Improved Grey Wolf Optimization Algorithm Research Articles

Coordinated optimization of production scheduling and maintenance activities with machine reliability deterioration

<p style='text-indent:20px;'>In this paper, we investigate a coordinated optimization problem of production and maintenance where the machine reliability decreases with the use of the machine. Lower reliability means the machine is more likely to fail during the production stage. In the event of a machine failure, corrective maintenance (CM) of the machine is required, and the CM of the machine will cause a certain cost. Preventive maintenance (PM) can improve machine reliability and reduce machine failures during the production stage, but it will also cause a certain cost. To minimize the total maintenance cost, we must determine an appropriate PM plan to balance these two types of maintenance. In addition, the tardiness cost of jobs is also considered, which is affected not only by the processing sequence of jobs but also by the PM decision. The objective is to find the optimal job processing sequence and the optimal PM plan to minimize the total expected cost. To solve the proposed problem, an improved grey wolf optimizer (IGWO) algorithm is proposed. Experimental results show that the IGWO algorithm outperforms GA, VNS, TS, and standard GWO in optimization and computational stability.</p>

An Improved GWO Algorithm Optimized RVFL Model for Oil Layer Prediction

In this study, a model based on the improved grey wolf optimizer (GWO) for optimizing RVFL is proposed to enable the problem of poor accuracy of Oil layer prediction due to the randomness of the parameters present in the random vector function link (RVFL) model to be addressed. Firstly, GWO is improved based on the advantages of chaos theory and the marine predator algorithm (MPA) to overcome the problem of low convergence accuracy in the optimization process of the GWO optimization algorithm. The improved GWO algorithm was then used to optimize the input weights and implicit layer biases of the RVFL network model so that the problem of inaccurate and unstable classification of RVFL due to the randomness of the parameters was avoided. MPA-GWO was used for comparison with algorithms of the same type under a function of 15 standard tests. From the results, it was concluded that it outperformed the algorithms of its type in terms of search accuracy and search speed. At the same time, the MPA-GWO-RVFL model was applied to the field of Oil layer prediction. From the comparison tests, it is concluded that the prediction accuracy of the MPA-GWO-RVFL model is on average 2.9%, 3.04%, 2.27%, 8.74%, 1.47% and 10.41% better than that of the MPA-RVFL, GWO-RVFL, PSO-RVFL, WOA-RVFL, GWFOA-RVFL and RVFL algorithms, respectively, and its practical applications are significant.

Dynamic Economic Dispatch of Power System Network Having Thermal Units and Electric Vehicles using IGWO

Dynamic economic dispatch (DED) is a complex power system problem. The problem becomes more complex with inclusion of  electric vehicles. In the presented paper,  Improved Grey Wolf Optimizer (IGWO) is proposed to solve this complex problem. IGWO is having a better balance between exploitation and exploration for the complex problem such as  Dynamic Economic Dispatch (DED) taking into account of valve- point effect, transmission losses and ramp-rate limits with and without electric vehicles (EVs). The efficiency of the algorithm is demonstrated on solving different DED problems for 5 generator and 15 generator test systems with and without losses along with different charging profile distribution of electric vehicles.  The results showcased by IGWO is compared with the other algorithm. The results obtained by IGWO algorithm  using repair method adopted in solving dynamic economic dispatch problem is giving competitive results as compared to the results given by other algorithm present in literature.

An improved grey wolf optimizer for welding shop inverse scheduling

The welding is widely used in many manufacturing industries. In real-life welding production shop, because of a variety of dynamic events, the original schedule maybe not optimal or even unfeasible. However, if the scheduling scheme is rescheduled blindly, the stability of production and the overall production efficiency will be greatly impacted. To deal with this problem, this paper proposes the model and algorithm of the welding shop inverse scheduling problem (WSISP) for the dynamic events according to welding shop characteristics. Minimizing the parameter adjustment is regarded as the objective. And an improved grey wolf optimizer (IGWO) algorithm is proposed to solve this problem. First, for typical discrete optimization problems, the encoding based on a matrix and the initialization method by the critical path are designed. These methods can avoid unnecessary search and improve search efficiency. Second, a variable neighborhood search method is used to enrich the local search ability of the IGWO algorithm. Afterwards, with the objective of minimizing the parameter adjustment, three kinds of instances are selected to verify the performance of the proposed algorithm, and the comparisons with the state-of-art methods are also conducted. The experimental results show the superiority of the proposed method for solving WSISIP. Finally, a case study considering a real-life welding shop is conducted to validate the proposed method.

