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P., Jose* P. Arumugam. "ENHANCED GREY WOLF OPTIMIZER FOR MEDICAL DATASET." Global Journal of Engineering Science and Research Management 4, no. 9 (2017): 65–73. https://doi.org/10.5281/zenodo.886921.
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High dimensional data classification becomes challenging task because data are large, complex to handle, heterogeneous and hierarchical. In order to reduce the data set without affecting the classifier accuracy. The feature selection plays a vital role in large datasets and which increases the efficiency of classification to choose the important features for high dimensional classification, when those features are irrelevant or correlated. Therefore feature selection is considered to use in preprocessing before applying classifier to a data set. Thus this good choice of feature selection leads to the high classification accuracy and minimize computational cost. Though different kinds of feature selection methods are investigate for selecting and fitting features, the best algorithm should be preferred to maximize the accuracy of the classification. The proposed Hybrid kernel Improved Support Vector Machine (HISVM) classifier is used to train the parameters and optimized using Enhanced Grey wolf Optimization (EGWO). The Novel approach aimed to select minimum number of features and providing high classification accuracy.
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Alzaghoul, Esra F., and Sandi N. Fakhouri. "Collaborative Strategy for Grey Wolf Optimization Algorithm." Modern Applied Science 12, no. 7 (2018): 73. http://dx.doi.org/10.5539/mas.v12n7p73.
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Grey wolf Optimizer (GWO) is one of the well known meta-heuristic algorithm for determining the minimum value among a set of values. In this paper, we proposed a novel optimization algorithm called collaborative strategy for grey wolf optimizer (CSGWO). This algorithm enhances the behaviour of GWO that enhances the search feature to search for more points in the search space, whereas more groups will search for the global minimal points. The algorithm has been tested on 23 well-known benchmark functions and the results are verified by comparing them with state of the art algorithms: Polar particle swarm optimizer, sine cosine Algorithm (SCA), multi-verse optimizer (MVO), supernova optimizer as well as particle swarm optimizer (PSO). The results show that the proposed algorithm enhanced GWO behaviour for reaching the best solution and showed competitive results that outperformed the compared meta-heuristics over the tested benchmarked functions.
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Kitonyi, Peter Mule, and Davies Rene Segera. "Hybrid Gradient Descent Grey Wolf Optimizer for Optimal Feature Selection." BioMed Research International 2021 (August 28, 2021): 1–33. http://dx.doi.org/10.1155/2021/2555622.
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Feature selection is the process of decreasing the number of features in a dataset by removing redundant, irrelevant, and randomly class-corrected data features. By applying feature selection on large and highly dimensional datasets, the redundant features are removed, reducing the complexity of the data and reducing training time. The objective of this paper was to design an optimizer that combines the well-known metaheuristic population-based optimizer, the grey wolf algorithm, and the gradient descent algorithm and test it for applications in feature selection problems. The proposed algorithm was first compared against the original grey wolf algorithm in 23 continuous test functions. The proposed optimizer was altered for feature selection, and 3 binary implementations were developed with final implementation compared against the two implementations of the binary grey wolf optimizer and binary grey wolf particle swarm optimizer on 6 medical datasets from the UCI machine learning repository, on metrics such as accuracy, size of feature subsets, F -measure, accuracy, precision, and sensitivity. The proposed optimizer outperformed the three other optimizers in 3 of the 6 datasets in average metrics. The proposed optimizer showed promise in its capability to balance the two objectives in feature selection and could be further enhanced.
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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 (2019): 480–86. http://dx.doi.org/10.5755/j01.mech.25.6.22512.
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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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Yue, Zhihang, Sen Zhang, and Wendong Xiao. "A Novel Hybrid Algorithm Based on Grey Wolf Optimizer and Fireworks Algorithm." Sensors 20, no. 7 (2020): 2147. http://dx.doi.org/10.3390/s20072147.
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Grey wolf optimizer (GWO) is a meta-heuristic algorithm inspired by the hierarchy of grey wolves (Canis lupus). Fireworks algorithm (FWA) is a nature-inspired optimization method mimicking the explosion process of fireworks for optimization problems. Both of them have a strong optimal search capability. However, in some cases, GWO converges to the local optimum and FWA converges slowly. In this paper, a new hybrid algorithm (named as FWGWO) is proposed, which fuses the advantages of these two algorithms to achieve global optima effectively. The proposed algorithm combines the exploration ability of the fireworks algorithm with the exploitation ability of the grey wolf optimizer (GWO) by setting a balance coefficient. In order to test the competence of the proposed hybrid FWGWO, 16 well-known benchmark functions having a wide range of dimensions and varied complexities are used in this paper. The results of the proposed FWGWO are compared to nine other algorithms, including the standard FWA, the native GWO, enhanced grey wolf optimizer (EGWO), and augmented grey wolf optimizer (AGWO). The experimental results show that the FWGWO effectively improves the global optimal search capability and convergence speed of the GWO and FWA.
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Tang, Yongli, Zhenlun Gao, Zhongqi Cai, Jinxia Yu, and Panke Qin. "Enhanced futures price-spread forecasting based on an attention-driven optimized LSTM network: integrating an improved grey wolf optimizer algorithm for enhanced accuracy." PeerJ Computer Science 11 (June 2, 2025): e2865. https://doi.org/10.7717/peerj-cs.2865.
