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1
Liu, Guangwei, Zhiqing Guo, Wei Liu, Feng Jiang, and Ensan Fu. "A feature selection method based on the Golden Jackal-Grey Wolf Hybrid Optimization Algorithm." PLOS ONE 19, no. 1 (January 2, 2024): e0295579. http://dx.doi.org/10.1371/journal.pone.0295579.
Повний текст джерелаАнотація:
This paper proposes a feature selection method based on a hybrid optimization algorithm that combines the Golden Jackal Optimization (GJO) and Grey Wolf Optimizer (GWO). The primary objective of this method is to create an effective data dimensionality reduction technique for eliminating redundant, irrelevant, and noisy features within high-dimensional datasets. Drawing inspiration from the Chinese idiom “Chai Lang Hu Bao,” hybrid algorithm mechanisms, and cooperative behaviors observed in natural animal populations, we amalgamate the GWO algorithm, the Lagrange interpolation method, and the GJO algorithm to propose the multi-strategy fusion GJO-GWO algorithm. In Case 1, the GJO-GWO algorithm addressed eight complex benchmark functions. In Case 2, GJO-GWO was utilized to tackle ten feature selection problems. Experimental results consistently demonstrate that under identical experimental conditions, whether solving complex benchmark functions or addressing feature selection problems, GJO-GWO exhibits smaller means, lower standard deviations, higher classification accuracy, and reduced execution times. These findings affirm the superior optimization performance, classification accuracy, and stability of the GJO-GWO algorithm.
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Liu, Yuanyuan, Jiahui Sun, Haiye Yu, Yueyong Wang, and Xiaokang Zhou. "An Improved Grey Wolf Optimizer Based on Differential Evolution and OTSU Algorithm." Applied Sciences 10, no. 18 (September 11, 2020): 6343. http://dx.doi.org/10.3390/app10186343.
Повний текст джерелаАнотація:
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.
3
Liu, Haiqiang, Gang Hua, Hongsheng Yin, and Yonggang Xu. "An Intelligent Grey Wolf Optimizer Algorithm for Distributed Compressed Sensing." Computational Intelligence and Neuroscience 2018 (2018): 1–10. http://dx.doi.org/10.1155/2018/1723191.
Повний текст джерелаАнотація:
Distributed Compressed Sensing (DCS) is an important research area of compressed sensing (CS). This paper aims at solving the Distributed Compressed Sensing (DCS) problem based on mixed support model. In solving this problem, the previous proposed greedy pursuit algorithms easily fall into suboptimal solutions. In this paper, an intelligent grey wolf optimizer (GWO) algorithm called DCS-GWO is proposed by combining GWO and q-thresholding algorithm. In DCS-GWO, the grey wolves’ positions are initialized by using the q-thresholding algorithm and updated by using the idea of GWO. Inheriting the global search ability of GWO, DCS-GWO is efficient in finding global optimum solution. The simulation results illustrate that DCS-GWO has better recovery performance than previous greedy pursuit algorithms at the expense of computational complexity.
4
Zhao, Jiankun, Wei Liu, Qingxian Zhang, Hexi Wu, Liangquan Ge, Tianbin Li, Yi Gu, Qi Zeng, and Yibao Liu. "Rapid localization of radioactive leaks based on hybrid adaptive grey wolf algorithm." Journal of Instrumentation 17, no. 08 (August 1, 2022): P08034. http://dx.doi.org/10.1088/1748-0221/17/08/p08034.
Повний текст джерелаАнотація:
Abstract Radioactive source localization algorithms have been widely used in the detection of nuclear accident areas. But some shortcomings, such as complex algorithm structure, slow localization speed and poor accuracy, were obviously performed to affect mobile robot locating autonomously. In this paper, a potential alternative method was investigated to be a new usage of locating leaks, just via specifying the change of exposure rate. In this model, several key factors, such as gamma ray attenuation, scattering factor, travel angle guide, spatial discretization, etc., were taken into consideration, to demonstrate the effectiveness of the algorithm, which is appropriated in unknown areas of the radioactive waste repository. Since there are three factors with different contribution, such as position, quantity of the source and gamma ray energy, which considered to demonstrate its impact on success. So, a hybrid adaptive grey wolf algorithm (HAGWO) has been adopted and implemented to develop a novel rapid method of radioactive leak location. Three aspects, including the good point set initialization in population size, balanced convergence function, and self-adaptive greedy strategy for population update, were optimized and merged into the locating model. To investigate the effectiveness of the algorithm, results of HAGWO are compared with grey wolf algorithm (GWO), good point set initialization strategy GWO(GGWO) and adaptive head wolf strategy GWO (ALGWO) in convergence speed, accuracy, stability and positioning error of single and double leak points. It is observed that convergence speed is increased by 37.93 ± 2% at the highest; the convergence accuracy is increased by 92.42 ± 2% at the most; the stability is improved by 30% ∼ 50%. The positioning error of single leak point is within 1.08%, and the positioning error of double leak point is less than 8.90%. Besides, compared with GWO, GGWO and ALGWO, the single-point accuracy is improved by 1.36 percentage points (to GWO), and the double-point accuracy is improved by 40.35 percentage points (to ALGWO) at most. It is observed that HAGWO performs the best in locating leaks, with a faster convergence, stronger stability and more accuracy.
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Zheng, Yukun, Ruyue Sun, Yixiang Liu, Yanhong Wang, Rui Song, and Yibin Li. "A Hybridization Grey Wolf Optimizer to Identify Parameters of Helical Hydraulic Rotary Actuator." Actuators 12, no. 6 (May 25, 2023): 220. http://dx.doi.org/10.3390/act12060220.
Повний текст джерелаАнотація:
Based on the grey wolf optimizer (GWO) and differential evolution (DE), a hybridization algorithm (H-GWO) is proposed to avoid the local optimum, improve the diversity of the population, and compromise the exploration and exploitation appropriately. The mutation and crossover principles of the DE algorithm are introduced into the GWO algorithm, and the opposition-based optimization learning technology is combined to update the GWO population to increase the population diversity. The algorithm is then benchmarked against nine typical test functions and compared with other state-of-the-art meta-heuristic algorithms such as particle swarm optimization (PSO), GWO, and DE. The results show that the proposed H-GWO algorithm can provide very competitive results. On this basis, the forgetting factor recursive least squares (FFRLS) method and the proposed H-GWO algorithm are combined to establish a parameter identification algorithm to identify parameters of the helical hydraulic rotary actuator (HHRA) with nonlinearity and uncertainty questions. In addition, the proposed method is verified by practical identification experiments. After comparison with the least squares (LS), recursive least squares (RLS), FFRLS, PSO, and GWO results, it can be concluded that the proposed method (H-GWO) has higher identification accuracy.
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Shi-fan, Qiao, Tan Jun-kun, Zhang Yong-gang, Wan Li-jun, Zhang Ming-fei, Tang Jun, and He Qing. "Settlement Prediction of Foundation Pit Excavation Based on the GWO-ELM Model considering Different States of Influence." Advances in Civil Engineering 2021 (January 27, 2021): 1–11. http://dx.doi.org/10.1155/2021/8896210.
Повний текст джерелаАнотація:
This paper proposes a novel grey wolf optimization-extreme learning machine model, namely, the GWO-ELM model, to train and predict the ground subsidence by combining the extreme learning machine with the grey wolf optimization algorithm. Taking an excavation project of a foundation pit of Kunming in China as an example, after analyzing the settlement monitoring data of cross sections JC55 and JC56, the representative monitoring sites JC55-2 and JC56-1 were selected as the training monitoring samples of the GWO-ELM model. And three kinds of GWO-ELM models such as considering the influence of time series, influence of settlement factors, and after optimization were established to predict the ground settlement near the foundation pit. The predictive results are that their average relative error and average absolute error are ranked from large to small as GWO-ELM model based on time series, GWO-ELM model based on settlement factors, and optimized GWO-ELM model for the three kinds of GWO-ELM models at monitoring points JC55-2 and JC56-1. Accordingly, the optimized GWO-ELM model has the strongest predictive ability.
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Kohli, Mehak, and Sankalap Arora. "Chaotic grey wolf optimization algorithm for constrained optimization problems." Journal of Computational Design and Engineering 5, no. 4 (March 7, 2017): 458–72. http://dx.doi.org/10.1016/j.jcde.2017.02.005.
