Improved Grey Wolf Optimization Algorithm Research Articles

Temperature Compensation of Wind Tunnel Balance Signal Detection System Based on IGWO-ELM.

The wind tunnel balance signal detection system is widely employed in aerospace applications for the accurate and automated measurement of aerodynamic forces and moments. However, measurement errors arise under different environmental temperature. This paper addresses the issue of measurement accuracy under different temperature conditions by proposing a temperature compensation method based on an improved gray wolf optimization (IGWO) algorithm and optimized extreme learning machine (ELM). The IGWO algorithm is enhanced by improving the initial population position, convergence factor, and iteration weights of the gray wolf optimization algorithm. Subsequently, the IGWO algorithm is employed to determine the optimal network parameters for the ELM. The calibration decoupling experiment and high-low temperature experiment are designed and carried out. On this basis, ELM, GWO-ELM, PSO-ELM, GWO-RBFNN and IGWO-ELM are used for temperature compensation experiments. The experimental results show that IGWO-ELM has a good temperature compensation effect, reducing the measurement error from 20%FS to within 0.04%FS. Consequently, the accuracy and stability of the wind tunnel balance signal detection system under different temperature environments are enhanced.

Enhanced Multi-Objective Power Flow Optimization for IEEE-118 Bus Test System using Advanced Grey Wolf Optimization Algorithm

This research article thoroughly examines the implementation of an enhanced Grey Wolf Optimization (GWO) method for Multi-Objective Power Flow Optimization (MO-PFO) in the IEEE-118 bus test system. Given the growing complexity and uncertainty in modern power systems, power flow optimization presents a crucial challenge. The suggested enhanced GWO algorithm improves the performance of the traditional GWO for MO-PFO issues while addressing its inherent flaws.
 The goal of this study is to obtain optimal solutions concurrently for multiple objectives, including the reduction of power losses, voltage variations, and the enhancement of system stability. In the Related Works section, I review the research on power flow optimization in the literature, which also showcases the improvements achieved by applying various optimization techniques and discusses the benefits and drawbacks of traditional optimization methods and their effectiveness in dealing with MO-PFO issues.
 The improved GWO algorithm, designed specifically for MO-PFO, is described in detail in the Methodology section. I explain the adjustments made to the regular GWO algorithm and introduce a cutting-edge method for handling multiple objectives. In the IEEE-118 bus test system, I present the mathematical model of the MO-PFO problem and develop fitness functions for each objective.
 Furthermore, I describe the implementation of the enhanced GWO algorithm, including parameter settings and termination criteria. The proposed algorithm demonstrates its capability to effectively handle the multi-objective nature of power flow optimization, thereby paving the way for future power system operations that are more dependable and sustainable

Research on control strategy of an active magnetorheological fluid bearing based on improved gray wolf optimization approach

In order to address the problems of insufficient load capacity and rotor vibration, an active fluid-film bearing lubricated with magnetorheological fluid (MRF) is proposed. First, the geometry of the MRF fluid-film bearing is designed and its intelligent lubrication mechanism is analyzed to clarify its advantages. In addition, mathematical model of MRF fluid-film bearing-rotor system is derived, and FEM model is utilized to obtain stiffness and damping coefficients to supplement mathematical model. Moreover, an improved gray wolf optimization (IGWO) algorithm is developed to tune the PID controller parameters. The validity of the proposed method is verified by numerical simulation. Furthermore, the simulation results show that with the increasing of current magnitude, the orbits of shaft center decrease. Under the presence of magnetic fields, the shaft center orbits of the MRF bearing can converge to a point, and therefore this bearing has ability to suppress rotor vibration. Finally, IGWO-PID controller has better response characteristics than GWO, PSO, and GA algorithms, and hence the IGWO algorithm can find the more appropriate PID controller parameters, that the validity of the improved algorithm is further proved. Therefore, the active bearing and its research findings provide new reference for MRF vibration control in the field of journal bearing lubrication.