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Financial market prediction faces significant challenges due to the complex temporal dependencies and heterogeneous data relationships inherent in futures price-spread data. Traditional machine learning methods struggle to effectively mine these patterns, while conventional long short-term memory (LSTM) models lack focused feature prioritization and suffer from suboptimal hyperparameter selection. This article proposes the Improved Grey Wolf Optimizer with Multi-headed Self-attention and LSTM (IGML) model, which integrates a multi-head self-attention mechanism to enhance feature interaction and introduces an improved grey wolf optimizer (IGWO) with four strategic enhancements for automated hyperparameter tuning. Benchmark tests on optimization problems validate IGWO’s superior convergence efficiency. Evaluated on real futures price-spread datasets, the IGML reduces mean square error (RMSE) and mean absolute error (MAE) by up to 88% and 85%, respectively, compared to baseline models, demonstrating its practical efficacy in capturing intricate financial market dynamics.
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El-ashry, Asmaa M., Mohammed F. Alrahmawy, and Magdi Z. Rashad. "Enhanced Quantum Inspired Grey Wolf Optimizer for Feature Selection." International Journal of Intelligent Systems and Applications 12, no. 3 (2020): 8–17. http://dx.doi.org/10.5815/ijisa.2020.03.02.
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Ozule, Chukwuka Prosper, Adeyinka Oluwo, Nehemiah Sabinus Alozie, et al. "A grey wolf optimization approach for evaluating the engine responses of various biodiesel blends in an internal combustion engine." Kufa Journal of Engineering 16, no. 1 (2025): 298–323. https://doi.org/10.30572/2018/kje/160118.
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The knowledge of the exact thresholds of parameters in the diesel engines, during combustion, is essential to simulate the combustion process, establish parametric values, reduce cost and predict exhaust emissions. Accordingly, the present paper applies the grey wolf optimization method to determine the optimal threshold of parameters and engine responses in a direct ignition engine. Twelve formulated linear equations of engine responses are introduced to the objective function of the grey wolf optimizer. A computer program in C++ was applied successfully using literature data to validate the grey wolf optimization procedure based on the encircling, hunting and attacking of prey by the wolf. The results show that load demand and turbocharge boast air pressure have the least and highest values of engine outputs, respectively. The blend ratio had its highest values when optimized alongside the main injection duration. The responses and parameters greatly improved from initial values to stopping criterion of 200 iterations. Instances reported include brake specific fuel consumption, which improved from 2.6468 to 1.0816 g/kWhr, blend ratio changes from 0.5031 to 0.4760%, speed drop from 0.0031 to 0.0010rpm, and load drop from 0.0017 to 0.0010%. The main contribution of this paper is to establish the optimal thresholds of engine responses using the grey wolf optimizer in a diesel engine combustion chamber. The development of a new method to optimize response and parameters of an internal combustion process using grey wolf optimizer is the novel aspect of this work. The results have essential practical significance to establish new emission profile for biodiesel. The practising engineers and researchers have a holistic insight into the problem’s solution and can utilize the results to enhance their engine responses.
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Zhang, Runze, and Yujie Zhu. "Predicting the Mechanical Properties of Heat-Treated Woods Using Optimization-Algorithm-Based BPNN." Forests 14, no. 5 (2023): 935. http://dx.doi.org/10.3390/f14050935.
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This paper aims to enhance the accuracy of predicting the mechanical behavior of wood subjected to thermal modification using an improved dung beetle optimization (IDBO) model. The IDBO algorithm improves the original DBO algorithm via three main steps: (1) using piece-wise linear chaotic mapping (PWLCM) to generate the initial dung beetle species and increase its heterogeneity; (2) adopting an adaptive nonlinear decreasing producer ratio model to control the number of producers and boost the algorithm’s convergence rate; and (3) applying a dimensional learning-enhanced foraging (DLF) search strategy that optimizes the algorithm’s ability to explore and exploit the search space. The IDBO algorithm is evaluated on 14 benchmark functions and outperforms other algorithms. The IDBO algorithm is then applied to optimize a back-propagation (BP) neural network for predicting five mechanical property parameters of heat-treated larch-sawn timber. The results indicate that the IDBO-BP model significantly reduces the error compared with the BP, tent-sparrow search algorithm (TSSA)-BP, grey wolf optimizer (GWO)-BP, nonlinear adaptive grouping grey wolf optimizer (IGWO)-BP and DBO-BP models, demonstrating its superiority in predicting the physical characteristics of lumber after heat treatment.
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Gupta, Shubham, and Kusum Deep. "Enhanced leadership-inspired grey wolf optimizer for global optimization problems." Engineering with Computers 36, no. 4 (2019): 1777–800. http://dx.doi.org/10.1007/s00366-019-00795-0.
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Rezaei, Farshad, Hamid Reza Safavi, Mohamed Abd Elaziz, Shaker H. Ali El-Sappagh, Mohammed Azmi Al-Betar, and Tamer Abuhmed. "An Enhanced Grey Wolf Optimizer with a Velocity-Aided Global Search Mechanism." Mathematics 10, no. 3 (2022): 351. http://dx.doi.org/10.3390/math10030351.
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This paper proposes a novel variant of the Grey Wolf Optimization (GWO) algorithm, named Velocity-Aided Grey Wolf Optimizer (VAGWO). The original GWO lacks a velocity term in its position-updating procedure, and this is the main factor weakening the exploration capability of this algorithm. In VAGWO, this term is carefully set and incorporated into the updating formula of the GWO. Furthermore, both the exploration and exploitation capabilities of the GWO are enhanced in VAGWO via stressing the enlargement of steps that each leading wolf takes towards the others in the early iterations while stressing the reduction in these steps when approaching the later iterations. The VAGWO is compared with a set of popular and newly proposed meta-heuristic optimization algorithms through its implementation on a set of 13 high-dimensional shifted standard benchmark functions as well as 10 complex composition functions derived from the CEC2017 test suite and three engineering problems. The complexity of the proposed algorithm is also evaluated against the original GWO. The results indicate that the VAGWO is a computationally efficient algorithm, generating highly accurate results when employed to optimize high-dimensional and complex problems.
