Grey Wolf Optimization Method Research Articles

Integer Ambiguity Parameter Identification for Fast Satellite Positioning and Navigation Based on LAMBDA-GWO with Tikhonov Regularization

Satellite positioning is one of the main navigation technologies in unmanned aerial vehicles (UAVs), the accuracy of which has an important impact on the safety, stability, and flexibility of UAVs. The parameters of integer ambiguity are important factors affecting the accuracy of satellite positioning. However, the accuracy of the integer ambiguity cannot be guaranteed when only a few epoch data can be obtained in the fast positioning such that the identification matrix of the integer ambiguity parameters is seriously ill-conditioned and the information of position deviation is enlarged. In this paper, an error checking and correcting strategy is proposed, where a Least-square Ambiguity Decorrelation Adjustment-Grey Wolf Optimization (LAMBDA-GWO) Method combined with the Tikhonov regularization method is developed to improve the accuracy of integer ambiguity for fast satellite positioning. More specifically, the LAMBDA-GWO is first used to search the integer ambiguity parameters. To reduce the ill-condition of the integer ambiguity parameter identification matrix, the Tikhonov regularization method is introduced to regularize the identification matrix such that a reliable integer ambiguity floating-point solution can be obtained. Furthermore, the correctness of the integer ambiguity is checked according to the prior accuracy information of the initial coordinates and the Total Electron Content (TEC), and the part that fails the test is corrected by the Grey Wolf Optimization (GWO) Method. Finally, experimental studies based on a 522 m baseline and a 975 m baseline show that the identification success rates of the proposed method are both above 99%, which is 12% and 23% higher than that of traditional LAMBDA, respectively.

Machine Learning-Based Hybrid Recommendation (SVOF-KNN) Model For Breast Cancer Coimbra Dataset Diagnosis

An effective way to identify breast cancer is by creating a prediction algorithm using risk factors. Models for ML have been used to improve the effectiveness of early detection. This article analyses a KNN combined with singular value decomposition and Grey wolf optimization(GWO) method to give a detection of breast cancer(BC) at the early phase depending on risk metrics. The SVD technique was utilized to eliminate the reliable feature vectors, the GW optimizer was used to select the feature vectors, and while KNN model was used to diagnose the BC status. The proposed hybrid recommendation model (SVOF-KNN) for BC prediction's main objective is to give an accurate recommendation for BC prognosis through four different steps such as;BCCD dataset collection, data pre-processing, feature selection, and classification/recommendation. It is implemented to classify the consequence of risk metrics connected withregular blood analysis(BA) in the BCCD database. The aspects of the BC dataset are insulin, glucose, HOMA, Leptin, resistin, etc. The error categories such as RMSE and MAE are used to calculate the exception values for each instance of the BC dataset. It hybrid model has recommended the best score instance having the minimumexception rateas the defined features for BC prediction. It improves significance in automatic BC classification with the optimum solution. The hybrid recommendation model (SVOF-KNN) also recommends the accurateclassification method for BC diagnosis. The results of this work shall enhance the QoS in BC care.

IGWO-IVNet3: DL-Based Automatic Diagnosis of Lung Nodules Using an Improved Gray Wolf Optimization and InceptionNet-V3

Artificial intelligence plays an essential role in diagnosing lung cancer. Lung cancer is notoriously difficult to diagnose until it has progressed to a late stage, making it a leading cause of cancer-related mortality. Lung cancer is fatal if not treated early, making this a significant issue. Initial diagnosis of malignant nodules is often made using chest radiography (X-ray) and computed tomography (CT) scans; nevertheless, the possibility of benign nodules leads to wrong choices. In their first phases, benign and malignant nodules seem very similar. Additionally, radiologists have a hard time viewing and categorizing lung abnormalities. Lung cancer screenings performed by radiologists are often performed with the use of computer-aided diagnostic technologies. Computer scientists have presented many methods for identifying lung cancer in recent years. Low-quality images compromise the segmentation process, rendering traditional lung cancer prediction algorithms inaccurate. This article suggests a highly effective strategy for identifying and categorizing lung cancer. Noise in the pictures was reduced using a weighted filter, and the improved Gray Wolf Optimization method was performed before segmentation with watershed modification and dilation operations. We used InceptionNet-V3 to classify lung cancer into three groups, and it performed well compared to prior studies: 98.96% accuracy, 94.74% specificity, as well as 100% sensitivity.