Power Generation Monitoring of a hybrid Power System with I-GWO designed trapezoidal type-II fuzzy Controller

ABSTRACT This article has implemented a robust type-II fuzzy PID controller for automatic generation control (AGC) of a diverse energy source-based multi-area power system under various operating conditions. The AGC aims to make stability over frequency and tie-line power of the power system. The proposed controller is accessed with Type-II Fuzzy systems with trapezoidal-based membership functions. In controller design concern, a novel soft computing algorithm called an improved-grey wolf optimizer (I-GWO) is approached for this study. The performance of the proposed trapezoidal membership function-based type-II fuzzy controller (TM-T2FC) has been extensively compared with the existing triangular membership-function based fuzzy-II controller (TrM-T2FC), type-I fuzzy controller (T1FC) and PID controller for AGC of the multi-area power system. Furthermore, the viability and effectiveness of the anticipated I-GWO algorithm have been separately assessed through suitable comparative studies. The robustness of the proposed TM-T2FC controller is investigated through different sensitive analyses and further assessment has been carried out by extending the proposed controller to a five area power system. It is observed from extensive studies and performance assessment that the proposed TM-T2FC controller can be a potential candidate for the AGC of the multi-area power system.

Illumination correction model with sine‐cosine algorithm to optimize gray wolf population for least‐squares support vector regression

AbstractThe color of an object appears different from its true color when illuminated with light sources of different hues. To solve this problem, this article proposes a combination algorithm (SCA‐GWO‐LSSVR) based on the sine‐cosine algorithm (SCA) and the gray wolf optimization (GWO) algorithm to optimize the regression prediction model of the least‐squares support vector regression (LSSVR) algorithm. The performance of the traditional LSSVR is significantly affected by the penalty parameter (gamma) and the sig2 kernel function parameter. The proposed method uses the improved GWO algorithm to search the population to find the best LSSVR parameter solution. The proposed algorithm uses the SCA to create multiple random candidate solutions in population initialization to avoid blind initialization of the GWO algorithm. In the process of iterative optimization, the SCA is infiltrated, and its sine‐cosine wave mathematical model is used to quickly identify the best outward or inward position of the gray wolf. Finally, the LSSVR combines the optimal sig2 kernel function parameters and penalty parameters (gamma) to obtain a highly versatile illumination correction model. The experimental results show that the fitting accuracy of the proposed method reaches 86.8%, which is 5% higher than that of the LSSVR algorithm alone.

Dam Deformation Prediction Based on Modified Grey Wolf Optimization Algorithm and Support Vector Machine

The establishing of an accurate and reliable dam deformation prediction model is an important content of dam safety evaluation. To this end, a dam deformation prediction method based on support vector machine (SVM) with optimal parameters selected by improved grey wolf optimization algorithm (IGWO) is proposed by introducing the crossover and mutation operators of the differential evolution algorithm into the GWO algorithm. The initial population is enriched by the differential evolution algorithm, an IGWO is proposed, and the penalty factor and kernel function of SVM are optimized by the IGWO algorithm, then a dam deformation prediction model based on the SVM-IGWO algorithm is established. The results will be compared with those of the SVM and SVM-GWO models through the measured data of Xiluodu super high arch dam. The research shows that the proposed SVM-IGWO model can effectively improve the accuracy of dam deformation prediction.

Optimization of heliostat field distribution based on improved Gray Wolf optimization algorithm

The heliostat field of tower solar thermal power station accounts for 40%–50% of the total cost, and influences the concentrating efficiency. Accordingly, it is necessary to optimize the layout of the heliostat field. Based on the optical efficiency model, an improved Gray Wolf Optimization (GWO) algorithm is proposed to optimize the field parameters of the heliostats, improve the convergence factor and weight updating formula, and effectively avoid the local optimal problem. Then SolarPILOT software is used to simulate the heliostat field distribution. In order to reduce the shadow and shielding efficiency loss, improve the land utilization rate and atmospheric attenuation efficiency, the heliostat field is initialized by radial staggered arrangement, which is easy to be optimized. By using the optical efficiency model, the program of heliostat field optimization algorithm is developed, and a Delingha tower power station is used to verify the algorithm. After the improved GWO algorithm optimizing the heliostat field, the optical or concentrating efficiency of the heliostat field is increased by 8.2% compared with the GWO algorithm. The improved GWO algorithm reduces the heliostat number by 3.4% compared with the Gray Wolf algorithm, and that reducing the cost of the heliostat field.