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Tripathi, Ashish Kumar, Kapil Sharma, and Manju Bala. "A Novel Clustering Method Using Enhanced Grey Wolf Optimizer and MapReduce." Big Data Research 14 (December 2018): 93–100. http://dx.doi.org/10.1016/j.bdr.2018.05.002.
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Pambudi, Elindra Ambar, Abid Yanuar Badharudin, and Agung Purwo Wicaksono. "ENHANCED K-MEANS BY USING GREY WOLF OPTIMIZER FOR BRAIN MRI SEGMENTATION." ICTACT Journal on Soft Computing 11, no. 3 (2021): 2353–58. http://dx.doi.org/10.21917/ijsc.2021.0336.
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Segmentation is an essential part of the detection and classification series. The best result of brain MRI detection was followed by the best segmentation process. Supporting brain MRI detection accurately, one of the ways could be used by increasing segmentation. This paper utilizes one of the segmentation methods which is called clustering. We propose a clustering approach using K-Means. K-Means has advantages easy to understand, fast process, and guarantees convergence. But it has drawbacks which are initialization cluster center randomly, sometimes it is given good results but sometimes it is not. Therefore, this research proposes to optimize the weak side of K-Means using a grey wolf optimizer. Initialization cluster center was chosen based on fitness value. The fitness value of this paper is Sum Square Error (SSE), we purpose to minimize the SSE of the population and searching new positions depend on Gray Wolf Optimization (GWO)’s rule. The final position of GWO would be initialized by K-Means. The series of our research steps are acquisition image, grayscaling, resizing, segmentation, and analysis performance based on MSE and PSNR. The best result of the purposed method is k=17 which PSNR (16.09) and MSE (15.99). GWO K-Means were given the best outcome segmentation brain MRI based on measuring error value and PSNR.
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Zhou, Xingyu, Guoqing Shi, and Jiandong Zhang. "Improved Grey Wolf Algorithm: A Method for UAV Path Planning." Drones 8, no. 11 (2024): 675. http://dx.doi.org/10.3390/drones8110675.
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The Grey Wolf Optimizer (GWO) algorithm is recognized for its simplicity and ease of implementation, and has become a preferred method for solving global optimization problems due to its adaptability and search capabilities. Despite these advantages, existing Unmanned Aerial Vehicle (UAV) path planning algorithms are often hindered by slow convergence rates, susceptibility to local optima, and limited robustness. To surpass these limitations, we enhance the application of GWO in UAV path planning by improving its trajectory evaluation function, convergence factor, and position update method. We propose a collaborative UAV path planning model that includes constraint analysis and an evaluation function. Subsequently, an Enhanced Grey Wolf Optimizer model (NI–GWO) is introduced, which optimizes the convergence coefficient using a nonlinear function and integrates the Dynamic Window Approach (DWA) algorithm into the model based on the fitness of individual wolves, enabling it to perform dynamic obstacle avoidance tasks. In the final stage, a UAV path planning simulation platform is employed to evaluate and compare the effectiveness of the original and improved algorithms. Simulation results demonstrate that the proposed NI–GWO algorithm can effectively solve the path planning problem for UAVs in uncertain environments. Compared to Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), GWO, and MP–GWO algorithms, the NI–GWO algorithm can achieve the optimal fitness value and has significant advantages in terms of average path length, time, number of collisions, and obstacle avoidance capabilities.
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Shen, Jiazheng, Tang Sai Hong, Luxin Fan, Ruixin Zhao, Mohd Khairol Anuar b. Mohd Ariffin, and Azizan bin As’arry. "Development of an Improved GWO Algorithm for Solving Optimal Paths in Complex Vertical Farms with Multi-Robot Multi-Tasking." Agriculture 14, no. 8 (2024): 1372. http://dx.doi.org/10.3390/agriculture14081372.
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As the global population grows, achieving Zero Hunger by 2030 presents a significant challenge. Vertical farming technology offers a potential solution, making the path planning of agricultural robots in vertical farms a research priority. This study introduces the Vertical Farming System Multi-Robot Trajectory Planning (VFSMRTP) model. To optimize this model, we propose the Elitist Preservation Differential Evolution Grey Wolf Optimizer (EPDE-GWO), an enhanced version of the Grey Wolf Optimizer (GWO) incorporating elite preservation and differential evolution. The EPDE-GWO algorithm is compared with Genetic Algorithm (GA), Simulated Annealing (SA), Dung Beetle Optimizer (DBO), and Particle Swarm Optimization (PSO). The experimental results demonstrate that EPDE-GWO reduces path length by 24.6%, prevents premature convergence, and exhibits strong global search capabilities. Thanks to the DE and EP strategies, the EPDE-GWO requires fewer iterations to reach the optimal solution, offers strong stability and robustness, and consistently finds the optimal solution at a high frequency. These attributes are particularly significant in the context of vertical farming, where optimizing robotic path planning is essential for maximizing operational efficiency, reducing energy consumption, and improving the scalability of farming operations.
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Wang, Yang, Chengyu Jin, Qiang Li, et al. "A Dynamic Opposite Learning-Assisted Grey Wolf Optimizer." Symmetry 14, no. 9 (2022): 1871. http://dx.doi.org/10.3390/sym14091871.