Повний текст джерелаАнотація:
Abstract The Grey Wolf Optimizer (GWO) algorithm is a novel meta-heuristic, inspired from the social hunting behavior of grey wolves. This paper introduces the chaos theory into the GWO algorithm with the aim of accelerating its global convergence speed. Firstly, detailed studies are carried out on thirteen standard constrained benchmark problems with ten different chaotic maps to find out the most efficient one. Then, the chaotic GWO is compared with the traditional GWO and some other popular meta-heuristics viz. Firefly Algorithm, Flower Pollination Algorithm and Particle Swarm Optimization algorithm. The performance of the CGWO algorithm is also validated using five constrained engineering design problems. The results showed that with an appropriate chaotic map, CGWO can clearly outperform standard GWO, with very good performance in comparison with other algorithms and in application to constrained optimization problems. Highlights Chaos has been introduced to the GWO to develop Chaotic GWO for global optimization. Ten chaotic maps have been investigated to tune the key parameter ‘a’, of GWO. Effectiveness of the algorithm is tested on many constrained benchmark functions. Results show CGWO's better performance over other nature-inspired optimization methods. The proposed CGWO is also used for some engineering design applications.
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Milenković, Branislav, Mladen Krstić, and Đorđe Jovanović. "Application of grey wolf algorithm for solving engineering optimization problems." Tehnika 76, no. 1 (2021): 50–57. http://dx.doi.org/10.5937/tehnika2101050m.
Повний текст джерелаАнотація:
This paper presents grey wolf optimization - GWO. After presenting the biological basis of GWO, it explains the method itself and then the main algorithms of the GWO method as well as their mathematical models. The Grey Wolf Algorithm (GWO) is presented in detail as well as the manner of its operation and it application to optimization examples of engineering problems, such as: optimization of speed reducer, pressure vessel, spring, car side impact, cone coupling and cantilever beam. At the end, the results obtained by the GWO method are compared to the results previously obtained by other methods.
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Wang, Bo, Muhammad Shahzad, Xianglin Zhu, Khalil Ur Rehman, and Saad Uddin. "A Non-linear Model Predictive Control Based on Grey-Wolf Optimization Using Least-Square Support Vector Machine for Product Concentration Control in l-Lysine Fermentation." Sensors 20, no. 11 (June 11, 2020): 3335. http://dx.doi.org/10.3390/s20113335.
Повний текст джерелаАнотація:
l-Lysine is produced by a complex non-linear fermentation process. A non-linear model predictive control (NMPC) scheme is proposed to control product concentration in real time for enhancing production. However, product concentration cannot be directly measured in real time. Least-square support vector machine (LSSVM) is used to predict product concentration in real time. Grey-Wolf Optimization (GWO) algorithm is used to optimize the key model parameters (penalty factor and kernel width) of LSSVM for increasing its prediction accuracy (GWO-LSSVM). The proposed optimal prediction model is used as a process model in the non-linear model predictive control to predict product concentration. GWO is also used to solve the non-convex optimization problem in non-linear model predictive control (GWO-NMPC) for calculating optimal future inputs. The proposed GWO-based prediction model (GWO-LSSVM) and non-linear model predictive control (GWO-NMPC) are compared with the Particle Swarm Optimization (PSO)-based prediction model (PSO-LSSVM) and non-linear model predictive control (PSO-NMPC) to validate their effectiveness. The comparative results show that the prediction accuracy, adaptability, real-time tracking ability, overall error and control precision of GWO-based predictive control is better compared to PSO-based predictive control.
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Tumari, Mohd Zaidi Mohd, Mohd Muzaffar Zahar, and Mohd Ashraf Ahmad. "Optimal tuning of a wind plant energy production based on improved grey wolf optimizer." Bulletin of Electrical Engineering and Informatics 10, no. 1 (February 1, 2021): 23–30. http://dx.doi.org/10.11591/eei.v10i1.2509.
Повний текст джерелаАнотація:
The tuning of optimal controller parameters in wind plant is crucial in order to minimize the effect of wake interaction between turbines. The purpose of this paper is to develop an improved grey wolf optimizer (I-GWO) in order to tune the controller parameters of the turbines so that the total energy production of a wind plant is increased. The updating mechanism of original GWO is modified to improve the efficiency of exploration and exploitation phase while avoiding trapping in local minima solution. A row of ten turbines is considered to evaluate the effectiveness of the I-GWO by maximizing the total energy production. The proposed approach is compared with original GWO and previously published modified GWO. Finally, I-GWO produces the highest total energy production as compared to other methods, as shown in statistical performance analysis.
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Abdel-Basset, Mohamed, Reda Mohamed, Karam M. Sallam, and Ripon K. Chakrabortty. "Light Spectrum Optimizer: A Novel Physics-Inspired Metaheuristic Optimization Algorithm." Mathematics 10, no. 19 (September 23, 2022): 3466. http://dx.doi.org/10.3390/math10193466.
Повний текст джерелаАнотація:
This paper introduces a novel physical-inspired metaheuristic algorithm called “Light Spectrum Optimizer (LSO)” for continuous optimization problems. The inspiration for the proposed algorithm is the light dispersions with different angles while passing through rain droplets, causing the meteorological phenomenon of the colorful rainbow spectrum. In order to validate the proposed algorithm, three different experiments are conducted. First, LSO is tested on solving CEC 2005, and the obtained results are compared with a wide range of well-regarded metaheuristics. In the second experiment, LSO is used for solving four CEC competitions in single objective optimization benchmarks (CEC2014, CEC2017, CEC2020, and CEC2022), and its results are compared with eleven well-established and recently-published optimizers, named grey wolf optimizer (GWO), whale optimization algorithm (WOA), and salp swarm algorithm (SSA), evolutionary algorithms like differential evolution (DE), and recently-published optimizers including gradient-based optimizer (GBO), artificial gorilla troops optimizer (GTO), Runge–Kutta method (RUN) beyond the metaphor, African vultures optimization algorithm (AVOA), equilibrium optimizer (EO), grey wolf optimizer (GWO), Reptile Search Algorithm (RSA), and slime mold algorithm (SMA). In addition, several engineering design problems are solved, and the results are compared with many algorithms from the literature. The experimental results with the statistical analysis demonstrate the merits and highly superior performance of the proposed LSO algorithm.
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Gao, Zheng-Ming, and Juan Zhao. "An Improved Grey Wolf Optimization Algorithm with Variable Weights." Computational Intelligence and Neuroscience 2019 (June 2, 2019): 1–13. http://dx.doi.org/10.1155/2019/2981282.
Повний текст джерелаАнотація:
With a hypothesis that the social hierarchy of the grey wolves would be also followed in their searching positions, an improved grey wolf optimization (GWO) algorithm with variable weights (VW-GWO) is proposed. And to reduce the probability of being trapped in local optima, a new governing equation of the controlling parameter is also proposed. Simulation experiments are carried out, and comparisons are made. Results show that the proposed VW-GWO algorithm works better than the standard GWO, the ant lion optimization (ALO), the particle swarm optimization (PSO) algorithm, and the bat algorithm (BA). The novel VW-GWO algorithm is also verified in high-dimensional problems.
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Yang, Can-Ming, Ye Liu, Yi-Ting Wang, Yan-Ping Li, Wen-Hui Hou, Sheng Duan, and Jian-Qiang Wang. "A Novel Adaptive Kernel Picture Fuzzy C-Means Clustering Algorithm Based on Grey Wolf Optimizer Algorithm." Symmetry 14, no. 7 (July 13, 2022): 1442. http://dx.doi.org/10.3390/sym14071442.
Повний текст джерелаАнотація:
Over the years, research on fuzzy clustering algorithms has attracted the attention of many researchers, and they have been applied to various areas, such as image segmentation and data clustering. Various fuzzy clustering algorithms have been put forward based on the initial Fuzzy C-Means clustering (FCM) with Euclidean distance. However, the existing fuzzy clustering approaches ignore two problems. Firstly, clustering algorithms based on Euclidean distance have a high error rate, and are more sensitive to noise and outliers. Secondly, the parameters of the fuzzy clustering algorithms are hard to determine. In practice, they are often determined by the user’s experience, which results in poor performance of the clustering algorithm. Therefore, considering the above deficiencies, this paper proposes a novel fuzzy clustering algorithm by combining the Gaussian kernel function and Grey Wolf Optimizer (GWO), called Kernel-based Picture Fuzzy C-Means clustering with Grey Wolf Optimizer (KPFCM-GWO). In KPFCM-GWO, the Gaussian kernel function is used as a symmetrical measure of distance between data points and cluster centers, and the GWO is utilized to determine the parameter values of PFCM. To verify the validity of KPFCM-GWO, a comparative study was conducted. The experimental results indicate that KPFCM-GWO outperforms other clustering methods, and the improvement of KPFCM-GWO is mainly attributed to the combination of the Gaussian kernel function and the parameter optimization capability of the GWO. What is more, the paper applies KPFCM-GWO to analyzes the value of an airline’s customers, and five levels of customer categories are defined.