Hybrid MPPT approach using Cuckoo Search and Grey Wolf Optimizer for PV systems under variant operating conditions

Photovoltaic (PV) systems are adversely affected by partial shading and non-uniform conditions. Meanwhile, the addition of a bypass shunt diode to each PV module prevents hotspots. It also produces numerous peaks in the PV array’s power-voltage characteristics, thereby trapping conventional maximum power point tracking (MPPT) methods in local peaks. Swarm optimization approaches can be used to address this issue. However, these strategies have an unreasonably long convergence time. The Grey Wolf Optimizer (GWO) is a fast and more dependable optimization algorithm. This renders it a good option for MPPT of PV systems operating in varying partial shading. The conventional GWO method involves a long conversion time, large steady-state oscillations, and a high failure rate. This work attempts to address these issues by combining Cuckoo Search (CS) with the GWO algorithm to improve the MPPT performance. The results of this approach are compared with those of conventional MPPT according to GWO and MPPT methods based on perturb and observe (P&O). A comparative analysis reveals that under non-uniform operating conditions, the hybrid GWO CS (GWOCS) approach presented in this article outperforms the GWO and P&O approaches.

A novel energy management model among interdependent sections in the smart grids

AbstractTechnically, residential energy management systems are fundamental sectors in the smart grids for implementing demand response programs in the layer of households for managing energy consumption and reducing energy bills. The paper proposes a novel energy management scheme that takes production and usage into account based on a heuristic searching operation. In addition to modelling the grid, renewable energy sources, batteries, and electric vehicles, various kinds of electrical and thermal devices have been examined, including air conditioners, water heaters, vacuum cleaners etc. A method is developed for solving the objective constraint issue in a smart home in order to reduce energy consumption and determine feasible operation states among the various loads. Moreover, this paper proposes a grey wolf optimization method for solving the issue over a longer simulation period. Various cases were examined to evaluate the effectiveness of this suggested robust optimization algorithm. The outcomes show that the suggested model could not only reduce energy costs significantly but has also shown good performance for energy management purposes.

Energy Efficiency in Wireless Sensor Networks using Advanced LEACH Protocol

The wireless sensor network is the best research subject matter with huge applications in various domains. By and large, a wireless sensor network involves hundreds to thousands of sensor nodes, which transmit and communicate with each other by the utilization of radio transmissions or signals. A portion of the difficulties that exist in the sketch of wireless sensor networks are confined computation power, storage capacity, battery, and transmission transfer speed or bandwidth. To determine these issues, clustering and routing algorithm have been introduced. Clustering and routing processes are viewed as an optimization issue in wireless sensor networks which can be settled by the utilization of swarm intelligence-based approaches. This article presents an original multitude of insight-based grouping and multihop routing protocol for wireless sensor report giving a new swarm optimization technique applied for picking the cluster heads and organizing the cluster capably. Then, at that point, the grey wolf optimization algorithm-based routing process takes place to choose the ideal ways in the network. The introduced better particle swarm optimization-grey wolf optimization approach consolidates the advantages of both the clustering and routing processes which prompts the greatest energy efficiency and network lifetime. The proposed model is reproduced under a broad arrangement of experiments, and the outcomes are approved under a few measures. The acquired trial result exhibited the predominant qualities of the improved particle swarm optimization–grey wolf optimization method under all the test cases. It enhances LEACH protocols in terms of energy efficiency, network Lifetime, and throughput. Maintaining the Energy Efficiency of a wireless sensor network has been a great concern nowadays. The main aim is to overcome the drawback of Improvement of the energy efficiency of a wireless sensor network. It may be improved with the performance of an optimization algorithm using swarm Intelligence. The novelty of the project is Network lifetime and Energy Consumption. The future of this project is to share the complete information which is present in the cluster head without any interruption

Fault Detection in HVDC System with Gray Wolf Optimization Algorithm Based on Artificial Neural Network

Various methods have been proposed to provide the protection necessitated by the high voltage direct current system. In this field, most of the research is confined to various types of DC and AC line faults and a maximum of two switching converter faults. The main contribution of this study is to use a new method for fault detection in HVDC systems, using the gray wolf optimization method along with artificial neural networks. Under this method, with the help of faulted and non-faulted signals, the features of the voltage and current signals are extracted in a much shorter period of the signal. Subsequently, differences are detected with the help of an artificial neural network. In the studied HVDC system, the behavior of the rectifier, along with its controllers and the required filters are completely modeled. In this study, other methods, such as artificial neural network, radial basis function, learning vector quantization, and self-organizing map, were tested and compared with the proposed method. To demonstrate the performance of the proposed method the accuracy, sensitivity, precision, Jaccard, and F1 score were calculated and obtained as 99.00%, 99.24%, 98.74%, 98.00%, and 98.99%, respectively. Finally, according to the simulation results, it became evident that this method could be a suitable method for fault detection in HVDC systems.