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The grey wolf optimization (GWO) algorithm is widely utilized in many global optimization applications. In this paper, a dynamic opposite learning-assisted grey wolf optimizer (DOLGWO) was proposed to improve the search ability. Herein, a dynamic opposite learning (DOL) strategy is adopted, which has an asymmetric search space and can adjust with a random opposite point to enhance the exploitation and exploration capabilities. To validate the performance of DOLGWO algorithm, 23 benchmark functions from CEC2014 were adopted in the numerical experiments. A total of 10 popular algorithms, including GWO, TLBO, PIO, Jaya, CFPSO, CFWPSO, ETLBO, CTLBO, NTLBO and DOLJaya were used to make comparisons with DOLGWO algorithm. Results indicate that the new model has strong robustness and adaptability, and has the significant advantage of converging to the global optimum, which demonstrates that the DOL strategy greatly improves the performance of original GWO algorithm.
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Wang, Zhendong, Huamao Xie, Zhongdong Hu, Dahai Li, Junling Wang, and Wen Liang. "Node coverage optimization algorithm for wireless sensor networks based on improved grey wolf optimizer." Journal of Algorithms & Computational Technology 13 (January 2019): 174830261988949. http://dx.doi.org/10.1177/1748302619889498.
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Aiming at the problem of wireless sensor network node coverage optimization with obstacles in the monitoring area, based on the grey wolf optimizer algorithm, this paper proposes an improved grey wolf optimizer (IGWO) algorithm to improve the shortcomings of slow convergence, low search precision, and easy to fall into local optimum. Firstly, the nonlinear convergence factor is designed to balance the relationship between global search and local search. The elite strategy is introduced to protect the excellent individuals from being destroyed as the iteration proceeds. The original weighting strategy is improved, so that the leading wolf can guide the remaining grey wolves to prey in a more reasonable way. The design of the grey wolf’s boundary position strategy and the introduction of dynamic variation strategy enrich the population diversity and enhance the ability of the algorithm to jump out of local optimum. Then, the benchmark function is used to test the convergence performance of genetic algorithm, particle swarm optimization, grey wolf optimizer, and IGWO algorithm, which proves that the convergence performance of IGWO algorithm is better than the other three algorithms. Finally, the IGWO algorithm is applied to the deployment of wireless sensor networks with obstacles (rectangular obstacle, trapezoidal obstacle and triangular obstacles). Simulation results show that compared with GWO algorithm, IGWO algorithm can effectively improve the coverage of wireless sensor network nodes and obtain higher coverage rate with fewer nodes, thereby reducing the cost of deploying the network.
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Long, Wen, Jianjun Jiao, Ximing Liang, and Mingzhu Tang. "An exploration-enhanced grey wolf optimizer to solve high-dimensional numerical optimization." Engineering Applications of Artificial Intelligence 68 (February 2018): 63–80. http://dx.doi.org/10.1016/j.engappai.2017.10.024.
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Ma, Mingshang. "Optimization of Speed Reducer Design based on an Enhanced Grey Wolf Optimizer." Information Technology and Control 54, no. 1 (2025): 268–89. https://doi.org/10.5755/j01.itc.54.1.36908.
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Traditional swarm intelligence optimization methods perform erratically in engineering design due to difficulties in handling nonlinear data, local optimal errors and premature convergence. To address these problems, we developed an enhanced Gray Wolf Optimizer (OGWO) that employs Levy flight and elite adversarial-based learning methods. We evaluated its effectiveness using 20 benchmark functions and compared it with other GWO variants and popular algorithms. The results show that OGWO is superior in terms of convergence speed, accuracy, and freedom from stagnation, as confirmed by the Wilcoxon rank sum test. Furthermore, the effectiveness of OGWO in training Multilayer Perceptron (MLP) has been evaluated using the UCL datasets. Finally, OGWO has been applied to solve the gearbox design problem, proving its ability to provide optimal solutions in addressing real-life engineering issues.
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Chen, Chengcheng, Xianchang Wang, Helong Yu, Nannan Zhao, Mingjing Wang, and Huiling Chen. "An Enhanced Comprehensive Learning Particle Swarm Optimizer with the Elite-Based Dominance Scheme." Complexity 2020 (October 19, 2020): 1–24. http://dx.doi.org/10.1155/2020/4968063.
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In recent years, swarm-based stochastic optimizers have achieved remarkable results in tackling real-life problems in engineering and data science. When it comes to the particle swarm optimization (PSO), the comprehensive learning PSO (CLPSO) is a well-established evolutionary algorithm that introduces a comprehensive learning strategy (CLS), which effectively boosts the efficacy of the PSO. However, when the single modal function is processed, the convergence speed of the algorithm is too slow to converge quickly to the optimum during optimization. In this paper, the elite-based dominance scheme of another well-established method, grey wolf optimizer (GWO), is introduced into the CLPSO, and the grey wolf local enhanced comprehensive learning PSO algorithm (GCLPSO) is proposed. Thanks to the exploitative trends of the GWO, the algorithm improves the local search capacity of the CLPSO. The new variant is compared with 15 representative and advanced algorithms on IEEE CEC2017 benchmarks. Experimental outcomes have shown that the improved algorithm outperforms other comparison competitors when coping with four different kinds of functions. Moreover, the algorithm is favorably utilized in feature selection and three constrained engineering construction problems. Simulations have shown that the GCLPSO is capable of effectively dealing with constrained problems and solves the problems encountered in actual production.
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Karnavas, Yannis L., Ioannis D. Chasiotis, and Emmanouil L. Peponakis. "Permanent Magnet Synchronous Motor Design using Grey Wolf Optimizer Algorithm." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 3 (2016): 1353. http://dx.doi.org/10.11591/ijece.v6i3.9771.
