Grey Wolf Algorithm Research Articles

Piston correction of sparse aperture systems using hybrid optimization algorithm

In a sparse aperture imaging system, the piston error affects the co-phase of subapertures and reduces the imaging resolution. Based on the image quality evaluation function, the piston error correction can be converted into a class of optimization problems. In this paper, a metaheuristic algorithm is used to implement global search based on stochastic parallel gradient descent algorithm. A hybrid algorithm is obtained by combining the stochastic parallel gradient descent algorithm with the Gray Wolf algorithm. The hybrid algorithm is developed at the best three positions of the population (the head wolf) using the gradient direction of the SPGD algorithm, and the other populations keep moving toward the best three head wolves. This hybrid algorithm balances exploration and exploitation better in the piston error correction of sparse aperture systems. The hybrid algorithm has a good optimization speed while satisfying the correction accuracy. The performance of this hybrid algorithm is verified in simulations with three-aperture and seven-aperture systems.

A new machine vision detection method for identifying and screening out various large foreign objects on coal belt conveyor lines

Large foreign object transporting by coal mine conveyor belt may lead to production safety hazards. To reduce safety accidents during coal mining, a large foreign object detection method based on machine vision is proposed in this paper. An adaptive weighted multi-scale Retinex (MSR) image enhancement algorithm is proposed to improve the captured image quality of the belt conveyor line. An improved multi-scale template matching algorithm is designed by combining the frame difference and area methods to screen and identify large foreign objects mixed in coals. The multi-layer perceptron (MLP) network optimized by the Gray Wolf algorithm is introduced to identify the large objects. Experimental results show that the identification accuracy reaches 98.8% for the large foreign objects. Furthermore, industrial field test is carried out in the Gaoyang coal mine, and the filed test results demonstrate that the identification accuracy of the proposed method is more than 95%. Hence, the proposed method meets the industrial detection requirements and can be used in practices for detecting the large foreign objects.

Research on Rolling Bearing Fault Diagnosis Based on Variational Modal Decomposition Parameter Optimization and an Improved Support Vector Machine

Aiming at the problems of modal aliasing and poor noise resistance when processing the vibration acceleration signal of rolling bearings by empirical modal decomposition (EMD), a variational modal decomposition (VMD) method based on parameter optimization is proposed. Combined with the improved particle swarm optimization algorithm (IPSO) and improved envelope entropy, the VMD decomposition layers and penalty parameters were optimized. The components with high correlation coefficients with the original signal were screened out, and the fault characteristics were extracted by combining the sample entropy. Aiming at the low classification accuracy of the support vector machine with fixed parameters in the fault diagnosis stage and the defects of the gray wolf algorithm, such as insufficient population diversity and large influence of the initial population on the optimization effect, an improved gray wolf algorithm (IGWO) based on multistrategy improvement is proposed. The IGWO was combined with the support vector machine to obtain an improved gray wolf algorithm optimization support vector machine (IGWO-SVM). The rolling bearing fault diagnosis test bench is established to collect the vibration acceleration signals of rolling bearing under different states. The experimental results show that the fault diagnosis of rolling bearings with strong noise can be effectively realized by applying the above methods, and the average fault diagnosis accuracy rate reaches 98.875%.

The research of a novel WOG-YOLO algorithm for autonomous driving object detection

Object detection has been one of the critical technologies in autonomous driving. To improve the detection precision, a novel optimization algorithm is presented to enhance the performance of the YOLOv5 model. First, by improving the hunting behavior of the grey wolf algorithm(GWO) and incorporating it into the whale optimization algorithm(WOA), a modified whale optimization algorithm(MWOA) is proposed. The MWOA leverages the population’s concentration ratio to calculate p_h for selecting the hunting branch of GWO or WOA. Tested by six benchmark functions, MWOA is proven to possess better global search ability and stability. Second, the C3 module in YOLOv5 is substituted by G-C3, and an extra detection head is added, thus a highly optimizable detection G-YOLO network is constructed. Based on the self-built dataset, 12 initial hyperparameters in the G-YOLO model are optimized by MWOA using a score fitness function of compound indicators, thus the final hyperparameters are optimized and the whale optimization G-YOLO (WOG-YOLO) model is obtained. In comparison with the YOLOv5s model, the overall mAP increases by 1.7%, the mAP of pedestrians increases by 2.6% and the mAP of cyclists increases by 2.3%.