A Novel Horse Racing Algorithm Based MPPT Control for Standalone PV Power Systems

This paper proposes a novel maximum power point tracking (MPPT) method inspired by the horse racing game for standalone photovoltaic (PV) power systems, such that the highest PV power conversion efficiency is obtained. From the horse racing game rules, we develop the horse racing algorithm (HRA) with the qualifying stage and final ranking stage. The MPP can be searched even if there exist multiple local MPPs for the PV power system. Moreover, from the proposed horse racing algorithm, the calculation is reduced, so that the transient searching points are less than traditional methods, i.e., the transient oscillation is less during the MPPT control. Therefore, the HRA based MPPT method avoids local maximum power traps and achieves the MPP quickly even if considering partial shading influence and varying environment for PV panels. Evidence of the accuracy and effectiveness of the proposed HRA method is exhibited by simulation results. These results are also compared with typical particle swarm optimization (PSO) and grey wolf optimization (GWO) methods and shown better convergence time as well as transient oscillation. Within the range from 0.34 to 0.58 s, the proposed method has effectively tracked the global maximum power point, which is from 0.42 to 0.48 s faster than the conventional PSO technique and from 0.36 to 0.74 s faster than the GWO method. Finally, the obtained findings proved the effectiveness and superiority of the proposed HRA technique through experimental results. The fast response in terms of good transient oscillation and global power tracking time of the proposed method are from 0.40 to 1.0 s, while the PSO and GWO methods are from 1.56 to 1.6 s and from 1.9 to 2.2 s, respectively.

Optimised VMD based on Improved Grey Wolf for Human Pulse Wave Characterisation

To address the difficulty in determining the penalty factor α mode and number K for the Variational Mode Decomposition (VMD) human pulse wave signal, which makes it difficult to extract and analyse its features accurately, an Improved Grey Wolf Optimization (IGWO) method is proposed to optimise the VMD. In this paper, the optimal modal number K and penalty factor α in the VMD are determined by IGWO using Minimum Average Mutual Information (MAMI) as the fitness function. The proposed method is used to completely decompose the pulse wave into K Intrinsic Mode Functions (IMF), and the marginal spectral analysis of the decomposed modes is performed to calculate the spectral energy ratio. And the effectiveness of the proposed method is demonstrated experimentally.

An Innovative Prediction of Link Failure and Node Lifetime in Mobile Ad Hoc Networks Using Grey Wolf Optimization

The Path Restoration System in Mobile Ad hoc Network (MANET) was tough due to the changing environment. Data packets are lost if a link is broken while delivering information, and the system is vulnerable to various assaults. Considering this, we propose the Grey Wolf Optimization technique (GWO) to predict connection failure, link and node lifetime before broadcasting packets to avoid packet loss. To define the path, we used the Route Information Protocol (RIP). Following that, GWO is manually played; with this method, this research forecasts the node and lifetime, and achieves a packet delivery ratio of 0.7. The proposed Gray-Wolf algorithm achieves an efficient packet transmission rate and improves the early detection of links and node lifetimes to maintain path stability for data transmission. The proposed model reduces end-to-end delay, overhead, and packet drop. It improves the residual energy of nodes and the packet delivery ratio. Grey Wolf Optimization is one of many examining boosting methods activated by the grouping within the wolf family and the special hunting techniques used by grey wolves. As a result, the Grey Wolf optimization method was used to find the optimal result by mocking the overall characteristics of the grey wolf colony.

Agricultural waste-to-energy concerning a biofuel-fed molten carbonate fuel cell toward a novel trigeneration scheme; exergoeconomic/sustainability study and multi-objective optimization

Attributable to the global upsurge in demand for agricultural products, agricultural energy needs are extending widely, while water and energy sources have been mitigated in recent years. This study focused upon bringing a proper solution to the mentioned discrepancy concerning the waste-to-energy concept. With this in mind, a gasification process for an agricultural waste, i.e., rice husk, is considered in this study for the simultaneous production of power, freshwater, and hydrogen to meet the agricultural needs. Meanwhile, a novel trigeneration scheme comprising a biofuel-fed molten carbonate fuel cell, a humidification/dehumidification-based desalination, and a solid oxide electrolyzer cell is designed and scrutinized from the exergoeconomic and sustainability standpoints. The sensitivity analysis is implemented at sole and dual parametric forms, where five decision-makings and six performance metrics are defined. Subsequently, a soft computing study through an artificial neural network is conducted to optimize the designed system utilizing the multi-objective grey wolf optimization method. Here, the objective functions are exergy destruction rate, sustainability index, and products’ sum unit cost. The optimum electricity, freshwater, and hydrogen generation capacities are designated at 1442 kW, 4303 kg/h, and 51.51 kg/h. Besides, the aforementioned optimum objective functions are computed at 5084 kW, 0.0442 $/kWh, and 1.482, respectively.