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Common high-torque low-speed motor drive schemes combine an induction motor coupled to the load by a mechanical subsystem which consists of gears, belt/pulleys or camshafts. Consequently, these setups present an inherent drawback regarding to maintenance needs, high costs and overall system deficiency. Thus, the replacement of such a conventional drive with a properly designed low speed permanent magnet synchronous motor (PMSM) directly coupled to the load, provides an attractive alternative. In this context, the paper deals with the design evaluation of a 5kW/50rpm radial flux PMSM with surface-mounted permanent magnets and inner rotor topology. Since the main goal is the minimization of the machine's total losses and therefore the maximization of its efficiency, the design is conducted by solving an optimization problem. For this purpose, the application of a new meta-heuristic optimization method called “<em>Grey Wolf Optimizer</em>” is studied. The effectiveness of the method in finding appropriate PMSM designs is then evaluated. The obtained results of the applied method reveal satisfactorily enhanced design solutions and performance when compared with those of other optimization techniques.
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Karnavas, Yannis L., Ioannis D. Chasiotis, and Emmanouil L. Peponakis. "Permanent Magnet Synchronous Motor Design using Grey Wolf Optimizer Algorithm." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 3 (2016): 1353. http://dx.doi.org/10.11591/ijece.v6i3.pp1353-1362.
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Common high-torque low-speed motor drive schemes combine an induction motor coupled to the load by a mechanical subsystem which consists of gears, belt/pulleys or camshafts. Consequently, these setups present an inherent drawback regarding to maintenance needs, high costs and overall system deficiency. Thus, the replacement of such a conventional drive with a properly designed low speed permanent magnet synchronous motor (PMSM) directly coupled to the load, provides an attractive alternative. In this context, the paper deals with the design evaluation of a 5kW/50rpm radial flux PMSM with surface-mounted permanent magnets and inner rotor topology. Since the main goal is the minimization of the machine's total losses and therefore the maximization of its efficiency, the design is conducted by solving an optimization problem. For this purpose, the application of a new meta-heuristic optimization method called “<em>Grey Wolf Optimizer</em>” is studied. The effectiveness of the method in finding appropriate PMSM designs is then evaluated. The obtained results of the applied method reveal satisfactorily enhanced design solutions and performance when compared with those of other optimization techniques.
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Thaher, Thaer, Mohammed Awad, Mohammed Aldasht, Alaa Sheta, Hamza Turabieh, and Hamouda Chantar. "An Enhanced Evolutionary Based Feature Selection Approach Using Grey Wolf Optimizer for the Classification of High-dimensional Biological Data." JUCS - Journal of Universal Computer Science 28, no. (5) (2022): 499–539. https://doi.org/10.3897/jucs.78218.
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Feature selection (FS) is a pre-processing step that aims to eliminate the redundant and less-informative features to enhance the performance of data mining techniques. It is also considered as one of the key success factors for classification problems in high-dimensional datasets. This paper proposes an efficient wrapper feature selection method based on Grey Wolf Optimizer (GWO). GWO is a recent metaheuristic algorithm that has been widely employed to solve diverse optimization problems. However, GWO mainly follows the search directions toward the leading wolves, making it prone to fall into local optima, especially when dealing with high-dimensional problems, which is the case when dealing with many biological datasets. An enhanced variation of GWO called EGWO, which adapts two enhancements, is introduced to overcome this specific shortcoming. In the first place, the transition parameter concept is incorporated to move GWO from the exploration phase to the exploitation phase. Several adaptive non-linear decreasing formulas are introduced to control the transition parameters. In the second place, a random-based search strategy is exploited to empower diversity during the search process. Two binarization schemes using S-shaped and V-shaped transfer functions are incorporated to map the continuous search space into a binary one for FS. The efficiency of the proposed EGWO is validated on ten high-dimensional low-samples biological data. Our experiments show the promising performance of EGWO compared to the original GWO approach and other state-of-the-art techniques in terms of dimensionality reduction and the enhancement of classification performance.
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Chen, Wenwei, Lisang Liu, Liwei Zhang, Zhihui Lin, Jian Chen, and Dongwei He. "Path Planning of Mobile Robots with an Improved Grey Wolf Optimizer and Dynamic Window Approach." Applied Sciences 15, no. 7 (2025): 3999. https://doi.org/10.3390/app15073999.
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To address the critical limitations of conventional Grey Wolf Optimization (GWO) in path planning scenarios—including insufficient exploration capability during the initial phase, proneness to local optima entrapment, and inherent deficiency in dynamic obstacle avoidance—this paper proposes a multi-strategy enhanced GWO algorithm. Firstly, the Piecewise chaotic mapping is applied to initialize the Grey Wolf population, enhancing the initial population quality. Secondly, the linear convergence factor is modified to a nonlinear one to balance the algorithm’s global and local search capabilities. Thirdly, Evolutionary Population Dynamics (EPD) is incorporated to enhance the algorithm’s ability to escape local optima, and dynamic weights are used to improve convergence speed and accuracy. Finally, the algorithm is integrated with the Improved Dynamic Window Approach (IDWA) to enhance path smoothness and perform dynamic obstacle avoidance. The proposed algorithm is named PAGWO-IDWA. The results demonstrate that, compared to traditional GWO, PAGWO-IDWA reduces the path length, number of turns, and running time by 9.58%, 33.16%, and 30.31%, respectively. PAGWO-IDWA not only overcomes the limitations of traditional GWO but also enables effective path planning in dynamic environments, generating paths that are both safe and smooth, thus validating the effectiveness of the algorithm.
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Vasudha Bahl and Anoop Kumar. "Probabilistic Multi-Tiered Grey Wolf Optimizer-Based Routing for Sustainable Sensor Networks." Journal of Information and Communication Technology 21, no. 4 (2022): 627–63. http://dx.doi.org/10.32890/jict2022.21.4.7.