A new weak fault diagnosis approach for train bearings based on improved grey wolf optimizer and adaptive variational mode decomposition

Accurately identifying the health status of train running gear bearings is crucial to ensure the quality of operation. As the early fault information of bearings is weak and submerged in the complex noise environment, which is difficult to diagnose. Therefore, a new weak fault diagnosis approach for train running gear bearings based on variational mode decomposition (VMD) with improved performance and refined weighted kurtosis (RWK) index is proposed to solve this problem. First, an improved grey wolf optimizer (IGWO) based on a variety of strategies is proposed. Secondly, the VMD performance is improved using the IGWO algorithm, and the improved VMD is used to process the early weak signals of bearings. A new fault-sensitive index called the RWK is proposed to detect the mode with the most fault information. Finally, the envelope analysis of the characteristic signals is performed to achieve the early weak fault diagnosis of bearings. Compared with the other nine optimization algorithms, the IGWO algorithm has strong optimization ability, stable performance and a fast convergence speed. Four cases verify that the RWK index has the highest sensitivity to fault information and can more effectively filter out modal components containing rich fault information than the comparison methods.

A Novel IoT-Enabled Healthcare Monitoring Framework and Improved Grey Wolf Optimization Algorithm-Based Deep Convolution Neural Network Model for Early Diagnosis of Lung Cancer.

Lung cancer is a high-risk disease that causes mortality worldwide; nevertheless, lung nodules are the main manifestation that can help to diagnose lung cancer at an early stage, lowering the workload of radiologists and boosting the rate of diagnosis. Artificial intelligence-based neural networks are promising technologies for automatically detecting lung nodules employing patient monitoring data acquired from sensor technology through an Internet-of-Things (IoT)-based patient monitoring system. However, the standard neural networks rely on manually acquired features, which reduces the effectiveness of detection. In this paper, we provide a novel IoT-enabled healthcare monitoring platform and an improved grey-wolf optimization (IGWO)-based deep convulution neural network (DCNN) model for lung cancer detection. The Tasmanian Devil Optimization (TDO) algorithm is utilized to select the most pertinent features for diagnosing lung nodules, and the convergence rate of the standard grey wolf optimization (GWO) algorithm is modified, resulting in an improved GWO algorithm. Consequently, an IGWO-based DCNN is trained on the optimal features obtained from the IoT platform, and the findings are saved in the cloud for the doctor's judgment. The model is built on an Android platform with DCNN-enabled Python libraries, and the findings are evaluated against cutting-edge lung cancer detection models.

Calculation Method of Hosting Capacity for Distributed Grid-connected Photovoltaic Based on IGWO

In recent years, the distribution grid has been taking more and more risks due to the large-scale access of distributed photovoltaics (DPV), including potential problems such as voltage overrun and tidal current reversal, which seriously threaten grid security. Therefore, studying the maximum access capacity of distributed photovoltaics in the distribution grid is a research need nowadays. In this paper, we first establish a distributed PV grid-connected optimization operation model for distribution networks, taking into account multi-dimensional constraints such as voltage, load capacity, and active power, and then propose a grey wolf optimization based on particle swarm and Bernoulli chaos mapping, using chaos mapping initialization population, nonlinear convergence factor, and position update idea in the particle swarm algorithm to increase the initial population diversity and solution accuracy. Then, we use the improved grey wolf optimization algorithm (IGWO) as the basis for solving the maximum access capacity of distributed PV for distribution networks. Finally, simulations are conducted on the IEEE-33 node system to exemplify the previously developed model and algorithm.

Research on Grey Wolf Optimization Algorithm Based on Adaptive Adjustment Strategy

A grey wolf optimization algorithm based on an adaptive adjustment strategy (Improvements-Grey Wolf Optimization, IGWO) is proposed in this paper to address the issues with the Grey Wolf Optimization (GWO) algorithm, which has a slow convergence speed in the later stages and where the local search and global search cannot be taken into effective balance. First, a chaotic logistic map is used to initialize the population. Next, an inverse trigonometric function-based mathematical model is developed to achieve convergence purposes. Finally, a new location update method is used to update the next-generation location to improve search traversability. Additionally, the CEC2013-Benchmark standard test function was used to evaluate the benefits of performance compared to the standard GWO, PSO, and DEGWO (DE-Grey wolf optimization) algorithms. The improved GWO algorithm has greatly increased the convergence speed, search capability, and stability. It performs better than the other three algorithms as well.

