusion coating. The fitted regression model for each response was then analyzed by creating a central composite test with process parameters and power ratios (laser power, scanning speed, and Ni-clad graphite content) as inputs and the quality of the fused coating (dilution rate, microhardness, and width of the fused coating) as responses. The laser cladding process parameters and power ratios were optimized using the multi-objective Gray Wolf Optimization (MOGWO) algorithm. The best combinations of process parameters and power ratios were laser power of 1596 W, scanning speed of 21.7 mm/s, and Ni-clad graphite percentage of 18.4 %. Experimental validation demonstrates the superiority and accuracy of the MOGWO algorithm. The dilution rate of the optimized fusion-clad coating is increased by 47.4 % and the microhardness is increased by 17.8 %.

Enhanced TDS Modeling Using an AI Framework Integrating Grey Wolf Optimization with Kernel Extreme Learning Machine

Enhanced TDS Modeling Using an AI Framework Integrating Grey Wolf Optimization with Kernel Extreme Learning Machine

Evolutionary divergence of facial muscle physiology between domestic dogs and wolves.

Domestic dogs (Canis familiaris) are descended from gray wolf (Canis lupus) populations that inhabited Western Europe and Siberia. The specific timing of dog domestication is debated, but archeological and genetic evidence suggest that it was a multi-phase process that began at least 15,000 years ago. There are many morphological differences between dogs and wolves, including marked divergence in facial muscle morphology, but we know little about the comparative physiology of these muscles. A better understanding of comparative facial muscle physiology between domestic dogs and gray wolves would improve our conceptual framework for the processual mechanisms in dog domestication. To address these issues, we assessed the myosin profiles (type I and type II) from the zygomaticus and orbicularis oris muscles of 6 domestic dogs and 4 gray wolves. Due to small sample sizes, statistical analyses were not done. Results reveal that sampled domestic dogs have almost 100% fast-twitch (type II) muscle fibers while gray wolves have less than 50%, meaning that dog faces can contract fast while wolf faces are able to sustain facial muscle contraction. Sample sizes are limited but the present study indicates that dog domestication is associated with not only a change in facial muscle morphology but a concomitant change in how these muscles function physiologically. Selective pressures in the development of communication between dogs and humans using facial expression may have influenced this evolutionary divergence, but the paedomorphic retention of barking in adult dogs may have also played a role.

Efficient energy management and cost optimization using multi-objective grey wolf optimization for EV charging/discharging in microgrid

The depletion of conventional energy sources coupled with the rising demand for electric vehicles (EVs) has significantly underscored the necessity for electric vehicle supply equipment (EVSE) with advanced energy supply management within microgrids. Effective energy management of EVs and EVSEs is imperative to satisfy EV owners and stabilize microgrids. This paper introduces multi-objective grey wolf optimization (MOGWO) to optimize EVSE and EV charging costs. MOGWO achieves substantial cost savings through dynamic pricing based on time of day, state-of-charge and hour-based scheduling which promotes off-peak charging thereby enhancing system efficiency and reducing costs. The algorithm also provides flexibility for trip interruptions and outperforms other optimization algorithms in minimizing EV charging/discharging costs by achieving reductions of up to ₹488 and obtaining average grid efficiency up to 76.41 % during charging and 87.41 % during discharging. Integrating Vehicle-to-Grid and Grid-to-Vehicle systems enhances microgrid resilience and delivers economic benefits to EV owners. The cost optimization for EV charging/discharging has been executed using MATLAB 2023a software to determine the most cost-effective charging paths by considering energy availability and grid conditions. The evolving sustainable energy landscape combined with MOGWO paves the way for smarter and more resilient microgrids in the era of electrified transportation.

Advanced swarm intelligence algorithms in quantum circuit design

This study explores the potential of employing algorithms like iSOMA, Differential Evolution, Particle Swarm Optimization, Grey Wolf Optimization, and Ant Colony Optimization for the design of quantum computing circuits. Utilizing the Qiskit environment, the research involved simulating a straightforward quantum circuit with variable parameters. To substantiate the effectiveness of these algorithms, three distinct experimental setups were conducted under varying conditions and degrees of freedom. The findings reveal that these algorithms are not only suitable for simulations but also excel in identifying solutions that conserve qubits. A comparative analysis of the methods was performed using the Friedman test, followed by the Nemenyi post-hoc test to evaluate their relative performance.

An Optimized PID Controller Desing for BLDC Motor Using Nature-Inspired Algorithms

For the optimal control of speed in a brushless DC motor, it is crucial to appropriately adjust the parameters of the PID controller. This study addresses the determination of PID controller parameters using nature-inspired metaheuristic optimization algorithms. Initially, the dynamic model of the brushless DC motor is formulated in the MATLAB/Simulink environment. The grey wolf optimization algorithm, whale optimization algorithm, and firefly algorithm are successively applied to the simulation model to optimize the PID controller parameters. The integral time absolute error objective function is utilized to compare the error performances of these algorithms. Additionally, performance evaluations are conducted concerning parameters such as rise time, settling time, and maximum overshoot. As a result of the comparison based on the fitness criteria, it was determined that the grey wolf optimization algorithm is 35% more successful than the algorithm that provided the next closest result.

Optimization and exergoeconomic analysis of a biomass-driven cogeneration system for power and cooling applications

Due to the increasing population and the expansion of industry in daily life, a significant depletion of fossil fuel resources is observed, accompanied by escalating environmental concerns. In response to these challenges, this study presents an optimized cogeneration system powered by renewable biomass, designed for simultaneous power and cooling generation. The system integrates a biomass gasification unit, a steam Rankine cycle, a helium-driven gas turbine cycle, and an ammonia-water-based two-stage absorption refrigeration cycle with a bleed line. Thermodynamic and exergoeconomic analyses reveal a net power output of 5356 kW, a cooling load of 360.9 kW, and an exergetic efficiency of 29.52 %. Financially, the system shows a net present value of 11.76 $M and a payback period of 6.51 years. A multi-objective optimization utilizing the gray wolf algorithm improves system performance through the employment of three diverse scenarios, which raises power output to 5864 kW, cooling load to 372 kW, and optimize net present to 14.28 $M with a reduced payback period of 5.96 years. The studied system is well-suited for industrial applications, rural electrification, and district energy systems, and offers a sustainable and economically viable energy solution.