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Weickmann, Klaus, and Edward Berry. "The Tropical Madden–Julian Oscillation and the Global Wind Oscillation." Monthly Weather Review 137, no. 5 (May 1, 2009): 1601–14. http://dx.doi.org/10.1175/2008mwr2686.1.
Повний текст джерелаАнотація:
Abstract The global wind oscillation (GWO) is a subseasonal phenomenon encompassing the tropical Madden–Julian oscillation (MJO) and midlatitude processes like meridional momentum transports and mountain torques. A phase space is defined for the GWO following the approach of Wheeler and Hendon for the MJO. In contrast to the oscillatory behavior of the MJO, two red noise processes define the GWO. The red noise spectra have variance at periods that bracket 30–60 or 30–80 days, which are bands used to define the MJO. The correlation between the MJO and GWO is ∼0.5 and cross spectra show well-defined, coherent phase relations in similar frequency bands. However, considerable independent variance exists in the GWO. A basic dynamical distinction occurs in the direction of midlatitude wave energy dispersion, being predominantly meridional during a MJO and zonal during the GWO. This is primarily a winter season feature centered over the Pacific Ocean. A case study during April–May 2007 focuses on the GWO and two ∼30-day duration orbits with extreme anomalies in GWO phase space. The MJO phase space projections for the same time were irregular and, it is argued, partially driven by mountain torques and meridional transports. The case study reveals that multiple physical processes and time scales act to create slowly evolving planetary-scale circulation and tropical convection anomalies.
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Yan, Bo, Xu Yang Zhao, Na Xu, Yu Chen, and Wen Bo Zhao. "A Grey Wolf Optimization-based Track-Before-Detect Method for Maneuvering Extended Target Detection and Tracking." Sensors 19, no. 7 (April 1, 2019): 1577. http://dx.doi.org/10.3390/s19071577.
Повний текст джерелаАнотація:
A grey wolf optimization-based track-before-detect (GWO-TBD) method is developed for extended target detection and tracking. The aim of the GWO-TBD is tracking weak and maneuvering extended targets in a cluttered environment using the measurement points of an air surveillance radar. The optimal solution is the trajectory constituted by the points of an extended target. At the beginning of the GWO-TBD, the measurements of each scan are clustered into alternative sets. Secondly, closely sets are associated for tracklets. Each tracklet equals a candidate solution. Thirdly, the tracklets are further associated iteratively to find a better solution. An improved GWO algorithm is developed in the iteration for removal of unappreciated solution and acceleration of convergence. After the iteration of several generations, the optimal solution can be achieved, i.e. trajectory of an extended target. Both the real data and synthetic data are performed with the GWO-TBD and several existing algorithms in this work. Result infers that the GWO-TBD is superior to the others in detecting and tracking maneuvering targets. Meanwhile, much less prior information is necessary in the GWO-TBD. It makes the approach is engineering friendly.
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Yetkin, M., and O. Bilginer. "On the application of nature-inspired grey wolf optimizer algorithm in geodesy." Journal of Geodetic Science 10, no. 1 (June 24, 2020): 48–52. http://dx.doi.org/10.1515/jogs-2020-0107.
Повний текст джерелаАнотація:
AbstractNowadays, solving hard optimization problems using metaheuristic algorithms has attracted bountiful attention. Generally, these algorithms are inspired by natural metaphors. A novel metaheuristic algorithm, namely Grey Wolf Optimization (GWO), might be applied in the solution of geodetic optimization problems. The GWO algorithm is based on the intelligent behaviors of grey wolves and a population based stochastic optimization method. One great advantage of GWO is that there are fewer control parameters to adjust. The algorithm mimics the leadership hierarchy and hunting mechanism of grey wolves in nature. In the present paper, the GWO algorithm is applied in the calibration of an Electronic Distance Measurement (EDM) instrument using the Least Squares (LS) principle for the first time. Furthermore, a robust parameter estimator called the Least Trimmed Absolute Value (LTAV) is applied to a leveling network for the first time. The GWO algorithm is used as a computing tool in the implementation of robust estimation. The results obtained by GWO are compared with the results of the ordinary LS method. The results reveal that the use of GWO may provide efficient results compared to the classical approach.
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Makhija, Divya, Posham Bhargava Reddy, Chapram Sudhakar, and Varsha Kumari. "Workflow Scheduling in Cloud Computing Environment by Combining Particle Swarm Optimization and Grey Wolf Optimization." Computer Science & Engineering: An International Journal 12, no. 6 (December 30, 2022): 01–10. http://dx.doi.org/10.5121/cseij.2022.12601.
Повний текст джерелаАнотація:
Scheduling workflows is a vital challenge in cloud computing due to its NP-complete nature and if an efficient workflow task scheduling algorithm is not used then it affects the system’s overall performance. Therefore, there is a need for an efficient workflow task scheduling algorithm that can distribute dependent tasks to virtual machines efficiently. In this paper, a hybrid workflow task scheduling algorithm based on a combination of Particle Swarm Optimization and Grey Wolf Optimization (PSO GWO) algorithms, is proposed. PSO GWO overcomes the disadvantages of both PSO and GWO algorithms by improving the exploitation (local search) of PSO algorithm and exploration (global search) of GWO algorithm. This leads to better balance between exploration and exploitation, consequently it minimizes the makespan with 5.52% compared to GWO and 3.68% compared to PSO. The degree of imbalance reduced upto 33.22% compared to GWO and 17.61% compared to PSO, improves the convergence rate as well depending on number tasks and iterations. CloudSim tool is used to evaluate the proposed algorithm. The simulation results confirmed that the proposed method performs better than both of the standard PSO and GWO in terms of makespan, degree of imbalance and convergence rate
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Issa Mohsin, Asma, Asaad S. Daghal, and Adheed Hasan Sallomi. "A beamforming study of the linear antenna array using grey wolf optimization algorithm." Indonesian Journal of Electrical Engineering and Computer Science 20, no. 3 (December 1, 2020): 1538. http://dx.doi.org/10.11591/ijeecs.v20.i3.pp1538-1546.
Повний текст джерелаАнотація:
<p><br />The grey wolf optimization (GWO) algorithm is considered an inspired meta-heuristic algorithm, which inspired by the social hierarchy and hunting behavior of the grey wolves. GWO has a high-performance capability of solving constrained, as well as unconstrained optimization problems. In this paper, the beamforming of smart antennas in a code division multiple access system based on the GWO algorithm is investigated. The sidelobe level (SLL) is minimized along with peak sidelobe level reduction, as well as an optimal beam pattern has been accomplished by using GWO to uniform linear antenna arrays. In this work, an amplitude is introduced as constant, while the interspacing distance between antenna array elements and the number of elements in a linear array are variables. The simulation results show that a faster convergence and likely high accurate beamforming are gained using GWO based method. Finally, it is shown that the GWO outperforms the genetic algorithm (GA) based method.</p>
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Santosa, Paulus Insap, and Ricardus Anggi Pramunendar. "A Robust Feature Construction for Fish Classification Using Grey Wolf Optimizer." Cybernetics and Information Technologies 22, no. 4 (November 1, 2022): 152–66. http://dx.doi.org/10.2478/cait-2022-0045.
Повний текст джерелаАнотація:
Abstract The low quality of the collected fish image data directly from its habitat affects its feature qualities. Previous studies tended to be more concerned with finding the best method rather than the feature quality. This article proposes a new fish classification workflow using a combination of Contrast-Adaptive Color Correction (NCACC) image enhancement and optimization-based feature construction called Grey Wolf Optimizer (GWO). This approach improves the image feature extraction results to obtain new and more meaningful features. This article compares the GWO-based and other optimization method-based fish classification on the newly generated features. The comparison results show that GWO-based classification had 0.22% lower accuracy than GA-based but 1.13 % higher than PSO. Based on ANOVA tests, the accuracy of GA and GWO were statistically indifferent, and GWO and PSO were statistically different. On the other hand, GWO-based performed 0.61 times faster than GA-based classification and 1.36 minutes faster than the other.
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Lan, Pu, Kewen Xia, Yongke Pan, and Shurui Fan. "An Improved GWO Algorithm Optimized RVFL Model for Oil Layer Prediction." Electronics 10, no. 24 (December 20, 2021): 3178. http://dx.doi.org/10.3390/electronics10243178.
Повний текст джерелаАнотація:
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.