Blockchain based Securing Medical Records in Big Data Analytics

The patient privacy is danger while medical records and data are transmitted or share beyond secure big data. This is because violations push them to the margins and they begin to avoid fully revealing their stages. This kind of stages contains negative impact in scientific investigate. To overcome this issue, Secure Block Chain System for Managing and Sharing Electronic Medical Records in Big Data Field is proposed. In this manuscript, a Cryptographic Hash Generator (CHG) technique based Secured and Trusted Data storage and transmission using Block Chain (BC) in Hadoop Distributed File System (HDFS). Initially, the Big data collected from the health care center is partitioned into sensitive and insensitive data. Block chain system utilizes an asymmetric cryptography for validating transactions authentication. Here, the user key is created through secured bitwise cryptographic hash generator (CHG) while there is required to fetch the newly record for usage. In block chain system, when a user seeking data from a healthcare application have forward a request to CHG. The message is send back to the user with a secret key for confirmation. The key can be decrypted or even denied access if only a valid user allows the user to link to this cluster. Only sensitive data were selected to the process of encryption for the process of encryption, this CHG technique employs the Discrete Shearlet Transform (DST) for encrypting the data, and the data’s are warehoused in the block chain to upgrade the level of security. And the insensitive data are put directly on the Hadoop Distributed File System. During the verification process, CHG is utilized for creating the request forward through the user. The operator creates the purpose of remote key to create the block (request) and signing the request using transaction private key, then forward to request queuing. To validate a request, the request from the queue is supplied first and an Improved Grey wolf Optimization algorithm (IGWO) is utilized to determine the optimal request that is fetch through the consensus node for initiating the process of validation. After accepting the user’s request, access is given to the user associated with input or requested data, then the verified request is set to broadcast. The proposed method is executed in JAVA and Hadoop platform. Experimental results show that the proposed BC-CHG-DST-IGWOA shows better performances of higher Efficiency 20.14%, 31.25%, 24.33%, 14.69%, lower time 16.12%, 15.09%, 21.36%, 46.26% compared with the existing methods, such as medical records managing and securing blockchain based system supported by genetic algorithm with discrete wavelet transform (BC-SMR-BD-GA-DWT), DQN-based optimization framework to secure shared blockchain systems (BC-SMR-BD-DQNSB), Hyper ledger blockchain enabled secure medical record management along deep learning-based diagnosis model (BC-SMR-BD-HBESDM-DLD), Secure attribute-based signature scheme with multiple authorities for blockchain in electronic health records system (BC-SMR-BD-MA-ABS) respectively.

Improved Variational Mode Decomposition and One-Dimensional CNN Network with Parametric Rectified Linear Unit (PReLU) Approach for Rolling Bearing Fault Diagnosis

As a critical component of rotating machinery, rolling bearings are essential for the safe and efficient operation of machinery. Sudden faults of rolling bearings can lead to unscheduled downtime and substantial economic costs. Therefore, diagnosing and identifying the equipment status is essential for ensuring the operation and decreasing the additional maintenance costs of the machines. However, extracting the features from the early bearing fault signals is challenging under background noise interference. With the purpose of solving the above problem, we propose an integrated rolling bearing fault diagnosis model based on the improved grey wolf optimized variational modal decomposition (IGVMD) and an improved 1DCNN with a parametric rectified linear unit (PReLU). Firstly, an improved grey wolf optimizer (IGWO) with the fitness function, the minimum envelope entropy, is designed for adaptively searching the optimal parameter values of the VMD model. The performance of the basic grey wolf optimizer (GWO) algorithm by introducing three improvement strategies, the non-linear convergence factor adjustment strategy, the grey wolf adaptive position update strategy, and the Levy flight strategy in the IGWO algorithm, is improved. Then, an improved 1DCNN model with the PReLU activation function is proposed, which extracts the bearing fault features, and a grid search to optimize the model parameters of the 1DCNN is introduced. Finally, the effectiveness of the proposed model is demonstrated well by employing two experimental datasets. The preliminary comparative results of the average identification accuracy in the proposed method in two datasets are 99.98% and 99.50%, respectively, suggesting that this proposed method has a relatively higher recognition accuracy and application values.