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Wireless sensor networks (WSN) have a wide range of applications. Therefore, developing an energy-efficient methodology forestimating cluster heads (CHs) to ensure efficient data transmission has become highly relevant. Meta-heuristic strategies for optimal CHs are the current investigation inclination. As the network grows, the conventional optimization strategies emerge unsuccessful, and the outcomes of hybridizing bring performance enhancement in WSN. A Probabilistic Multi-Tiered Grey Wolf Optimizer (GWO) wasimplemented in this study on an upgraded Grey Wolf Optimizer for optimum CH selection. It used fitness value to strengthen GWO’ssearch for the best solution, resulting in even dispersal of CHs. Communication routes were updated based on routes to the CHs andbase station to lessen energy consumption by a layered-based routing scheme. GWO’s governance enhanced the network’s ability. The distributed nodes’ geographical territory was categorized into four tiers. CH was chosen grounded on the objective value that required fewer difficult control factors than existing techniques. Simulations showed that the suggested technique could extend the network’s stability time by (31.5 %) compared to hetDEEC-3, L-DDRI, Novel-LEACH-POS, DBSCDS-GWO, and P-SEP.
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Madhiarasan, M., and S. N. Deepa. "ELMAN Neural Network with Modified Grey Wolf Optimizer for Enhanced Wind Speed Forecasting." Circuits and Systems 07, no. 10 (2016): 2975–95. http://dx.doi.org/10.4236/cs.2016.710255.
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Xie, Hailun, Li Zhang, and Chee Peng Lim. "Evolving CNN-LSTM Models for Time Series Prediction Using Enhanced Grey Wolf Optimizer." IEEE Access 8 (2020): 161519–41. http://dx.doi.org/10.1109/access.2020.3021527.
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Jadhav, Amolkumar Narayan, and Gomathi N. "EKEGWO: Enhanced Kernel-Based Exponential Grey Wolf Optimizer for Bi-Objective Data Clustering." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 27, no. 04 (2019): 669–88. http://dx.doi.org/10.1142/s0218488519500296.
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The widespread application of clustering in various fields leads to the discovery of different clustering techniques in order to partition multidimensional data into separable clusters. Although there are various clustering approaches used in literature, optimized clustering techniques with multi-objective consideration are rare. This paper proposes a novel data clustering algorithm, Enhanced Kernel-based Exponential Grey Wolf Optimization (EKEGWO), handling two objectives. EKEGWO, which is the extension of KEGWO, adopts weight exponential functions to improve the searching process of clustering. Moreover, the fitness function of the algorithm includes intra-cluster distance and the inter-cluster distance as an objective to provide an optimum selection of cluster centroids. The performance of the proposed technique is evaluated by comparing with the existing approaches PSC, mPSC, GWO, and EGWO for two datasets: banknote authentication and iris. Four metrics, Mean Square Error (MSE), F-measure, rand and jaccord coefficient, estimates the clustering efficiency of the algorithm. The proposed EKEGWO algorithm can attain an MSE of 837, F-measure of 0.9657, rand coefficient of 0.8472, jaccord coefficient of 0.7812, for the banknote dataset.
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Yang, Yefeng, Bo Yang, Shilong Wang, Tianguo Jin, and Shi Li. "An enhanced multi-objective grey wolf optimizer for service composition in cloud manufacturing." Applied Soft Computing 87 (February 2020): 106003. http://dx.doi.org/10.1016/j.asoc.2019.106003.
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Wang, Zhendong, and Huamao Xie. "Wireless Sensor Network Deployment of 3D Surface Based on Enhanced Grey Wolf Optimizer." IEEE Access 8 (2020): 57229–51. http://dx.doi.org/10.1109/access.2020.2982441.
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Chandran, Vanisree, and Prabhujit Mohapatra. "Enhanced opposition-based grey wolf optimizer for global optimization and engineering design problems." Alexandria Engineering Journal 76 (August 2023): 429–67. http://dx.doi.org/10.1016/j.aej.2023.06.048.
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Trinh Thi, Trang, and Hai Nguyen Luong. "An applied grey wolf optimizer for scheduling construction projects." Transport and Communications Science Journal 73, no. 4 (2022): 397–411. http://dx.doi.org/10.47869/tcsj.73.4.5.
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Construction project delay has been reported as a significant cause of the project’s failure, which results in cost overrun, thereby decreasing the effectiveness of the project. Therefore, project management has placed much effort in construction works’ scheduling to enhance project performance. However, construction schedule has been commonly addressed within traditional methods that rely on project managers’ subjective experiences and manually-performed approaches, resulting in time-consuming and inaccurate decision-making. This study is thus aimed to handle these limitations. Using analyses of the Grey Wolf Optimizer (GWO) model, inspired by the leadership hierarchy and hunting mechanism of grey wolves in nature, this study supports reducing the construction time and minimizing the additional construction cost. Furthermore, another computational tool, namely Solver-addins, is also used to verify the reliability of the result. The findings of this study will provide a valuable tool for supporting construction management to deliver projects on time, improving construction project performance
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Bouhadouza, Boubekeur, Fares Sadaoui, and Abdelkader Boukaroura. "Performance of PSO-GWO, VAGWO, and GWO optimization techniques for economic dispatch problem: studied case of the southeast algerian electrical network." STUDIES IN ENGINEERING AND EXACT SCIENCES 5, no. 2 (2024): e10179. http://dx.doi.org/10.54021/seesv5n2-437.