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Jabbar, Ayad Mohammed, and Ku Ruhana Ku-Mahamud. "Grey wolf optimization algorithm for hierarchical document clustering." Indonesian Journal of Electrical Engineering and Computer Science 24, no. 3 (December 1, 2021): 1744. http://dx.doi.org/10.11591/ijeecs.v24.i3.pp1744-1758.
Повний текст джерелаАнотація:
In data mining, the application of grey wolf optimization (GWO) algorithm has been used in several learning approaches because of its simplicity in adapting to different application domains. Most recent works that concern unsupervised learning have focused on text clustering, where the GWO algorithm shows promising results. Although GWO has great potential in performing text clustering, it has limitations in dealing with outlier documents and noise data. This research introduces medoid GWO (M-GWO) algorithm, which incorporates a medoid recalculation process to share the information of medoids among the three best wolves and the rest of the population. This improvement aims to find the best set of medoids during the algorithm run and increases the exploitation search to find more local regions in the search space. Experimental results obtained from using well-known algorithms, such as genetic, firefly, GWO, and k-means algorithms, in four benchmarks. The results of external evaluation metrics, such as rand, purity, F-measure, and entropy, indicates that the proposed M-GWO algorithm achieves better document clustering than all other algorithms (i.e., 75% better when using Rand metric, 50% better than all algorithm based on purity metric, 75% better than all algorithms using F-measure metric, and 100% based on entropy metric).
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Saxena, Prerna, and Ashwin Kothari. "Optimal Pattern Synthesis of Linear Antenna Array Using Grey Wolf Optimization Algorithm." International Journal of Antennas and Propagation 2016 (2016): 1–11. http://dx.doi.org/10.1155/2016/1205970.
Повний текст джерелаАнотація:
The aim of this paper is to introduce the grey wolf optimization (GWO) algorithm to the electromagnetics and antenna community. GWO is a new nature-inspired metaheuristic algorithm inspired by the social hierarchy and hunting behavior of grey wolves. It has potential to exhibit high performance in solving not only unconstrained but also constrained optimization problems. In this work, GWO has been applied to linear antenna arrays for optimal pattern synthesis in the following ways: by optimizing the antenna positions while assuming uniform excitation and by optimizing the antenna current amplitudes while assuming spacing and phase as that of uniform array. GWO is used to achieve an array pattern with minimum side lobe level (SLL) along with null placement in the specified directions. GWO is also applied for the minimization of the first side lobe nearest to the main beam (near side lobe). Various examples are presented that illustrate the application of GWO for linear array optimization and, subsequently, the results are validated by benchmarking with results obtained using other state-of-the-art nature-inspired evolutionary algorithms. The results suggest that optimization of linear antenna arrays using GWO provides considerable enhancements compared to the uniform array and the synthesis obtained from other optimization techniques.
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Junian, Wahyu Eko, and Hendra Grandis. "HYBRID PARTICLE SWARM OPTIMIZATION AND GREY WOLF OPTIMIZER ALGORITHM FOR CONTROLLED SOURCE AUDIO-FREQUENCY MAGNETOTELLURICS (CSAMT) ONE-DIMENSIONAL INVERSION MODELLING." Rudarsko-geološko-naftni zbornik 38, no. 3 (2023): 65–80. http://dx.doi.org/10.17794/rgn.2023.3.6.
Повний текст джерелаАнотація:
The Controlled Source Audio-frequency Magnetotellurics (CSAMT) is a geophysical method utilizing artificial electromagnetic signal source to estimate subsurface resistivity structures. One-dimensional (1D) inversion modelling of CSAMT data is non-linear and the solution can be estimated by using global optimization algorithms. Particle Swarm Optimization (PSO) and Grey Wolf Optimizer (GWO) are well-known population-based algorithms having relatively simple mathematical formulation and implementation. Hybridization of PSO and GWO algorithms (called hybrid PSO-GWO) can improve the convergence capability to the global solution. This study applied the hybrid PSO-GWO algorithm for 1D CSAMT inversion modelling. Tests were conducted with synthetic CSAMT data associated with 3-layer, 4-layer and 5-layer earth models to determine the performance of the algorithm. The results show that the hybrid PSO-GWO algorithm has a good performance in obtaining the minimum misfit compared to the original PSO and GWO algorithms. The hybrid PSO-GWO algorithm was also applied to invert CSAMT field data for gold mineralization exploration in the Cibaliung area, Banten Province, Indonesia. The algorithm was able to reconstruct the resistivity model very well which is confirmed by the results from inversion of the data using standard 2D MT inversion software. The model also agrees well with the geological information of the study area.
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G. Krishna Mohan, Dr, Ms N. Sai Prasanna, Mr K. Siva Sai Krishna, and Mr I. Vamsi Krishna. "Applying Distribution Functions to GWO Algorithm." International Journal of Engineering & Technology 7, no. 2.32 (May 31, 2018): 192. http://dx.doi.org/10.14419/ijet.v7i2.32.15565.
Повний текст джерелаАнотація:
GWO is an Optimization algorithm. It depends on the different distribution functions. The features of Optimization algorithm are as follows Convergence, precision, and performance. These Characters will generalize this optimization algorithm. In this paper, we explored GWO algorithm for different distributing functions. There are many distribution functions that are kept practical to the GWO algorithm. We evaluated three different distribution functions which are the Gold Stein function, Beale function and the Booth function. To show the effectiveness of the GWO algorithm we have used the above three distribution functions.
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Wan, Yihao, Mingxuan Mao, Lin Zhou, Qianjin Zhang, Xinze Xi, and Chen Zheng. "A Novel Nature-Inspired Maximum Power Point Tracking (MPPT) Controller Based on SSA-GWO Algorithm for Partially Shaded Photovoltaic Systems." Electronics 8, no. 6 (June 15, 2019): 680. http://dx.doi.org/10.3390/electronics8060680.
Повний текст джерелаАнотація:
To overcome the real-time problem of maximum power point tracking (MPPT) for partially shaded photovoltaic (PV) systems, a novel nature-inspired MPPT controller with fast convergence and high accuracy is proposed in this paper. The proposed MPPT controller is achieved by combining salp swarm algorithm (SSA) with grey wolf optimizer (GWO) (namely, SSA-GWO). The leader structure of the GWO algorithm is introduced into the basic SSA algorithm to enhance the global search capability. Numerical simulation on 13 benchmark functions was done to evaluate the proposed SSA-GWO algorithm. Finally, the MPPT performance on PV system with the proposed SSA-GWO algorithm under static and dynamic partial shading conditions was investigated and compared with conventional MPPT algorithms. The quantitative and simulation results validated the effectiveness and superiority of the proposed method.
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Adedeji, Wasiu Oyediran, Mofoluwaso Kehinde Adeniran, Kasali Aderinmoye Adedeji, John Rajan, Sunday Ayoola Oke, and Elkanah Olaosebikan Oyetunji. "Optimization of the Wire Electric Discharge Machining Process of Nitinol-60 Shape Memory Alloy Using Taguchi-Pareto Design of Experiments, Grey-Wolf Analysis, and Desirability Function Analysis." IJIEM - Indonesian Journal of Industrial Engineering and Management 4, no. 1 (February 28, 2023): 28. http://dx.doi.org/10.22441/ijiem.v4i1.18087.
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The nitinol-60 shape memory alloy has been rated as the most widely utilized material in real-life industrial applications, including biomedical appliances, coupling and sealing elements, and activators, among others. However, less is known about its optimization characteristics while taking advantage to choose the best parameter in a surface integrity analysis using the wire EDM process. In this research, the authors proposed a robust Taguchi-Pareto (TP)-grey wolf optimization (GWO)-desirability function analysis (DFA) scheme that hybridizes the TP method, GWO approach, and DFA method. The point of coupling of the TP method to the GWO is the introduction of the discriminated signal-to-noise ratios contained in the selected 80-20 Pareto rule of the TP method into the objective function of the GWO, which was converted from multiple responses to a single response accommodated by the GWO. The comparative results of five outputs of the wire EDM process before and after optimization reveals the following understanding. For the CR, a gain of 398% was observed whereas for the outputs named Rz, Rt, SCD, and RLT, losses of 0.0996, 0.0875, 0.0821, and 0.0332 were recorded. This discrimination of signal-to-noise ratio based on the 80-20 rule makes the research different from previous studies, restricting the data fed into the GWO scheme to the most essential to accomplishing the TP-GWO-DFA scheme proposed. The use of the TP-GWO-DFA method is efficient given the limited volume of data required to optimize the wire EDM process parameters of nitinol.