A novel fractional structural adaptive grey Chebyshev polynomial Bernoulli model and its application in forecasting renewable energy production of China

Accurate mid-to-long term China's renewable energy forecasting is becoming more and more important for integrating renewable energy systems with smart grid and energy strategic planning. For this purpose, this paper proposes a novel fractional structural adaptive grey Chebyshev polynomial Bernoulli model. The new model is based on NGBM(1,1) model to describe nonlinear phenomena. The proposed adjacent accumulation operator can balance old and new information accumulation with less fluctuation. The introduction of the Chebyshev polynomial enables the model to adaptively adjust the structure of the model, which reduces the difficulty of the modeler's operation from the point of view of the expert system. Meanwhile, an improved Grey Wolf optimization algorithm (IGWO) was used to optimize the parameters. Four real cases of renewable energy in China, including production and consumption are used for verification. In addition, Monte Carlo simulation and probability density analysis illustrate the robustness and accuracy of the proposed model. Finally, the development of the four cases in the next 5 years is predicted.

Multi-step ahead wind speed forecasting approach coupling maximal overlap discrete wavelet transform, improved grey wolf optimization algorithm and long short-term memory

Accurate and reliable wind speed forecasting is of great significance to the management and utilization of wind energy. An improved deep learning model for wind speed forecasting, abbreviated as MODWT-RF-IGWO-LSTM, is presented in this paper. Firstly, the maximum overlap discrete wavelet transform (MODWT) is applied to denoise the original wind speed series. Secondly, the random forest (RF) algorithm is used for feature selection. Thirdly, the improved grey wolf optimization algorithm (IGWO) is applied to optimize the parameters of the long short-term memory (LSTM) model. Finally, the denoised wind speed data is entered into the well-trained LSTM model to obtain the final wind speed forecasting result. The performance of the proposed model is assessed by actual wind speed data for three different months of the year. The experimental results show that the proposed deep learning model for wind speed forecasting has good predictive ability. And the proposed model performs better than other benchmark models in this paper.

Intelligent Power Allocation Algorithm for Energy-Efficient Mobile Internet of Things (IoT) Networks

With mobile communication technology development, the mobile Internet of Things (IoT) is booming, and the IoT applications are springing up all the time. However, the wireless channels are complex, and the security of mobile IoT networks is facing many challenges. Energy efficiency is critical for secure communications in mobile IoT networks. To reduce energy consumption, we propose a transmit antenna selection (TAS)-based secrecy scheme employing amplify-and-forward (AF) relaying. Firstly, we derive the exact expressions, and analyze the physical layer security performance. Then, to further improve energy efficiency, we formulate the power allocation problem, which is a non-convex complicated problem. To solve this problem, we propose a novel power allocation intelligent optimization algorithm. Based on the designed power allocation function, an improved grey wolf optimization (IGWO) algorithm is employed to obtain the allocation parameter. For convergence speed and convergence precision, the proposed IGWO algorithm obtains better optimization performance than other swarm intelligence algorithms. Compared with other algorithms, the running time of IGWO is reduced by 24%, while maintaining the same optimization accuracy. This greatly improves the energy efficiency of mobile IoT networks.

Grey wolf optimizer based on Aquila exploration method

The grey wolf optimizer(GWO) is an effective meta-heuristic algorithm. However, since the update of the search agent's position often depends on the alpha wolf, it is easy to fall into a local optimal solution. Therefore, this paper proposes an improved GWO algorithm to solve the global optimization. The improved algorithm is inspired by the Aquila Optimizer(AO), which enables some wolves to have the ability to fly, expand their search range to improve the global search ability and reduce the possibility of falling into the local optimum. At the same time, a new reduction strategy is proposed, which mainly emphasizes the exploitation ability of grey wolf and the exploration ability of Aquila, which is used to balance the two stages of exploration and exploitation. In order to verify the effectiveness of the algorithm, the algorithm was benchmarked on 23 well-known test functions and compared with other popular meta-heuristic algorithms. The results show that our proposed algorithm has good performance. Finally, it is applied to four practical engineering problems, and the results show that the algorithm is suitable for challenging problems with unknown search space. Matlab codes of AO are available at https://ww2.mathworks.cn/matlabcentral/fileexchange/110410-grey-wolf-optimizer-based-on-aquila-exploration-method.