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Economic dispatch (ED) aims to identify the most cost-effective strategy for allocating power generation while meeting demand and adhering to the physical constraints of the power system. In this paper, three algorithms are proposed to solve the ED problem in power systems, including a hybrid Particle Swarm Optimization with Grey Wolf Optimizer (PSO-GWO), modified Velocity Aided Grey Wolf Optimizer (VAGWO), and Grey Wolf Optimizer (GWO). PSO is a meta-heuristic optimization technique designed to find the optimal solution to a problem by guiding the movement of particles within a defined exploration space. GWO is also a meta-heuristic optimization algorithm based on the natural behavior of grey wolves. In this paper, to enhance the performance of GWO, it is first combined with the PSO method. VAGWO is employed to refine the GWO formulation by optimizing the convergence steps. At the beginning of the iterations, the distance between the solutions (wolves) is increased to promote exploration of the search space. As the iterations progress, the step size is reduced, allowing for a more precise and efficient convergence toward the optimal solution. These algorithms will be implemented in Matlab software to minimize the fuel cost production of the following test networks: IEEE 30-bus network, Algerian 114-bus network, and Southeast Algerian network. These methods show that the PSO-GWO algorithm offers better results when compared through VAGWO and GWO.
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Otair, Mohammed, Osama Talab Ibrahim, Laith Abualigah, Maryam Altalhi, and Putra Sumari. "An enhanced Grey Wolf Optimizer based Particle Swarm Optimizer for intrusion detection system in wireless sensor networks." Wireless Networks 28, no. 2 (2022): 721–44. http://dx.doi.org/10.1007/s11276-021-02866-x.
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Hudaib, Amjad A., and Ahmad Kamel AL Hwaitat. "Movement Particle Swarm Optimization Algorithm." Modern Applied Science 12, no. 1 (2017): 148. http://dx.doi.org/10.5539/mas.v12n1p148.
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Particle Swarm Optimization (PSO) ia a will known meta-heuristic that has been used in many applications for solving optimization problems. But it has some problems such as local minima. In this paper proposed a optimization algorithm called Movement Particle Swarm Optimization (MPSO) that enhances the behavior of PSO by using a random movement function to search for more points in the search space. The meta-heuristic has been experimented over 23 benchmark faction compared with state of the art algorithms: Multi-Verse Optimizer (MFO), Sine Cosine Algorithm (SCA), Grey Wolf Optimizer (GWO) and particle Swarm Optimization (PSO). The Results showed that the proposed algorithm has enhanced the PSO over the tested benchmarked functions.
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Hussien, Hussien Rezk, El-Sayed M. El-Kenawy, and Ali I. El-Desouky. "EEG Channel Selection Using A Modified Grey Wolf Optimizer." European Journal of Electrical Engineering and Computer Science 5, no. 1 (2021): 17–24. http://dx.doi.org/10.24018/ejece.2021.5.1.265.
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Consider an increasingly growing field of research, Brain-Computer Interface (BCI) is to form a direct channel of communication between a computer and the brain. However, extracting features of random time-varying EEG signals and their classification is a major challenge that faces current BCI. This paper proposes a modified grey wolf optimizer (MGWO) that can select optimal EEG channels to be used in (BCIs), the way that identifies main features and the immaterial ones from that dataset and the complexity to be removed. This allows (MGWO) to opt for optimal EEG channels as well as helping machine learning classification in its tasks when doing training to the classifier with the dataset. (MGWO), which imitates the grey wolves leadership and hunting manner nature and which consider metaheuristics swarm intelligence algorithms, is an integration with two modification to achieve the balance between exploration and exploitation the first modification applies exponential change for the number of iterations to increase search space accordingly exploitation, the second modification is the crossover operation that is used to increase the diversity of the population and enhance exploitation capability. Experimental results use four different EEG datasets BCI Competition IV- dataset 2a, BCI Competition IV- data set III, BCI Competition II data set III, and EEG Eye State from UCI Machine Learning Repository to evaluate the quality and effectiveness of the (MGWO). A cross-validation method is used to measure the stability of the (MGWO).
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Jian, Dong, Lu Jinling, Liang Wuke, Wang Wei, and Ai Gaigai. "An Improved Grey Wolf Optimizer(IGWO) algorithm for optimization of centrifugal pump with guide vane." Journal of Physics: Conference Series 2854, no. 1 (2024): 012061. http://dx.doi.org/10.1088/1742-6596/2854/1/012061.
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Abstract To improve the hydraulic performance of a centrifugal pump with guide vane, an improved grey wolf optimizer (IGWO) algorithm is proposed. First, the IGWO algorithm enhances the diversity and global exploration of the initial population with optimal Latin hypercube sampling. Then, the convergence factor is improved by combining the Tanh function to meet the needs of complex non-linear optimization problems. Finally, a search mechanism that enhances population communication is constructed and combined with a mutation-driven search scheme to improve the ability to avoids the local optima traps. The results show that IGWO algorithm has obvious advantages in convergence speed and robustness when dealing with complex non-linear optimization problems. Additionally, satisfactory results are achieved in the application of centrifugal pump optimization. The efficiency of optimized pump reaches 87.8%, which is 1.2% higher than that of the original pump. The anti-cavitation performance of the centrifugal pump is enhanced by improving the distribution of blade inlet attack angles. The vortex area inside the optimized pump impeller is reduced over a large area, and the operating stability of the pump, the matching between the impeller and the guide vane, and the flow characteristics in the guide vane domain are all improved.
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Ou, Yun, Pengfei Yin, and Liping Mo. "An Improved Grey Wolf Optimizer and Its Application in Robot Path Planning." Biomimetics 8, no. 1 (2023): 84. http://dx.doi.org/10.3390/biomimetics8010084.