MPPT Control Research of Improved Gray Wolf Algorithm According to Levy Flight and Greedy Strategy

MPPT control study of improved Gray Wolf algorithm (GWO) according to Levy flight and greedy strategy: Solving the problems of maximum power point tracking (MPPT) with traditional Gray Wolf algorithm (GWO) under local shadows of photovoltaic arrays and sudden environmental changes, which is easily trapped in local optimum and has a slow convergence rate, and poor solution accuracy, a improved gray wolf algorithm (LGWO) according to Levy flight and greedy strategy is presented. It is used for the first time for maximum power point tracking under partial shadow and dynamic shadow changes of photovoltaic array. It is based on the traditional Grey Wolf algorithm (GWO), it introduces Levy’s flight search strategy, improve the algorithm of global search ability, expands search range, and filters optimal range through greedy strategy to further accelerate the convergence speed. MPPT simulation model according to Boost circuit is built using MATLAB/Simulink to verify. Experiments in the presence of partial shadows and shadow mutations. The results of simulation experiments show that the ameliorated Gray Wolf algorithm (LGWO) improves the tracking accuracy of MPPT by 0.03%, improves the convergence speed by 1.1 times and is more stable after reaching the maximum number of iterations. This verifies the feasibility and superiority of the ameliorated Grey Wolf algorithm in maximum power point tracking control.

CLUSTERIZATION OF DATA ARRAYS BASED ON THE MODIFIED GRAY WOLF ALGORITHM

Context. The task of clustering arrays of multidimensional data, the main goal of which is to find classes of observations that are homogeneous in the sense of the accepted metric, is an important part of the intelligent data analysis of Data Mining. From a computational point of view, the problem of clustering turns into the problem of finding local extrema of a multiextreme function, which are repeatedly started from different points of the original data array. To speed up the process of finding these extrema using the ideas of evolutionary optimization, which includes algorithms inspired by nature, swarm algorithms, population algorithms, etc.
 Objective. The purpose of the work is to introduce a procedure for clustering data arrays based on the improved gray wolf algorithm.
 Method. A method of clustering data arrays based on the modified gray wolf algorithm is introduced. The advantage of the proposed approach is a reduction in the time of solving optimization problems in conditions where clusters are overlap. A feature of the proposed method is computational simplicity and high speed, due to the fact that the entire array is processed only once, that is, eliminates the need for multi-era self-learning, implemented in traditional fuzzy clustering algorithms.
 Results. The results of the experiments confirm the effectiveness of the proposed approach in clustering problems under the condition of classes that overlap and allow us to recommend the proposed method for use in practice to solve problems of automatic clustering big data.
 Conclusions. A method of clustering data arrays based on the modified gray wolf algorithm is introduced. The advantage of the proposed approach is the reduction of time for solving optimization problems. The results of the experiments confirm the effectiveness of the proposed approach in clustering problems under the conditions of overlapping clusters.

Tool Wear Prediction in Glass Fiber Reinforced Polymer Small-Hole Drilling Based on an Improved Circle Chaotic Mapping Grey Wolf Algorithm for BP Neural Network