Improved Grey Wolf Optimization Algorithm and Application.

This paper proposed an improved Grey Wolf Optimizer (GWO) to resolve the problem of instability and convergence accuracy when GWO is used as a meta-heuristic algorithm with strong optimal search capability in the path planning for mobile robots. We improved chaotic tent mapping to initialize the wolves to enhance the global search ability and used a nonlinear convergence factor based on the Gaussian distribution change curve to balance the global and local searchability. In addition, an improved dynamic proportional weighting strategy is proposed that can update the positions of grey wolves so that the convergence of this algorithm can be accelerated. The proposed improved GWO algorithm results are compared with the other eight algorithms through several benchmark function test experiments and path planning experiments. The experimental results show that the improved GWO has higher accuracy and faster convergence speed.

A two-stage framework for demand-side management and energy savings of various buildings in multi smart grid using robust optimization algorithms

Advances in technology and population growth are two factors responsible for increasing electricity consumption, which directly increases the production of electrical energy. Also, due to environmental, technical and economic constraints, it is challenging to meet the demand at certain hours, such as peak hours. Therefore, it is necessary to manage the network consumption to modify the peak load and complete the power system constraints. One way to achieve this goal is to use a demand response program. The best infrastructure for running these types of programs is the smart grid (SG). This paper first considers many shiftable loads of different kinds in the SG, including residential, commercial, and industrial microgrids. The load shift method is, then, modeled as a multi-objective optimization problem to manage the shiftable load consumption of the SG. The main goals of this problem include reducing the cost of customer bills, the peak load, losses and improving network voltage. Simulations are performed by two methods of Simplex and Improved Grey Wolf Optimization (IGWO). The Simplex method is implemented with the CPLEX solver in General Algebraic Modelling System (GAMS) software and the IGWO algorithm is implemented in MATLAB software. Then, the effect of implementing the proposed program with both methods is examined and compared. For example, in terms of reducing the peak load, the CPLEX method could reduce the peak in the residential, commercial, and industrial microgrids by 6.7%, 1%, and 16% more than IGWO, respectively. CPLEX also can reduce the production costs of residential, commercial, and industrial microgrids by more than 3.5, 2.2, and 3.9% compared to IGWO. In general, the CPLEX solver provides better results than the IGWO in many cases.

A Hybrid Multi-Target Path Planning Algorithm for Unmanned Cruise Ship in an Unknown Obstacle Environment.

To solve the problem of traversal multi-target path planning for an unmanned cruise ship in an unknown obstacle environment of lakes, this study proposed a hybrid multi-target path planning algorithm. The proposed algorithm can be divided into two parts. First, the multi-target path planning problem was transformed into a traveling salesman problem, and an improved Grey Wolf Optimization (GWO) algorithm was used to calculate the multi-target cruise sequence. The improved GWO algorithm optimized the convergence factor by introducing the Beta function, which can improve the convergence speed of the traditional GWO algorithm. Second, based on the planned target sequence, an improved D* Lite algorithm was used to implement the path planning between every two target points in an unknown obstacle environment. The heuristic function in the D* Lite algorithm was improved to reduce the number of expanded nodes, so the search speed was improved, and the planning path was smoothed. The proposed algorithm was verified by experiments and compared with the other four algorithms in both ordinary and complex environments. The experimental results demonstrated the strong applicability and high effectiveness of the proposed method.

A hybrid clustering algorithm based on improved GWO and KHM clustering

To solve the problem that the K-means algorithm is sensitive to the initial clustering centers and easily falls into local optima, we propose a new hybrid clustering algorithm called the IGWOKHM algorithm. In this paper, we first propose an improved strategy based on a nonlinear convergence factor, an inertial step size, and a dynamic weight to improve the search ability of the traditional grey wolf optimization (GWO) algorithm. Then, the improved GWO (IGWO) algorithm and the K-harmonic means (KHM) algorithm are fused to solve the clustering problem. This fusion clustering algorithm is called IGWOKHM, and it combines the global search ability of IGWO with the local fast optimization ability of KHM to both solve the problem of the K-means algorithm’s sensitivity to the initial clustering centers and address the shortcomings of KHM. The experimental results on 8 test functions and 4 University of California Irvine (UCI) datasets show that the IGWO algorithm greatly improves the efficiency of the model while ensuring the stability of the algorithm. The fusion clustering algorithm can effectively overcome the inadequacies of the K-means algorithm and has a good global optimization ability.