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Li, Tan, Lin Li, and Zhicheng Liu. "Time Course Changes of the Mechanical Properties of the Iris Pigment Epithelium in a Rat Chronic Ocular Hypertension Model." BioMed Research International 2018 (October 21, 2018): 1–10. http://dx.doi.org/10.1155/2018/4862309.
Повний текст джерелаАнотація:
Background. The flow field of aqueous humor correlates to the stiffness of iris pigment epithelium (IPE) which acts as a wall of posterior chamber. We focus on the variations of IPE stiffness in a rat ocular hypertension (OHT) model, so as to prepare for exploring the mechanism of duration of OHT. Methods. Episcleral venous cauterization (EVC) was applied on one eye of male adult Sprague-Dawley rats to induce chronic high intraocular pressure. According to the duration of OHT (0, 1, 2, 4, and 8 weeks), rats were randomly divided into Gw0, Gw1, Gw2, Gw4, and Gw8. Atomic force microscope (AFM) analysis was applied to test IPE stiffness in three regions: iris root, mid-periphery, and pupillary-margin in each group. Histological changes of IPE were also examined in Gw4 and Gw8. Results. There was an overall growing tendency of IPE stiffness in EVC eye. IPE in EVC eye was significantly stiffer than fellow eye in Gw2, Gw4, and Gw8 (in iris root, mid-periphery, and pupillary-margin, p<0.05). IPE in EVC eye in pupillary-margin was significantly stiffer than iris root in Gw4 and Gw8 (p<0.05). In EVC eye, IPE becomes thinner and IPE cell density decreases. Conclusion. IPE stiffness increases gradually with the duration of chronic high intraocular pressure.
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Chakraborty, Sayan, Ratika Pradhan, Amira S. Ashour, Luminita Moraru, and Nilanjan Dey. "Grey-Wolf-Based Wang’s Demons for Retinal Image Registration." Entropy 22, no. 6 (June 15, 2020): 659. http://dx.doi.org/10.3390/e22060659.
Повний текст джерелаАнотація:
Image registration has an imperative role in medical imaging. In this work, a grey-wolf optimizer (GWO)-based non-rigid demons registration is proposed to support the retinal image registration process. A comparative study of the proposed GWO-based demons registration framework with cuckoo search, firefly algorithm, and particle swarm optimization-based demons registration is conducted. In addition, a comparative analysis of different demons registration methods, such as Wang’s demons, Tang’s demons, and Thirion’s demons which are optimized using the proposed GWO is carried out. The results established the superiority of the GWO-based framework which achieved 0.9977 correlation, and fast processing compared to the use of the other optimization algorithms. Moreover, GWO-based Wang’s demons performed better accuracy compared to the Tang’s demons and Thirion’s demons framework. It also achieved the best less registration error of 8.36 × 10−5.
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Elaiyaraja, K., and M. Senthil Kumar. "A Novel Variable Weight Grey Wolf Optimization Algorithm in Medical Image Fusion." Journal of Medical Imaging and Health Informatics 11, no. 5 (May 1, 2021): 1501–8. http://dx.doi.org/10.1166/jmihi.2021.3475.
Повний текст джерелаАнотація:
Medical image fusion (MIF) is essential in clinical domain that integrates the multi-modal medical features to a unique frame known as fused image which finds utility in diagnosis process. Scaling based approaches are the commonly used multimodal MIF model where the generalized scaling has a stationary scale value selection that enhances the fusion quality Discrete Wavelet Transform (db4)-based approaches give a maximum amount of approximation in multi-modal medical image fusion, while using less edge features. For generating efficient edge features, Laplacian filtering (LF) approach is employed. This paper introduces an optimized Laplacian Wavelet Mask (OLWM) based fusion model for multi-modal MIF using Variable Weight Grey Wolf Optimization (VW-GWO). An enhanced GWO algorithm with variable weights (VW-GWO) is faced with the idea of using the social hierarchy of the grey wolves to locate the searching positions. Besides, to minimize the possibility of trapping into local optima, an efficient parameter control mechanism is employed. The VW-GWO algorithm has the capability to choose the control variables of the GWO algorithm in an automated way. A set of medical images, including MR-SPECT, MR-PET, MR-CT and MR: T1–T2 of brain scans, validates the proposed VW-GWO algorithm. The simulation outcome showed that the effectiveness of the VW-GWO algorithm seems to be much higher over the compared methods under various dimensions.
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Tang, Kai, Jiahui Li, MengTing Yang, Xinyi Yang, Junxiong Feng, and Suhang Liu. "A Hybrid Model Based on Grey Wolf Optimizer and Lagrangian Support Vector Regression for European Natural Gas Consumption Forecasting." Journal of Energy Research and Reviews 13, no. 2 (February 23, 2023): 11–19. http://dx.doi.org/10.9734/jenrr/2023/v13i2258.
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Natural gas plays an important role in industry as a clean energy, with the intensification of the Russia-Ukraine war, there is a large-scale energy shortage in Europe, and the natural gas supply in Europe has a natural gas crisis due to the cut-off of the Nord Stream No.1 pipeline. Therefore, it is necessary to accurately predict the consumption of natural gas. In order to fulfill this requirement, this paper uses the Lagrangian Support Vector Regression model with Sorensen kernel based on the Nonlinear Auto-Regressive model and Grey Wolf Optimizer for 5-step forecasting of monthly natural gas consumption in all European countries. Under three time lags, comparing the 5-step predict results of GWO-LSVR with SVR, RF, LightGBM, XGBoost, and MLP, those five models’ hyperparameters also optimized by GWO, it found that GWO-LSVR has smallest MAPE in almost all cases, and the numerical results of MAPE generated by GWO-LSVR is from 5.844% to 11.622%, the smaller the forecasting step size, the better the effect. Moreover, compares the difference of GWO and WOA, it is found that GWO can obtained better model hyperparameters and smaller MAPE results. To sum up, the proposed GWO-LSVR model has strong generalization performance and robustness, and is a reliable natural gas consumption prediction model.
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Rezaei, Farshad, Hamid R. Safavi, Mohamed Abd Elaziz, Laith Abualigah, Seyedali Mirjalili, and Amir H. Gandomi. "Diversity-Based Evolutionary Population Dynamics: A New Operator for Grey Wolf Optimizer." Processes 10, no. 12 (December 6, 2022): 2615. http://dx.doi.org/10.3390/pr10122615.
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Evolutionary Population Dynamics (EPD) refers to eliminating poor individuals in nature, which is the opposite of survival of the fittest. Although this method can improve the median of the whole population of the meta-heuristic algorithms, it suffers from poor exploration capability to handle high-dimensional problems. This paper proposes a novel EPD operator to improve the search process. In other words, as the primary EPD mainly improves the fitness of the worst individuals in the population, and hence we name it the Fitness-Based EPD (FB-EPD), our proposed EPD mainly improves the diversity of the best individuals, and hence we name it the Diversity-Based EPD (DB-EPD). The proposed method is applied to the Grey Wolf Optimizer (GWO) and named DB-GWO-EPD. In this algorithm, the three most diversified individuals are first identified at each iteration, and then half of the best-fitted individuals are forced to be eliminated and repositioned around these diversified agents with equal probability. This process can free the merged best individuals located in a closed populated region and transfer them to the diversified and, thus, less-densely populated regions in the search space. This approach is frequently employed to make the search agents explore the whole search space. The proposed DB-GWO-EPD is tested on 13 high-dimensional and shifted classical benchmark functions as well as 29 test problems included in the CEC2017 test suite, and four constrained engineering problems. The results obtained by the proposal upon implemented on the classical test problems are compared to GWO, FB-GWO-EPD, and four other popular and newly proposed optimization algorithms, including Aquila Optimizer (AO), Flow Direction Algorithm (FDA), Arithmetic Optimization Algorithm (AOA), and Gradient-based Optimizer (GBO). The experiments demonstrate the significant superiority of the proposed algorithm when applied to a majority of the test functions, recommending the application of the proposed EPD operator to any other meta-heuristic whenever decided to ameliorate their performance.
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Nsour, Heba Al, Mohammed Alweshah, Abdelaziz I. Hammouri, Hussein Al Ofeishat, and Seyedali Mirjalili. "A Hybrid Grey Wolf Optimiser Algorithm for Solving Time Series Classification Problems." Journal of Intelligent Systems 29, no. 1 (July 31, 2018): 846–57. http://dx.doi.org/10.1515/jisys-2018-0129.