Parameter Identification of DC‐Link Capacitor for Electric Vehicle Based on IGWO‐BP Neural Network

DC‐link capacitor is one of the most vulnerable passive components in the drive system of electric vehicle, so the condition monitoring of DC‐link capacitors can significantly improve the reliability of drive system and even driving safety. The existing monitoring methods are subject to the low accuracy, added hardware and the irreversible impact on system. Therefore, based on Back Propagation (BP) neural network with Improved Gray Wolf Optimization (IGWO), a parameter identification method for the DC‐link capacitor in electric vehicle inverter is proposed. In this method, the capacitance (C) is taken as health parameter. The A‐phase current and DC‐link capacitor voltage in electric drive system are taken as inputs, and the capacitance (C) is taken as output for condition monitoring. IGWO algorithm can be applied to obtain the optimal weights and thresholds. Ultimately, under four actual working conditions in electric drive system, the condition monitoring test is carried out. The results are compared and analyzed, which show that the monitoring using IGWO‐BP neural network has better performance. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Precise locomotion controller design for a novel magnetorheological fluid robot based on improved gray wolf optimization algorithm

Spherical magnetorheological fluid (MRF) robots are capable to move in narrow space, which can be used for drug releasing to human stomach; however, the magnetic-controlled rolling movement often generates a large displacement error, which greatly hinders the practical applications of the MRF robots. In order to bridge this research gap, this paper introduces a new MRF robot with a precise locomotion controller. In this control system, a data acquisition system is designed for the MRF robot and an optimal proportion integration differentiation (PID) controller is proposed based on an improved grey wolf optimization algorithm (IGWO). Both simulations and experiments have been performed to verify the performance of the locomotion controller. The simulation results show that the proposed IGWO-PID controller is superior to the conventional PID and GWO PID controller, with faster response output and smaller overshoot. Experimental analysis results demonstrate the proposed MRF robot can move in a complex trace with a speed fluctuation rate below 5.4%. As a result, precise locomotion has been achieved to make the new MRF robot ready for medicine delivery in narrow space.

Intelligent Outage Probability Prediction for Mobile IoT Networks Based on an IGWO-Elman Neural Network

Advances in sensor technology have accelerated the development of mobile internet applications and contributed to the tremendous growth of the Internet of Things (IoT). Mobile IoT networks typically operate in complex and highly variable urban wireless channel environments. This makes effective and reliable communications challenging. To ensure efficient and stable communications, mobile IoT networks must adapt to the complex environment. This can be achieved by predicting the outage probability (OP). This paper investigates OP analysis and prediction for these networks. Exact closed-form OP expressions are derived and Monte-Carlo simulation is used to verify the analysis and evaluate the OP performance. Then, an OP prediction approach based on an improved grey wolf optimization (IGWO) algorithm and Elman neural network (IGWO-Elman) is proposed. The IGWO algorithm employs improved opposition-based learning to optimize the population initialization and ensure sufficient population diversity. The Elman neural network uses the IGWO algorithm to obtain good network parameters. Simulation results are presented which demonstrate that the prediction accuracy of IGWO-Elman is better than SVM, ELM, and BP algorithms. In terms of prediction accuracy, the IGWO-Elman algorithm is increased by 44%.

An Improved Grey Wolf Optimization Algorithm and its Application in Path Planning

Grey wolf algorithm (GWO) is a classic swarm intelligence algorithm, but it has the disadvantages of slow convergence speed and easy to fall into local optimum on some problems. Therefore, an improved grey wolf optimization algorithm(IGWO) is proposed. The lion optimizer algorithm and dynamic weights are integrated into the original grey wolf optimization algorithm. When the positions of $\alpha $ wolf, $\beta $ wolf, and $\delta $ wolf are updated, the lion optimizer algorithm is used to add disturbance factors to the wolves to give $\alpha $ wolf, $\beta $ wolf, and $\delta $ wolf active search capabilities. Dynamic weights are added to the grey wolf position update to prevent wolves from losing diversity and falling into local optimum. Through multiple benchmark function test experiments and path planning experiments, the experimental results show that the improved grey wolf optimization algorithm can effectively improve the accuracy and convergence speed, and the optimization effect is better.

Economic dispatching Optimization of power grid based on IGWO Algorithm

An improved grey Wolf optimization algorithm (IGWO) is proposed to solve economic dispatch (ED) of power systems is proposed in this paper. The column vector composed of the active power of each generator in the power grid system is taken as the decision variable, and the total power generation cost of the power grid is taken as the objective function. The improved grey Wolf optimization algorithm is used to search for the optimal value by introducing the inertial characteristic constant and Gaussian mutation operator, and is compared with particle swarm optimization (PSO). The simulation results show that the proposed method has the characteristics of fast convergence speed, high solution accuracy and easy to jump out of the local optimal solution, and can be used as an effective means to solve ED problems.