Glass fiber reinforced polymer (GFRP) is a typical difficult-to-process material. Its drilling quality is directly affected by the processing technology and tool life; burrs, tearing, delamination and other defects will reduce the service life of GFRP structural parts. Through drilling damage and tool wear experiments of GFRP, the thrust force, vibration amplitude, the number of processed holes, feed rate and cutting speed were found to be the main factors in drilling damage and tool wear. Using those main factors as the input layer, a tool wear and delamination factors prediction model was established based on an improved circle chaotic mapping (CCM) Grey Wolf algorithm for a back propagation (BP) neural network. Compared with the original BP neural network, the maximum prediction error of the improved BP neural network model was reduced by 71.2% and the root mean square (RMS) prediction error was reduced by 63.82%. The maximum prediction error of the delamination factor at the entrance was less than 3%, and the maximum prediction error of the delamination factor at the exit was less than 1%. The prediction results showed that the BP neural network model optimized by an improved circle chaotic mapping Grey Wolf algorithm can better predict the GFRP drilling quality and tool wear, and had higher accuracy, optimization efficiency and better robustness than the ordinary BP neural network.

Use an adaptive network fuzzy inference system model for estimating the compressive strength of high-performance concrete with two optimizers improved Grey Wolf algorithm and Dragonfly optimization algorithm

Use an adaptive network fuzzy inference system model for estimating the compressive strength of high-performance concrete with two optimizers improved Grey Wolf algorithm and Dragonfly optimization algorithm

Microservice combination optimisation based on improved gray wolf algorithm

Microservices architecture is a new paradigm for application development. The problem of optimising the performance of microservice architectures from a non-functional perspective is a typical Nondeterministic Polynomial (NP) problem. Therefore, aiming to quantify the non-functional requirements of computing microservice systems, while solving the problem of latency in computing the best combination of services with the maximum QoS objective function value, this paper proposes a microservice combination approach based on the QoS model and a CGWO algorithm for optimisation computation for this model. The experimental results verify that the error rate of the method is only 0.528% on the non-functional combination optimisation problem, and the computational efficiency of the algorithm increases by 97.29% when the complexity of the problem search space increases, while CGWO improves 65.97% and 81.25% respectively in the accuracy of optimisation compared to the prototype of the algorithm (GWO), and has a stable optimisation performance, aspect. It proves that the research in this paper has a high advantage in automatically searching for the best QoS for the microservice combination problem.

Research on Data Classification Method of Optimized Support Vector Machine Based on Gray Wolf Algorithm

The data classification method based on support vector machine (SVM) has been widely used in various studies as a non-linear, high precision, and good generalization ability machine learning method. Among them, the kernel function and its parameters have a great impact on the classification accuracy. In order to find the optimal parameters to improve the classification accuracy of SVM, this paper proposes a data multi-classification method based on gray wolf algorithm optimized SVM(GWO-SVM). In this paper, the iris data set is used to test the performance of GWO-SVM, and the classification result is compared with those based on genetic algorithm (GA), particle swarm optimization (PSO) and the original SVM model. The test results show that the GWO-SVM model has a higher recognition and classification accuracy than the other three models, and has the shortest running time, which has obvious advantages and can effectively improve the classification accuracy of SVM. This method has practical significance in image classification, text classification, and fault detection.

Risk assessment of cardiovascular disease based on SOLSSA-CatBoost model

Cardiovascular disease (CVD) has become a significant public health problem affecting national economic and social development, and ranks among the top causes of death in the world. Thus, people pay increasing attention to the prevention, control, and risk assessment of CVD. In this paper, an improved sparrow search algorithm (SSA) is designed to optimize the parameters of Categorical Boosting (CatBoost) model, and it is applied to the risk assessment of CVD. The contributions of this research are mainly in the following aspects: (1) In the position update formula of the discoverer, the salp swarm algorithm is integrated, the global optimal solution of the previous generation is added to improve the global search ability and local development ability of SSA; (2) Using Opposition-based Learning (OBL) and Lateral mutation strategy to improve the search ability of the worst individual; (3) Sparrow search algorithm based on salp swarm algorithm, OBL and Lateral mutation strategy (SOLSSA) is used to optimize parameters of CatBoost to improve the prediction effect, and the experiments are carried out for the proposed model (SOLSSA-CatBoost) using two CVD data sets on Kaggle. The proposed model is compared with six machine learning models, including random forest (RF), logistic regression (LR), k-nearest neighbor (KNN), support vector machine (SVM), light gradient Boosting (LGB) and CatBoost, and is also compared with other four optimization algorithms (whale optimization algorithm (WOA), gray wolf algorithm (GWO), seagull optimization algorithm (SOA) and SSA) in optimizing the performance of the CatBoost. The experimental results show that compared with other comparison algorithms, SOLSSA-CatBoost has better prediction effect on test set, with F1-score reaching 90% and 81.51% in two CVD data sets respectively. The SOLSSA-CatBoost model in this paper can make a more accurate prediction of patients' disease risk, and provide a certain basis for doctors to judge the condition.