Повний текст джерелаАнотація:
Abstract One of the major objectives of any classification technique is to categorise the incoming input values based on their various attributes. Many techniques have been described in the literature, one of them being the probabilistic neural network (PNN). There were many comparisons made between the various published techniques depending on their precision. In this study, the researchers investigated the search capability of the grey wolf optimiser (GWO) algorithm for determining the optimised values of the PNN weights. To the best of our knowledge, we report for the first time on a GWO algorithm along with the PNN for solving the classification of time series problem. PNN was used for obtaining the primary solution, and thereby the PNN weights were adjusted using the GWO for solving the time series data and further decreasing the error rate. In this study, the main goal was to investigate the application of the GWO algorithm along with the PNN classifier for improving the classification precision and enhancing the balance between exploitation and exploration in the GWO search algorithm. The hybrid GWO-PNN algorithm was used in this study, and the results obtained were compared with the published literature. The experimental results for six benchmark time series datasets showed that this hybrid GWO-PNN outperformed the PNN algorithm for the studied datasets. It has been seen that hybrid classification techniques are more precise and reliable for solving classification problems. A comparison with other algorithms in the published literature showed that the hybrid GWO-PNN could decrease the error rate and could also generate a better result for five of the datasets studied.
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Li, Taiyong, Zijie Qian, and Ting He. "Short-Term Load Forecasting with Improved CEEMDAN and GWO-Based Multiple Kernel ELM." Complexity 2020 (February 25, 2020): 1–20. http://dx.doi.org/10.1155/2020/1209547.
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Short-term load forecasting (STLF) is an essential and challenging task for power- or energy-providing companies. Recent research has demonstrated that a framework called “decomposition and ensemble” is very powerful for energy forecasting. To improve the effectiveness of STLF, this paper proposes a novel approach integrating the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), grey wolf optimization (GWO), and multiple kernel extreme learning machine (MKELM), namely, ICEEMDAN-GWO-MKELM, for STLF, following this framework. The proposed ICEEMDAN-GWO-MKELM consists of three stages. First, the complex raw load data are decomposed into a couple of relatively simple components by ICEEMDAN. Second, MKELM is used to forecast each decomposed component individually. Specifically, we use GWO to optimize both the weight and the parameters of every single kernel in extreme learning machine to improve the forecasting ability. Finally, the results of all the components are aggregated as the final forecasting result. The extensive experiments reveal that the ICEEMDAN-GWO-MKELM can outperform several state-of-the-art forecasting approaches in terms of some evaluation criteria, showing that the ICEEMDAN-GWO-MKELM is very effective for STLF.
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Xiong, Zhenqiang, Jiadong Li, Peng Zhao, and Yong Li. "Prediction of Mechanical Properties of Aluminium Alloy Strip Using the Extreme Learning Machine Model Optimized by the Gray Wolf Algorithm." Advances in Materials Science and Engineering 2023 (July 4, 2023): 1–16. http://dx.doi.org/10.1155/2023/5952072.
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Mechanical properties are important indicators for evaluating the quality of strips. This paper proposes a mechanical performance prediction model based on the Gray Wolf Optimization (GWO) algorithm and the Extreme Learning Machine (ELM) algorithm. In the modeling process, GWO is used to determine the optimal weights and deviations of ELM and experiments are used to determine the model’s key parameters. The model effectively avoids manual intervention and significantly improves aluminum alloy strips’ mechanical property prediction accuracy. This paper uses processed data from the aluminum alloy production plant of Shandong Nanshan Aluminum Co., Ltd. as experimental data. When the prediction deviation is controlled within ±10%, the GWO-ELM model can achieve a correct rate of 100% for tensile strength, 97.5% for yield strength, and 77.5% for elongation on the test set. The RMSE of the tensile strength, yield strength, and elongation of the GWO-ELM model was 5.365, 11.881, and 1.268, respectively. The experimental results show that the GWO-ELM model has higher accuracy and stability in predicting aluminum alloy strips’ tensile strength, yield strength, and elongation. The GWO-ELM model effectively avoids the defects of the traditional model. It has a special guiding significance for producing aluminum alloy strips.
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Liu, Zhaosu, and Jianfeng Yang. "Research on Short-term Load Forecasting Based on GWO-BILSTM." Journal of Physics: Conference Series 2290, no. 1 (June 1, 2022): 012100. http://dx.doi.org/10.1088/1742-6596/2290/1/012100.
Повний текст джерелаАнотація:
Abstract In short-term power load forecasting, changes in external multi-dimensional factors will have a certain impact on the accuracy of load forecasting. In response to this problem, this paper proposes a prediction model GWO-BILSTM that combines the Grey Wolf Optimizer (GWO) and the bi-directional long short-term memory (BILSTM) network. Taking the real power load data as the data set, the high correlation parameters are selected as the input through the Pearson correlation analysis, the hyperparameters of BILSTM are optimized by the GWO algorithm, and finally the GWO-BILSTM prediction model is established based on the optimized parameters to predict the data set. The experimental results show that the mean absolute percentage error, root mean square error and mean absolute error index of the GWO-BILSTM model are better than other comparison models, which effectively improves the prediction accuracy of multi-dimensional power load data.
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R., Essaki Raj, and Sundaramoorthy Sridhar. "Grey wolf optimizer algorithm for the performance predetermination of variable speed self-excited induction generators." COMPEL - The international journal for computation and mathematics in electrical and electronic engineering 41, no. 1 (December 8, 2021): 319–33. http://dx.doi.org/10.1108/compel-06-2021-0197.
Повний текст джерелаАнотація:
Purpose This paper aims to apply grey wolf optimizer (GWO) algorithm for steady state analysis of self-excited induction generators (SEIGs) supplying isolated loads. Design/methodology/approach Taking the equivalent circuit of SEIG, the impedances representing the stator, rotor and the connected load are reduced to a single loop impedance in terms of the unknown frequency, magnetizing reactance and core loss resistance for the given rotor speed. This loop impedance is taken as the objective function and minimized using GWO to solve for the unknown parameters. By including the value of the desired voltage as a constraint, the formulated objective function is also extended for estimating the required excitation capacitance. Findings The experimental results obtained on a three phase 415 V, 3.5 kW SEIG and the corresponding predetermined performance characteristics agree closely, thereby validating the proposed GWO method. Moreover, a comparative study of GWO with genetic algorithm and particle swarm optimization techniques reveals that GWO exhibits much quicker convergence of the objective function. Originality/value The important contributions of this paper are as follows: for the first time, GWO has been introduced for the SEIG performance predetermination and computation of the excitation capacitance for attaining the desired terminal voltage for the given load and speed; the predicted performance accuracy is improved by considering the variable core loss of the SEIG; and GWO does not require derivations of lengthy equations for calculating the SEIG performance.
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Yan, Fu, Jianzhong Xu, and Kumchol Yun. "Dynamically Dimensioned Search Grey Wolf Optimizer Based on Positional Interaction Information." Complexity 2019 (December 5, 2019): 1–36. http://dx.doi.org/10.1155/2019/7189653.
Повний текст джерелаАнотація:
The grey wolf optimizer (GWO) algorithm is a recently developed, novel, population-based optimization technique that is inspired by the hunting mechanism of grey wolves. The GWO algorithm has some distinct advantages, such as few algorithm parameters, strong global optimization ability, and ease of implementation on a computer. However, the paramount challenge is that there are some cases where the GWO is prone to stagnation in local optima. This drawback of the GWO algorithm may be attributed to an insufficiency in its position-updated equation, which disregards the positional interaction information about the three best grey wolves (i.e., the three leaders). This paper proposes an improved version of the GWO algorithm that is based on a dynamically dimensioned search, spiral walking predation technique, and positional interaction information (referred to as the DGWO). In addition, a nonlinear control parameter strategy, i.e., the control parameter that is nonlinearly increased with an increase in iterations, is designed to balance the exploration and exploitation of the GWO algorithm. The experimental results for 23 general benchmark functions and 3 well-known engineering optimization design applications validate the effectiveness and feasibility of the proposed DGWO algorithm. The comparison results for the 23 benchmark functions show that the proposed DGWO algorithm performs significantly better than the GWO and its improved variant for most benchmarks. The DGWO provides the highest solution precision, strongest robustness, and fastest convergence rate among the compared algorithms in almost all cases.
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Dang, Minh Phung, Hieu Giang Le, Ngoc Phat Nguyen, Ngoc Le Chau, and Thanh-Phong Dao. "Optimization for a New XY Positioning Mechanism by Artificial Neural Network-Based Metaheuristic Algorithms." Computational Intelligence and Neuroscience 2022 (December 1, 2022): 1–18. http://dx.doi.org/10.1155/2022/9151146.