Sea Clutter Suppression Method of HFSWR Based on RBF Neural Network Model Optimized by Improved GWO Algorithm.

The detection performance of high-frequency surface-wave radar (HFSWR) is closely related to the suppression effect of sea clutter. To effectively suppress sea clutter, a sea clutter suppression method based on radial basis function neural network (RBFNN) optimized by improved gray wolf optimization (IGWO) algorithm is proposed. Firstly, according to shortcomings of the standard gray wolf optimization (GWO) algorithm, such as slow convergence speed and easily getting into local optimum, an adaptive division of labor search strategy is proposed, which makes the population have abilities of both large-scale search and local exploration in the entire optimization process. Then, the IGWO algorithm is used to optimize RBFNN, finally, establishing a sea clutter prediction model (IGWO-RBFNN) and realizing the prediction and suppression of sea clutter. Experiments show that the IGWO algorithm has significantly improved convergence speed and optimization accuracy. Compared with the particle swarm algorithm with linear decreasing weight strategy (LDWPSO) and the GWO algorithm, the RBFNN prediction model optimized by the IGWO algorithm has higher prediction accuracy and has a better suppression effect on sea clutter of HFSWR.

An Improved Grey-Wolf Optimization Algorithm Based on Circle Map

To overcome the shortcomings of grey wolf optimization algorithm (GWO) such as being easy to fall into the local optimum, and the slow convergence rate in the later stage, an adaptive weighted grey-wolf optimization algorithm based on the Circle map is proposed. Firstly, in this algorithm, Circle chaotic map, which enhances the diversity of the initialization population, is introduced into the initialization of population, therefore, the search space can be searched more thoroughly; Secondly, trigonometric function and the beta distribution are introduced in the convergence factor ‘a’ and the population position update formula, which improve the convergence speed in the later period of the algorithm; Finally, the simulation experiments on the four common test functions on CEC2017 show that under the same experimental conditions, the improved grey wolf optimization algorithm improves the solution accuracy and convergence speed significantly, and its performance is obviously better than other smart optimization algorithms and other improved GWO algorithms.

An improved grey wolf optimizer for solving engineering problems

In this article, an Improved Grey Wolf Optimizer (I-GWO) is proposed for solving global optimization and engineering design problems. This improvement is proposed to alleviate the lack of population diversity, the imbalance between the exploitation and exploration, and premature convergence of the GWO algorithm. The I-GWO algorithm benefits from a new movement strategy named dimension learning-based hunting (DLH) search strategy inherited from the individual hunting behavior of wolves in nature. DLH uses a different approach to construct a neighborhood for each wolf in which the neighboring information can be shared between wolves. This dimension learning used in the DLH search strategy enhances the balance between local and global search and maintains diversity. The performance of the proposed I-GWO algorithm is evaluated on the CEC 2018 benchmark suite and four engineering problems. In all experiments, I-GWO is compared with six other state-of-the-art metaheuristics. The results are also analyzed by Friedman and MAE statistical tests. The experimental results and statistical tests demonstrate that the I-GWO algorithm is very competitive and often superior compared to the algorithms used in the experiments. The results of the proposed algorithm on the engineering design problems demonstrate its efficiency and applicability.

An Improved Grey Wolf Optimizer Based on Differential Evolution and OTSU Algorithm

Aimed at solving the problems of poor stability and easily falling into the local optimal solution in the grey wolf optimizer (GWO) algorithm, an improved GWO algorithm based on the differential evolution (DE) algorithm and the OTSU algorithm is proposed (DE-OTSU-GWO). The multithreshold OTSU, Tsallis entropy, and DE algorithm are combined with the GWO algorithm. The multithreshold OTSU algorithm is used to calculate the fitness of the initial population. The population is updated using the GWO algorithm and the DE algorithm through the Tsallis entropy algorithm for crossover steps. Multithreshold OTSU calculates the fitness in the initial population and makes the initial stage basically stable. Tsallis entropy calculates the fitness quickly. The DE algorithm can solve the local optimal solution of GWO. The performance of the DE-OTSU-GWO algorithm was tested using a CEC2005 benchmark function (23 test functions). Compared with existing particle swarm optimizer (PSO) and GWO algorithms, the experimental results showed that the DE-OTSU-GWO algorithm is more stable and accurate in solving functions. In addition, compared with other algorithms, a convergence behavior analysis proved the high quality of the DE-OTSU-GWO algorithm. In the results of classical agricultural image recognition problems, compared with GWO, PSO, DE-GWO, and 2D-OTSU-FA, the DE-OTSU-GWO algorithm had accuracy in straw image recognition and is applicable to practical problems. The OTSU algorithm improves the accuracy of the overall algorithm while increasing the running time. After adding the DE algorithm, the time complexity will increase, but the solution time can be shortened. Compared with GWO, DE-GWO, PSO, and 2D-OTSU-FA, the DE-OTSU-GWO algorithm has better results in segmentation assessment.