Maximizing Output Power for Solar Panel using Grey Wolf Optimization

Obtaining the highest output power delivered from solar panel that leads to an amelioration in the efficiency of the solar system related to the weather conditions and the rate of solar radiation falling on the panel. The optimization in the solar system efficiency by MPPT is performed using the gray wolf optimization method by applying the proposed algorithm in order to maximize the output power. This means that the gray wolf technologies must increase the power received from the solar panels in order to increase the efficiency

Optimal DG allocation and sizing in distribution systems with Thevenin based impedance stability index

The applications of Distributed Generation (DG) play a significant role to provide the benefit to conventional distribution systems. However, the sizing and location of these DG units should be taken into consideration to get maximum gain and benefit. If DGs are not located and sized properly, the distribution system can be adversely affected. So, optimal allocation and sizing are needed to avoid instability problems and more costs to increase the efficiency and quality of energy in distribution systems. This paper addresses the optimal sizing and allocation of DGs for power losses, voltage profile and stability improvement, introducing a proposed stability index based on Thevenin impedance in a distribution network. The stability index is calculated by the proposed approach based on Thevenin. Thevenin is obtained based on a two-bus system by reduction of more than two bus systems. The optimal sizing and location of DGs are also obtained by operating DGs under different power factors. The reactive capability of DGs is also shown by simulations with DGs operated under different power factors. The problem of optimal sizing and location of DGs, which is a nonlinear optimization problem with discrete and continuous variables, is solved based on the mixed integer Genetic Algorithm (GA). Also, the proposed approach is verified by using Grey Wolf Optimization (GWO) method. The problem contains multi-objectives with three objective functions: system power losses, voltage deviation and the Thevenin based stability index. The proposed approach has been tested on the IEEE-69 and 118-bus test system to demonstrate its effectiveness. The comparison with some other methods shows that the proposed approach gives better results than other methods while reducing the power losses and improving voltage and system stability in all suggested scenarios by finding the optimal placement and sizing of DG units. It is also shown that the reduction in power loss, voltage and stability improvements are increased more by using the reactive power capability of DGs. The results show that optimum located and sized DGs not only decreases the power loss, but also improve the voltage profile and stability of the system.

Modified Quasi-Opposition-Based Grey Wolf Optimization for Mathematical and Electrical Benchmark Problems

This paper proposes a modified quasi-opposition-based grey wolf optimization (mQOGWO) method to solve complex constrained optimization problems. The effectiveness of mQOGWO is examined on (i) 23 mathematical benchmark functions with different dimensions and (ii) four practical complex constrained electrical problems that include economic dispatch of 15, 40, and 140 power generating units and a microgrid problem with different energy sources. The obtained results are compared with the reported results using other methods available in the literature. Considering the solution quality of all test cases, the proposed technique seems to be a promising alternative for solving complex constrained optimization problems.

IFS and SODA based computational method for fuzzy time series forecasting

Time series forecasting has fascinated a great deal of interest from various research communities due to its wide applications in medicine, economics, finance, engineering and many other crucial fields. Various studies in past have shown that intuitionistic fuzzy sets (IFSs) not only handle non-stochastic non-determinism in time series forecasting but also enhance accuracy in forecasted outputs. Clustering is another one of the methods that improves accuracy of time series forecasting. The contribution of this research work is a novel computational fuzzy time series (FTS) forecasting method which relies on IFSs and self-organized direction aware (SODA) approach of clustering. The usage of SODA aids in making the proposed FTS forecasting method as autonomous as feasible, as it does not require human intervention or prior knowledge of the data. Forecasted outputs in proposed FTS forecasting method are computed using a weighted formula and weights are optimized using grey wolf optimization (GWO) method. Proposed FTS is applied to forecast enrolments of the University of Alabama and market price of State Bank of India (SBI) share at Bombay stock exchange (BSE), India and performance is compared in terms of root mean square error (RMSE), average forecasting error (AFE) and mean absolute deviation (MAD). Goodness of the proposed FTS forecasting method in forecasting enrolments of the University of Alabama and market price of SBI share is also tested using coefficient of correlation and determination, criteria of Akaike and Bayesian information.