Повний текст джерелаАнотація:
This paper devotes a new method in modeling and optimizing to handle the optimization of the XY positioning mechanism. The fitness functions and constraints of the mechanism are formulated via proposing a combination of artificial neural network (ANN) and particle swarm optimization (PSO) methods. Next, the PSO is hybridized with the grey wolf optimization, namely PSO-GWO, which is applied to three scenarios in handling the single objective function. In order to search the multiple functions for the mechanism, the multiobjective optimization genetic algorithm (MOGA) is applied to the last scenario. The achieved results showed that the fitness functions are well-formulated using the PSO-based ANN method. In the scenario 1, the stroke achieved by the PSO-GWO (1852.9842 μm) is better than that gained from the GWO (1802.8087 μm). In the scenarios 2, the stress gained from the PSO-GWO (243.3183 MPa) is lower than that achieved from the GWO (245.0401 MPa). In the scenario 3, the safety factor retrieved from the PSO-GWO (1.9767) is greater than that achieved from the GWO (1.9278). In the scenario 4, by using MOGA, the optimal results found that the stroke is about (1741.3 μm) and the safety factor is 1.8929. The prediction results are well-fitted with the numerical and experimental verifications. The results of this paper are expected to facilitate the synthesis and analysis of compliant mechanisms and related engineering designs.
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Gupta, Priyank, Sanjay Kumar Gupta, and Rakesh Singh Jadon. "Adaptive Grey Wolf Optimization Technique for Stock Index Price Prediction on Recurring Neural Network Variants." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 11s (October 7, 2023): 309–18. http://dx.doi.org/10.17762/ijritcc.v11i11s.8103.
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In this paper, we propose a Long short-term memory (LSTM) and Adaptive Grey Wolf Optimization (GWO)--based hybrid model for predicting the stock prices of the Major Indian stock indices, i.e., Sensex. The LSTM is an advanced neural network that handles uncertain, nonlinear, and sequential data. The challenges are its weight and bias optimization. The classical backpropagation has issues of dangling on local minima or overfitting the dataset. Thus, we propose a GWO-based hybrid approach to evolve the weights and biases of the LSTM and the dense layers. We have made the GWO more robust by introducing an approach to improve the best possible solution by using the optimal ranking of the wolves. The proposed model combines the GWO with Adam Optimizer to train the LSTM. Apart from the LSTM, we have also implemented the Adaptive GWO on other variants of Recurring Neural Networks (RNN) like LSTM, Bi-Directional LSTM, Gated Recurrent Units (GRU), and Bi-Directional GRU and computed the corresponding results. The Adaptive GWO here evolves the initial weights and biases of the above-discussed neural networks. In this research, we have also compared the forecasting efficiency of our proposed work with a particle-warm optimization (PSO) based hybrid LSTM model, simple Grey-wolf Optimization (GWO), and Adaptive PSO. According to the experimental findings, the suggested model has effectively used the best initial weights, and its results are the best overall.
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Luo, Yuan, Qiong Qin, Zhangfang Hu, and Yi Zhang. "Path Planning for Unmanned Delivery Robots Based on EWB-GWO Algorithm." Sensors 23, no. 4 (February 7, 2023): 1867. http://dx.doi.org/10.3390/s23041867.
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With the rise of robotics within various fields, there has been a significant development in the use of mobile robots. For mobile robots performing unmanned delivery tasks, autonomous robot navigation based on complex environments is particularly important. In this paper, an improved Gray Wolf Optimization (GWO)-based algorithm is proposed to realize the autonomous path planning of mobile robots in complex scenarios. First, the strategy for generating the initial wolf pack of the GWO algorithm is modified by introducing a two-dimensional Tent–Sine coupled chaotic mapping in this paper. This guarantees that the GWO algorithm generates the initial population diversity while improving the randomness between the two-dimensional state variables of the path nodes. Second, by introducing the opposition-based learning method based on the elite strategy, the adaptive nonlinear inertia weight strategy and random wandering law of the Butterfly Optimization Algorithm (BOA), this paper improves the defects of slow convergence speed, low accuracy, and imbalance between global exploration and local mining functions of the GWO algorithm in dealing with high-dimensional complex problems. In this paper, the improved algorithm is named as an EWB-GWO algorithm, where EWB is the abbreviation of three strategies. Finally, this paper enhances the rationalization of the initial population generation of the EWB-GWO algorithm based on the visual-field line detection technique of Bresenham’s line algorithm, reduces the number of iterations of the EWB-GWO algorithm, and decreases the time complexity of the algorithm in dealing with the path planning problem. The simulation results show that the EWB-GWO algorithm is very competitive among metaheuristics of the same type. It also achieves optimal path length measures and smoothness metrics in the path planning experiments.
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Hu, Pei, Jeng-Shyang Pan, Shu-Chuan Chu, Qing-Wei Chai, Tao Liu, and Zhong-Cui Li. "New Hybrid Algorithms for Prediction of Daily Load of Power Network." Applied Sciences 9, no. 21 (October 24, 2019): 4514. http://dx.doi.org/10.3390/app9214514.
Повний текст джерелаАнотація:
Two new hybrid algorithms are proposed to improve the performances of the meta-heuristic optimization algorithms, namely the Grey Wolf Optimizer (GWO) and Shuffled Frog Leaping Algorithm (SFLA). Firstly, it advances the hierarchy and position updating of the mathematical model of GWO, and then the SGWO algorithm is proposed based on the advantages of SFLA and GWO. It not only improves the ability of local search, but also speeds up the global convergence. Secondly, the SGWOD algorithm based on SGWO is proposed by using the benefit of differential evolution strategy. Through the experiments of the 29 benchmark functions, which are composed of the functions of unimodal, multimodal, fixed-dimension and composite multimodal, the performances of the new algorithms are better than that of GWO, SFLA and GWO-DE, and they greatly balances the exploration and exploitation. The proposed SGWO and SGWOD algorithms are also applied to the prediction model based on the neural network. Experimental results show the usefulness for forecasting the power daily load.
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Chen, Xiaoyu, Xiangli Dong, and Li Shi. "Short-term power load forecasting based on I-GWO-KELM algorithm." MATEC Web of Conferences 336 (2021): 05021. http://dx.doi.org/10.1051/matecconf/202133605021.
Повний текст джерелаАнотація:
In this paper, I-GWO-KELM algorithm is used for short-term power load forecasting. Normalize the power data and meteorological data of the short-term power load, and use GWO to optimize the regularization coefficient of KELM and the RBF kernel parameters. To apply the model to short-term power load forecasting to obtain simulations for the next 24 hours and 168 hours curve. Experiments show that the improved model I3-GWO-KELM proposed in this paper has the best effect. The improvement of GWO in this paper is effective and feasible. In the application of short-term power load forecasting, the IGWO-KELM model is more accurate than the ELM and KELM models.
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Bhatt, Bhargav, Himanshu Sharma, Krishan Arora, Gyanendra Prasad Joshi, and Bhanu Shrestha. "Levy Flight-Based Improved Grey Wolf Optimization: A Solution for Various Engineering Problems." Mathematics 11, no. 7 (April 5, 2023): 1745. http://dx.doi.org/10.3390/math11071745.
Повний текст джерелаАнотація:
Optimization is a broad field for researchers to develop new algorithms for solving various types of problems. There are various popular techniques being worked on for improvement. Grey wolf optimization (GWO) is one such algorithm because it is efficient, simple to use, and easy to implement. However, GWO has several drawbacks as it is stuck in local optima, has a low convergence rate, and has poor exploration. Several attempts have been made recently to overcome these drawbacks. This paper discusses some strategies that can be applied to GWO to overcome its drawbacks. This article proposes a novel algorithm to enhance the convergence rate, which was poor in GWO, and it is also compared with the other optimization algorithms. GWO also has the limitation of becoming stuck in local optima when used in complex functions or in a large search space, so these issues are further addressed. The most remarkable factor is that GWO purely depends on the initialization constraints such as population size and wolf initial positions. This study demonstrates the improved position of the wolf by applying strategies with the same population size. As a result, this novel algorithm has enhanced its exploration capability compared to other algorithms presented, and statistical results are also presented to demonstrate its superiority.
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Mohammedi, Ridha Djamel, Abdellah Kouzou, Mustafa Mosbah, Aissa Souli, Jose Rodriguez, and Mohamed Abdelrahem. "Allocation and Sizing of DSTATCOM with Renewable Energy Systems and Load Uncertainty Using Enhanced Gray Wolf Optimization." Applied Sciences 14, no. 2 (January 9, 2024): 556. http://dx.doi.org/10.3390/app14020556.