Green scheduling optimization of ship plane block flow line considering carbon emission and noise

Recently, with the strategic requirements of sustainable development, more and more attention has been paid on the green scheduling problems. Different from the traditional flow-line scheduling problem, the ship plane block possesses the characteristics of larger volume and weight, so the problem of ship plane block flow-line can be regarded as a blocking flow-line scheduling problem (BFSP). In this paper, considering the complexity of ship building and the particularity of green scheduling problem (e.g., carbon emission and noise of equipment), the green blocking flow-line scheduling problem model of panel block (GBFSP) in shipbuilding was established to minimize the maximum completion time, carbon emission cost and noise cost. Moreover, the improved gray wolf optimization (IGWO) algorithm was proposed to solve this problem effectively. In the proposed IGWO algorithm, a ranked order value (ROV) method was performed to realize the transformation from continuous gray wolf individual position to discrete optimal solution. Secondly, a nonlinear convergence factor and PSO algorithm were introduced to balance the development and exploration ability of the IGWO algorithm. In addition, variable neighborhood search (VNS) was also used to improve the accuracy and effectiveness of local search. Furthermore, the validity of the proposed IGWO algorithm is verified by some famous benchmark examples. In addition, a real data of a shipyard was used for example verification and multiple numerical experiments. Our results suggested that the model and the IGWO algorithm can solve the problems existing in the ship plane block flow-line effectively. Meanwhile, reducing the impact of pipeline on the environment, and achieving the goal of green production.

An improved extreme gradient boosting approach to vehicle speed prediction for construction simulation of earthwork

Construction simulation is an effective tool to provide schedule plans. Vehicle speed is one of the most significant factors in earthwork construction simulation. However, neglecting the strong correlation with contextual factors, random distribution methods will lead to inaccurate prediction of vehicle speed. To address such issues, an improved extreme gradient boosting (XGBoost) approach to vehicle speed prediction is proposed for earthwork construction simulation. Firstly, to improve the global searching ability, an improved grey wolf optimization algorithm (IGWO) is put forward. Secondly, XGBoost is optimized by IGWO to construct an IGWO-XGBoost model. Then, the prediction model is embedded in the earthwork construction simulation model. The case study proves that the simulation results of the proposed method are more consistent with an actual construction schedule. It is expected that the vehicle speed prediction embedded into a simulation program facilitated an accurate development of schedule plan, thereby improving the efficiency of construction management.

Task assignment strategy for multi-robot based on improved Grey Wolf Optimizer

Multi-robot task allocation (MRTA) is the basis of a multi-robot system to perform tasks automatically, which directly affects the execution efficiency of the whole system. A distributed cooperative task allocation strategy based on the algorithm of the improved Grey Wolf Optimizer (IGWO) was proposed to quickly and effectively plan the cooperative task path with a large number of working task points. The MRTA problem was transformed into multiple traveling salesman problems (MTSPs), and the task target points were clustered by the K-means clustering algorithm and divided into several traveling salesman problems (TSPs). The Grey Wolf Optimizer (GWO) was improved by the Kent chaotic algorithm to initialize the population and enhance the diversity of the population. Furthermore, an adaptive adjustment strategy of the control parameter $$\vec {a}$$ was proposed to balance exploration and exploitation. The individual speed and position updates in PSO were introduced to enable the gray wolf individual to preserve its optimal location information and accelerate the convergence speed. The IGWO was used to solve the optimal solutions to multiple TSP problems. Finally, the optimal solution space was integrated to get the optimal solution of MTSP, and 16 international classical test functions simulated the IGWO. The results showed that the IGWO algorithm has faster convergence speed and higher accuracy. The task allocation strategy is reasonable, with roughly equal path length, small planning cost, fast convergence speed, and excellent stability.