Research on heat and power load optimal dispatch based on improved grey wolf optimization algorithm

The heat and power load optimal dispatch (LOD) is a useful method for combined heat and power (CHP) plants to improve energy efficiency and reduce energy consumption. To obtain a better load dispatch effect, this paper proposes the improved grey wolf optimization (IGWO) algorithm and LOD problem constraint processing methods, which constitute three kinds of optimization schemes. The load optimal dispatch research is performed for a typical CHP plant consisting of seven units and different optimization schemes are discussed. The results show that the power generation cost obtained from the proposed optimization scheme is lower than the data obtained by a single algorithm in the literature. The results also indicate that the adopted constraint processing method can well solve the characteristics of multiple constraints of the LOD problem. Meanwhile, the comparison results of different schemes show that the IGWO algorithm has more obvious advantages in the performance of better convergence and robustness, meaning it is more suitable for solving the LOD problems. In short, the improved algorithm can accurately solve the load matching problem between power plants and power and heat grids, providing better solutions.

Hybrid Strategy Improved Grey Wolf Optimization Algorithm for Plume Tracking and Localization Method in Indoor Weak Wind Environment

Reliable tracking of the plume by the robot is the key to achieving plume source localization. To address the problem of low success rate and long search time of robot source location due to the unavailability of reliable information on gas diffusion flow direction and flow velocity in an indoor weak wind environment, a hybrid strategy is proposed to improve the grey wolf optimization algorithm for robot plume tracking and location. The plume is modeled using Computational Fluid Dynamics (CFD) in a two-dimensional indoor weak wind environment, and the plume concentration value is used as the individual adaptation degree of the algorithm. Without carrying plume velocity and flow direction sensors, the source-finding robot simulates the grey wolf population’s social mechanism and hunting behavior to update its position. The improved Grey Wolf Optimization algorithm is compared with the traditional Grey Wolf Optimization (GWO), Particle Swarm Algorithm (PSO), and Genetic Algorithm (GA) in simulation experiments. The simulation experiments show that the average number of iterations of the improved GWO is 7, 108, and 118 times shorter than the four source finding algorithms of GWO, PSO, and GA. The average planning path is reduced by 0.91 meters, 2.35meters, and 2.90 meters. 3s, 10.3s, and 9.3s reduce the average running time. The average positioning success rate is improved by 30%, 32%, and 40%. The applicability and Stability of the improved GWO algorithm in solving the plume tracking and localization problem in an indoor weak wind environment are verified.

Short-Term Traffic Flow Prediction Method Based on WT-IGWO-ELM

Accurate prediction of short-term traffic flow provides crucial data support for the stable operation of intelligent transportation systems. For this issue, this paper proposes a short-term traffic flow prediction method based on WT-IGWO-ELM. The algorithm uses the Wavelet Transform (WT) method to denoise the traffic flow data in advance, which improves the data quality of the dataset. Then, the IGWO algorithm, which integrates the initial population based on Sine chaotic map and reverse learning strategy, the adjustment of nonlinear convergence factor and the introduction of dynamic weights, is used to avoid local optimality more effectively, speed up the convergence speed, and improve the solution accuracy. Finally, the improved grey wolf optimizer (IGWO) was used to update the optimal parameters of the ELM prediction model, and the average relative error of the prediction of the WT-IGWO-ELM model was verified by comparison experiments compared with those of ELM and WT. -ELM, GWO-ELM, WT-GWO-ELM and IGWO-ELM decreased by 96.6625%, 95.5972%, 87.9447%, 79.5021%, 72.0571%, respectively, and its prediction effect was much better than ELM, WT-ELM, GWO-ELM, WT-GWO-ELM and IGWO-ELM methods have high prediction performance and accuracy in short-term traffic flow prediction.