Повний текст джерелаАнотація:
Over the last decade, flexible alternating current transmission systems (FACTS) have been crucial in ensuring optimal power distribution within modern power systems. A vital component of FACTS devices is the distribution static compensator (DSTATCOM), which is essential for maintaining a reliable power supply. It is commonly used for reactive power compensation, voltage regulation, and harmonic reduction. Determining the appropriate size and placement of DSTATCOMs is vital to ensuring their efficiency. This study introduces the improved gray wolf optimizer (I-GWO), a refined version of the classical gray wolf optimization (GWO) method. The I-GWO incorporates a dimension learning-based hunting (DLH) strategy to preserve population diversity, balance exploration and exploitation, and prevent the premature convergence of classical GWO. In this research, the I-GWO was applied to determine the optimum allocation and sizing of the DSTATCOMs, considering system constraints, including those presented by the intermittent and stochastic nature of the load and renewable energy resources, specifically wind and solar energy. The suggested approach was successfully tested on 33-, 69-, and 85-bus distribution systems and then compared with existing studies. The results demonstrated the I-GWO-based approach’s superiority in terms of reducing power losses, improving voltage profiles, and enhancing voltage stability.
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Surendra Yadav, Zubair Ahmad Mir,. "Leveraging Gray Wolf Optimization for Enhanced Security Management in Wireless Sensor Network." Tuijin Jishu/Journal of Propulsion Technology 44, no. 4 (October 16, 2023): 6295–302. http://dx.doi.org/10.52783/tjjpt.v44.i4.2142.
Повний текст джерелаАнотація:
In the realm of enhancing security management systems within Wireless Sensor Network (WSNs) for Big Data applications, the role of optimization algorithms is pivotal. This chapter delves into the utilization of Gray Wolf Optimization (GWO) as a fundamental tool to achieve efficient resource allocation, node placement optimization, and the enhancement of data transmission efficiency within WSNs. A detailed exploration of the mathematical foundations of GWO is provided to foster a comprehensive understanding of its application in security enhancement.
In this paper Gray Wolf Optimization (GWO) is a nature-inspired metaheuristic algorithm presented that draws inspiration from the social behavior of gray wolves in the wild. In our study, GWO is employed to optimize various parameters that significantly impact the efficiency and effectiveness of WSNs.
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Mohammed, Hardi M., Zrar Kh Abdul, Tarik A. Rashid, Abeer Alsadoon, and Nebojsa Bacanin. "A new K-means grey wolf algorithm for engineering problems." World Journal of Engineering 18, no. 4 (March 1, 2021): 630–38. http://dx.doi.org/10.1108/wje-10-2020-0527.
Повний текст джерелаАнотація:
Purpose This paper aims at studying meta-heuristic algorithms. One of the common meta-heuristic optimization algorithms is called grey wolf optimization (GWO). The key aim is to enhance the limitations of the wolves’ searching process of attacking gray wolves. Design/methodology/approach The development of meta-heuristic algorithms has increased by researchers to use them extensively in the field of business, science and engineering. In this paper, the K-means clustering algorithm is used to enhance the performance of the original GWO; the new algorithm is called K-means clustering gray wolf optimization (KMGWO). Findings Results illustrate the efficiency of KMGWO against to the GWO. To evaluate the performance of the KMGWO, KMGWO applied to solve CEC2019 benchmark test functions. Originality/value Results prove that KMGWO is superior to GWO. KMGWO is also compared to cat swarm optimization (CSO), whale optimization algorithm-bat algorithm (WOA-BAT), WOA and GWO so KMGWO achieved the first rank in terms of performance. In addition, the KMGWO is used to solve a classical engineering problem and it is superior.
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Liu, Yizhe, Yu Jiang, Xin Zhang, Yong Pan, and Yingquan Qi. "Combined Grey Wolf Optimizer Algorithm and Corrected Gaussian Diffusion Model in Source Term Estimation." Processes 10, no. 7 (June 22, 2022): 1238. http://dx.doi.org/10.3390/pr10071238.
Повний текст джерелаАнотація:
It is extremely critical for an emergency response to quickly and accurately use source term estimation (STE) in the event of hazardous gas leakage. To determine the appropriate algorithm, four swarm intelligence optimization (SIO) algorithms including Gray Wolf optimizer (GWO), particle swarm optimization (PSO), genetic algorithm (GA) and ant colony optimization (ACO) are selected to be applied in STE. After calculation, all four algorithms can obtain leak source parameters. Among them, GWO and GA have similar computational efficiency, while ACO is computationally inefficient. Compared with GWO, GA and PSO, ACO requires larger population and more iterations to ensure accuracy of source parameters. Most notably, the convergence factor of GWO is self-adaptive, which is in favor of obtaining accurate results with lower population and iterations. On this basis, combination of GWO and a modified Gaussian diffusion model with surface correction factor is used to estimate the emission source term in this work. The calculation results demonstrate that the corrected Gaussian plume model can improve the accuracy of STE, which is promising for application in emergency warning and safety monitoring.
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Chen, Yingyu, Shenhua Yang, Yongfeng Suo, and Minjie Zheng. "Ship Track Prediction Based on DLGWO-SVR." Scientific Programming 2021 (September 14, 2021): 1–14. http://dx.doi.org/10.1155/2021/9085617.
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To improve the accuracy of ship track prediction, the improved Grey Wolf Optimizer (GWO) and Support Vector Regression (SVR) models are incorporated for ship track prediction. The hunting strategy of dimensional learning was used to optimize the move search process of GWO and balance exploration and exploitation while maintaining population diversity. Selection and updating procedures keep GWO from being stuck in locally optimal solutions. The optimal parameters obtained by modified GWO were substituted into the SVR model to predict ship trajectory. Dimension Learning Grey Wolf Optimizer and Support Vector Regression (DLGWO-SVR), Grey Wolf Optimized Support Vector Regression (GWO-SVR), and Differential Evolution Grey Wolf Optimized Support Vector Regression (DEGWO-SVR) model trajectory prediction simulations were carried out. A comparison of the results shows that the trajectory prediction model based on DLGWO-SVR has higher prediction accuracy and meets the requirements of ship track prediction. The results of ship track prediction can not only improve the efficiency of marine traffic management but also prevent the occurrence of traffic accidents and maintain marine safety.
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Şahin, İsmail, Murat Dörterler, and Harun Gokce. "Optimization of Hydrostatic Thrust Bearing Using Enhanced Grey Wolf Optimizer." Mechanics 25, no. 6 (December 4, 2019): 480–86. http://dx.doi.org/10.5755/j01.mech.25.6.22512.
Повний текст джерелаАнотація:
The need for precise mechanical and tribological properties of the hydrostatic bearings has made them an interesting study topic for optimisation studies. In this paper, power-loss minimization problems of hydrostatic thrust bearings were solved through Grey Wolf Optimizer (GWO). Grey Wolf Optimizer is a meta-heuristic optimization method standing out with its successful applications in engineering design problems. Power-loss minimization problem of hydrostatic thrust bearings was applied on Grey Wolf Optimizer (GWO) for the first time. The results obtained were evaluated together with the previous studies conducted and a detailed comparison was made. The most significant innovation of the study is the innovation made in the mathematical model of the GWO. A new model (Enhanced GWO, EGWO) that increases the variety of valid solutions is proposed. The comparisons made both with GWO and other studies in the literature show that EGWO got the known best fitness value with the highest success rate. The consistency and statistical performance of the EGWO show that this method can be used in the optimization of machine elements.
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Sule, Aliyu Hamza, Ahmad Safawi Mokhtar, Jasrul Jamani Bin Jamian, Attaullah Khidrani, and Raja Masood Larik. "Optimal tuning of proportional integral controller for fixed-speed wind turbine using grey wolf optimizer." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 5 (October 1, 2020): 5251. http://dx.doi.org/10.11591/ijece.v10i5.pp5251-5261.
Повний текст джерелаАнотація:
The need for tuning the PI controller is to improve its performance metrics such as rise time, settling time and overshoot. This paper proposed the Grey Wolf Optimizer (GWO) tuning method of a Proportional Integral (PI) controller for fixed speed Wind Turbine. The objective is to overcome the limitations in using the PSO and GA tuning methods for tuning the PI controller, such as quick convergence occurring too soon into a local optimum, and the controller step input response. The GWO, the Particle Swarm Optimization (PSO), and the Genetic Algorithm (GA) tuning methods were implemented in the Matlab 2016b to search the optimal gains of the Proportional and Integral controller through minimization of the objective function. A comparison was made between the results obtained from the GWO tuning method against PSO and GA tuning techniques. The GWO computed the smallest value of the objective function minimized. It exhibited faster convergence and better time response specification compared to other methods. These and more performance indicators show the superiority of the GWO tuning method.