Research on Control Optimization Method of Mine Ventilation System Based on Intelligent Optimization Algorithm

<p>In view of the complex ventilation network in the mine, many influencing factors, and the complex establishment of the air demand model, this paper establishes an air volume optimization model with the minimum energy consumption of the entire ventilation network as the optimization objective, and adds the air volume balance law conditions, the air pressure balance law conditions, the branch air demand law conditions, the air resistance regulation law conditions, and the fan conditions as constraints to the model. Then, the intelligent algorithm is used to solve the problem. In order to make the intelligent algorithm converge faster and solve the problem optimally, the Grey Wolf algorithm is improved. Finally, the model and the improved intelligent algorithm are used to optimize the ventilation regulation of the actual operating coal mine, and the air volume control scheme is given.</p> <p> </p>

Research on pollution identification of catenary insulator based on Improved Gray Wolf algorithm

Support vector machines are used as classifiers for the contact network insulator fouling level recognition model. To improve the accuracy of the recognition model, the search strategy of the gray wolf algorithm is improved, the population is initialized using chaotic mapping rules, and the position of individual gray wolves is updated based on a dynamic weighting strategy. The improved algorithm is applied to the kernel parameter and penalty factor seeking of SVM to find out the optimized parameters to construct the recognition model, and the recognition status of several other intelligent algorithms is compared. The experimental results show that the proposed method can effectively improve the accuracy of insulator fouling level recognition and has certain engineering application value.

Short-term Power Load Forecasting Based on Orthogonal PCA-LPP Dimension Reduction and IGWO-BiLSTM

Background: Accurate power load forecasting is of great significance in ensuring power load planning, reliability and economic operation. The traditional power load is easy to be affected by climate, environment, date type and other factors, resulting in the problem of poor forecasting accuracy. Therefore, it is necessary to study power load forecasting. Objective: Through machine learning, dimension reduction method and intelligent optimization algorithm, the accuracy of load forecasting is improved Methods: In order to fully extract load information and improve the accuracy of short-term load forecasting for campus electricity, an improved combination of orthogonal dimensionality reduction and Bilstm is proposed to optimize the hyperparameters in BiLSTM using an improved gray wolf algorithm. Firstly, using the advantages of principal component analysis (PCA) and Locality Preserving Projection (LPP) to maintain the global and local structure of the data, respectively, the Orthogonal PCA-LPP（OPCA-LPP） dimensionality reduction method is proposed to reduce the dimensionality of the original multidimensional data. Finally, the dimensionality-reduced data is used as the input of BiLSTM and optimized by the improved Gray Wolf algorithm, which can enhance the prediction capability of the model and thus achieve accurate prediction of short-term electric load. Results: The Mae and RMSE of this paper are 1.6585 and 1.7602 respectively. The results show that the method proposed in this paper is reasonable Conclusion: This method is applied to power load forecasting. The comparative experimental results show that this method reduces the dimension of data input, simplifies the complexity of network input data, and improves the accuracy of load forecasting. Compared with other methods, it can effectively improve the accuracy of load forecasting, and provide a basis for formulating reasonable power grid operation mode and balanced dispatching of power grid.