A multi-energy load prediction of a building using the multi-layer perceptron neural network method with different optimization algorithms

Since cooling and heating loads are recognized as key characteristics for evaluating the energy efficiency of buildings, it appears indisputable that they must be predicted and analyzed for residential structures. Accordingly, the multi-layer perceptron neural network is applied for predicting the heating and cooling loads using the experimental dataset. The used dataset is obtained by monitoring the impact of the building's dimensions on energy consumption. To optimize the training process of the multi-layer perceptron neural network, several optimizers are employed. Besides, different statistical performance indicators are considered to see which selected optimizer outperforms the rest in terms of accuracy and authenticity. The obtained results emphasize the remarkable performance of adaptive chaotic grey wolf optimization, which can be used to train the multi-layer perceptron neural network and forecast the buildings’ energy consumption with the highest accuracy. According to the obtained results, the hybrid multi-layer perceptron neural network- adaptive chaotic grey wolf optimization method demonstrates the best performance. The optimum number of neurons in the hidden layer is obtained to be 15. Also, based on the statistical performance indicators, the selected method reveals an R2 of 0.9123 and 0.9419 for cooling and heating loads, respectively.

Multi-objective optimal power flow based gray wolf optimization method

Introduction. One of predominant problems in energy systems is the economic operation of electric energy generating systems. In this paper, one a new evolutionary optimization approach, based on the behavior of meta-heuristic called grey wolf optimization is applied to solve the single and multi-objective optimal power flow and emission index problems. Problem. The optimal power flow are non-linear and non-convex very constrained optimization problems. Goal is to minimize an objective function necessary for a best balance between the energy production and its consumption, which is presented as a nonlinear function, taking into account of the equality and inequality constraints. Methodology. The grey wolf optimization algorithm is a nature inspired comprehensive optimization method, used to determine the optimal values of the continuous and discrete control variables. Practical value. The effectiveness and robustness of the proposed method have been examined and tested on the standard IEEE 30-bus test system with multi-objective optimization problem. The results of proposed method have been compared and validated with hose known references published recently. Originality. The results are promising and show the effectiveness and robustness of proposed approach.

Optimization of Permanent Magnet Synchronous Generator Output Power in Wind Power Plants Using the Gray Wolf Optimization Method

Wind is a renewable energy that is expected to meet the availability of energy sources as needed. The electrical power produced by the Permanent Magnet Synchronous Generator at Wind Power Plants in Indonesia is influenced by low wind conditions with wind speeds only ranging from 2.5 to 6 m/s. So that the output voltage can produce optimal power with a low and fluctuating average wind speed, an optimization will be carried out using the Gray Wolf Optimization method which is inspired by the social strata of wolves in hunting their prey. This method works by measuring the voltage and current at the load to get optimal power and maximize the existing power extraction using Maximum Power Point Tracking.

Energy-Efficient Node Localization Algorithm Based on Gauss-Newton Method and Grey Wolf Optimization Algorithm

Node localization process is a crucial prerequisite in the area of Wireless Sensor Networks (WSNs). The algorithms for node localization process can either range-based or range-free. Range-free algorithms are preferred over range-based ones due to their cost-effectiveness. DV-Hop along with its variants is normally well-liked range-free algorithm because of its straightforwardness, scalability and distributed nature, but it has some disadvantages such as poor accuracy and high-power utilization. To deal with these limitations, this paper introduces an algorithm, called GWOGN-LA. GWOGN-LA improves accuracy by applying Grey-Wolf Optimization and Gauss-Newton method. The proposed algorithm restricts the forwarding of packets in order to limit energy consumption. Simulation results show that given proposal outperforms other state-of-art algorithms in terms of accuracy and power consumption.

State feedback based on grey wolf optimizer controller for two-wheeled self-balancing robot

Abstract The two-wheeled self-balancing robot (TWSBR) is based on the axletree and inverted pendulum. Its balancing problem requires a control action. To speed up the response of the robot and minimize the steady state error, in this article, a grey wolf optimizer (GWO) method is proposed for TWSBR control based on state space feedback control technique. The controller stabilizes the balancing robot and minimizes the overshoot value of the system. The dynamic model of the system is derived based on Euler formula and linearized to state space representation to enhance the control technique. Then, the GWO optimizes the state feedback controller parameters. Simulation results show that the system reaches the zero steady-state error with less than 2 ms, which proves the effectiveness of the proposed controller over the classical state feedback controller in terms of fast response, very small overall error, and minimum overshoot.