Fractional multivariate grey Bernoulli model combined with improved grey wolf algorithm: Application in short-term power load forecasting

The accurate prediction of power load is helpful to make reasonable power generation plans and scientific dispatching schemes and achieve the goal of energy saving and emission reduction.There are many factors influencing the power load, and there may be a nonlinear relationship between the power load and these influencing factors. A new fractional multivariate grey Bernoulli model, referred to as MFGBM (q, r, N), is developed in this article for the short-term prediction of power load. The fractional differential equation and fractional accumulation generation are integrated into MFGBM (q, r, N). Second, this paper improves the grey wolf algorithm to better optimize many parameters in the model. The algorithm is improved by using a chaotic Tent map to optimize the initial population composition, adding inertia weights to change the position vector of the grey wolf, and introducing a nonlinear function and Lévy flight to enhance local exploitation and global exploration ability. Finally, this paper selects the daytime and nighttime power loads in Australia and takes the electricity price, humidity, and temperature as the influencing factors to validate the prediction capability of MFGBM (q, r, N). Our findings indicate that MFGBM (q, r, N) is highly applicable to short-term power system prediction.

Parameter Identification of Doubly-Fed Induction Wind Turbine Based on the ISIAGWO Algorithm

Variations in generator parameters that occur during the operation of a doubly-fed induction wind turbine (DFIG) constitute a significant factor in the degradation of control performance. To address the problem of difficulty in identifying multiple parameters simultaneously in DFIG, a parameter identification method depending on the adaptive grey wolf algorithm with an information-sharing search strategy (ISIAGWO) is proposed to solve the problem of low accuracy and slow identification speed of multiple parameters in DFIG. The easily obtainable generator outlet current was selected as the observed quantity, and the trajectory sensitivity analysis was performed on the five electrical parameters of the DFIG to derive its discriminability. Finally, the parameter recognition of the DFIG was carried out using the ISIAGWO algorithm in the MATLAB/Simulink simulation software, applying the proposed calculation functions. The experimental results show that the ISIAGWO algorithm has better identification accuracy, stability, and convergence for DFIG’s generator parameter identification.

International Gateway Hub Construction and Air Logistics Industry Development Based on Multivariate Strategy Improvement Grey Wolf Algorithm

INTRODUCTION: The global economic development pattern is changing, international economic and trade rules are being questioned, the global economy is not optimistic in the post-epidemic era, and it is more difficult to find growth points. OBJECTIVES: How to achieve economic growth has become a more complex global issue. China, as the world's second largest economy and the world's second largest consumer market, is also a key issue to be addressed: how to promote a strong economic recovery and maintain a high level of public confidence in the state of our economy. The aviation industry chain plays a relatively good role in achieving supply stability as well as industry chain stability, and has a strong positive effect on the domestic and foreign double-cycle strategy. Shenzhen, as a window of China's opening up, has policies, resources and places to promote the development of aviation logistics industry. METHODS: Information is obtained through literature review and fieldwork. Through the history of the development of aviation industry, the history of aviation logistics development and the current situation of the construction of China's aviation logistics are deeply reviewed and sorted out. RESULTS: The specific path to be taken for the development of aviation industry, aviation logistics development and air express and related industries is explained. It also makes a long-term plan for the subsequent development of aviation industry, aviation logistics and air express development of Shenzhen airport, and analyzes the related objectives, and finally uses the relevant theoretical knowledge to make a relevant outlook on aviation industry, aviation logistics and aviation industry development, and summarizes the experience of aviation logistics company development. Combined with the construction of Shenzhen domestic airport, the relevant design concept is proposed. CONCLUSION: Through the analysis of different routes, we finally found that the air logistics business of Shenzhen airport should take the road of synergistic development. In this way, the air logistics of Shenzhen airport should be built around five implementation points and six guarantee points in order to achieve a high-quality development in line with the new era.

A Coal Gangue Identification Method Based on HOG Combined with LBP Features and Improved Support Vector Machine

Identification of coal and gangue is one of the important problems in the coal industry. To improve the accuracy of coal gangue identification in the coal mining process, a coal gangue identification method based on histogram of oriented gradient (HOG) combined with local binary pattern (LBP) features and improved support vector machine (SVM) was proposed. First, according to the actual underground working environment of the mine, a machine vision platform for coal gangue identification was built and the coal gangue image acquisition experiment was carried out. Then, the images of coal and gangue were denoised by median filtering, and the coal and gangue features were extracted by using the HOG combined with LBP feature extraction algorithm, and these features were normalized and principal component analysis (PCA) reduced dimension to remove the correlation and redundancy between the features. Finally, SVM, SVM optimized by genetic algorithm (GA-SVM), SVM optimized by particle swarm optimization (PSO-SVM) algorithm, and SVM optimized by grey wolf optimization (GWO-SVM) algorithm were used as classifiers for identification and classification, respectively. The experimental results show that the GWO-SVM classification model has the highest accuracy, and the average classification accuracies were 96.49% and 94.82% of the training set and test set, respectively, which shows that grey wolf algorithm to optimize support vector machine has a good effect on classification of coal gangue images, which proves the feasibility and accuracy of the proposed method.

Optimization assisted frequent pattern mining for data replication in cloud: Combining sealion and grey wolf algorithm

It is critical in cloud computing to have excellent data accessibility and system performance. To improve system availability, commonly used data should be duplicated to many places, allowing users to access it from a nearby site. Deciding on a sensible number and location for replicas is a difficult problem in cloud computing. Therefore, a novel Data Replication system based on data mining techniques is being proposed in this research work. The data replication is done here by locating commonly utilized data patterns in a node's massive database. This will be accomplished using an optimization-assisted frequent pattern mining approach, with a novel hybrid algorithm performing the best threshold selection. The proposed hybrid algorithm referred to as Greywolves Updated Exploration and Exploitation with Sealion Behaviour (GUEES), hybrids the concept of Sealion Optimization Model (SLnO) and Grey wolf optimization (GWO) algorithms. Apart from this, the mining will be carried out under the defining dual constraints such as (i) Prioritization and (ii) Cost. The prioritization falls under two cases: queuing both high and low-priority data, and the cost relies on the evaluation of storage demand. The high-priority queues are optimized with the GUEES model. Finally, a comparative validation is carried out to validate the efficiency of the adopted model. Accordingly, when the number of requests=1000, the network usage of the proposed model is 35.07%, 34.9%, 30.5%, 29.23%, 24.57%, 16.8%, and 16.85% higher than the existing methods like SMO, LA, ROA, GWO, SLnO, PSO, HCS, respectively.

Tunicate swarm-based grey wolf algorithm for fetal heart chamber segmentation and classification: a heuristic-based optimal feature selection concept

Ultrasound image quality management and assessment are an important stage in clinical diagnosis. This operation is often carried out manually, which has several issues, including reliance on the operator’s experience, lengthy labor, and considerable intra-observer variance. As a result, automatic quality evaluation of Ultrasound images is particularly desirable in medical applications. This research work plans to perform the fetal heart chamber segmentation and classification using the novel intelligent technology named as hybrid optimization algorithm Tunicate Swarm-based Grey Wolf Algorithm (TS-GWA). Initially, the US fetal images data is collected and data undergoes the preprocessing using the total variation technique. From the preprocessed images, the optimal features are extracted using the TF-IDF approach. Then, Segmentation is processed on optimally selected features using Spatially Regularized Discriminative Correlation Filters (SRDCF) method. In the final step, the classification of fetal images is done using the Modified Long Short-Term Memory (MLSTM) Network. The fitness function behind the optimal feature selection as well as the hidden neuron optimization of MLSTM is the maximization of PSNR and minimization of MSE. The PSNR value is improved from 3.1 to 9.8 in the proposed method and accuracy of the proposed classification algorithm is improved from 1.9 to 12.13 compared to other existing techniques. The generalization ability and the adaptability of proposed TS-GWA method are described by conducting the various performance analysis. Extensive performance result shows that proposed intelligent techniques performs better than the existing segmentation methods.