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This paper discusses a hybrid grey wolf optimizer utilizing a clone selection algorithm (pGWO-CSA) to overcome the disadvantages of a standard grey wolf optimizer (GWO), such as slow convergence speed, low accuracy in the single-peak function, and easily falling into local optimum in the multi-peak function and complex problems. The modifications of the proposed pGWO-CSA could be classified into the following three aspects. Firstly, a nonlinear function is used instead of a linear function for adjusting the iterative attenuation of the convergence factor to balance exploitation and exploration automatically. Then, an optimal α wolf is designed which will not be affected by the wolves β and δ with poor fitness in the position updating strategy; the second-best β wolf is designed, which will be affected by the low fitness value of the δ wolf. Finally, the cloning and super-mutation of the clonal selection algorithm (CSA) are introduced into GWO to enhance the ability to jump out of the local optimum. In the experimental part, 15 benchmark functions are selected to perform the function optimization tasks to reveal the performance of pGWO-CSA further. Due to the statistical analysis of the obtained experimental data, the pGWO-CSA is superior to these classical swarm intelligence algorithms, GWO, and related variants. Furthermore, in order to verify the applicability of the algorithm, it was applied to the robot path-planning problem and obtained excellent results.
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Cherukuri, Santhan Kumar, and Srinivasa Rao Rayapudi. "Enhanced Grey Wolf Optimizer based MPPT Algorithm of PV system under Partial Shaded Condition." International Journal of Renewable Energy Development 6, no. 3 (2017): 203. http://dx.doi.org/10.14710/ijred.6.3.203-212.
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Partial shading condition is one of the adverse phenomena which effects the power output of photovoltaic (PV) systems due to inaccurate tracking of global maximum power point. Conventional Maximum Power Point Tracking (MPPT) techniques like Perturb and Observe, Incremental Conductance and Hill Climbing can track the maximum power point effectively under uniform shaded condition, but fails under partial shaded condition. An attractive solution under partial shaded condition is application of meta-heuristic algorithms to operate at global maximum power point. Hence in this paper, an Enhanced Grey Wolf Optimizer (EGWO) based maximum power point tracking algorithm is proposed to track the global maximum power point of PV system under partial shading condition. A Mathematical model of PV system is developed under partial shaded condition using single diode model and EGWO is applied to track global maximum power point. The proposed method is programmed in MATLAB environment and simulations are carried out on 4S and 2S2P PV configurations for dynamically changing shading patterns. The results of the proposed method are analyzed and compared with GWO and PSO algorithms. It is observed that proposed method is effective in tracking global maximum power point with more accuracy in less computation time compared to other methods.Article History: Received June 12nd 2017; Received in revised form August 13rd 2017; Accepted August 15th 2017; Available onlineHow to Cite This Article: Kumar, C.H.S and Rao, R.S. (2017 Enhanced Grey Wolf Optimizer Based MPPT Algorithm of PV System Under Partial Shaded Condition. Int. Journal of Renewable Energy Development, 6(3), 203-212.https://doi.org/10.14710/ijred.6.3.203-212
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Barraza, Juan, Luis Rodríguez, Oscar Castillo, Patricia Melin, and Fevrier Valdez. "A New Hybridization Approach between the Fireworks Algorithm and Grey Wolf Optimizer Algorithm." Journal of Optimization 2018 (May 27, 2018): 1–18. http://dx.doi.org/10.1155/2018/6495362.
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The main aim of this paper is to present a new hybridization approach for combining two powerful metaheuristics, one inspired by physics and the other one based on bioinspired phenomena. The first metaheuristic is based on physics laws and imitates the explosion of the fireworks and is called Fireworks Algorithm; the second metaheuristic is based on the behavior of the grey wolf and belongs to swarm intelligence methods, and this method is called the Grey Wolf Optimizer algorithm. For this work we studied and analyzed the advantages of the two methods and we propose to enhance the weakness of both methods, respectively, with the goal of obtaining a new hybridization between the Fireworks Algorithm (FWA) and the Grey Wolf Optimizer (GWO), which is denoted as FWA-GWO, and that is presented in more detail in this work. In addition, we are presenting simulation results on a set of problems that were tested in this paper with three different metaheuristics (FWA, GWO, and FWA-GWO) and these problems form a set of 22 benchmark functions in total. Finally, a statistical study with the goal of comparing the three different algorithms through a hypothesis test (Z-test) is presented for supporting the conclusions of this work.
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Selvaraju, P., and B. Kalaavathi. "Grey Wolf Optimizer Based Web usage Data Clustering with Enhanced Fuzzy C Means Algorithm." International Journal of Data Mining Techniques and Applications 6, no. 1 (2017): 12–16. http://dx.doi.org/10.20894/ijdmta.102.006.001.003.
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Fu, Xinghe, Dingyu Guo, Kai Hou, Hongchao Zhu, Wu Chen, and Da Xu. "Fault Diagnosis of an Excitation System Using a Fuzzy Neural Network Optimized by a Novel Adaptive Grey Wolf Optimizer." Processes 12, no. 9 (2024): 2032. http://dx.doi.org/10.3390/pr12092032.
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As the excitation system is the core control component of a synchronous condenser system, its fault diagnosis is crucial for maximizing the reactive power compensation capability of the synchronous condenser. To achieve accurate diagnosis of excitation system faults, this paper proposes a novel adaptive grey wolf optimizer (AGWO) to optimize the initial weights and biases of the fuzzy neural network (FNN), thereby enhancing the diagnostic performance of the FNN model. Firstly, an improved nonlinear convergence factor is introduced to balance the algorithm’s global exploration and local exploitation capabilities. Secondly, a new adaptive position update strategy that enhances the interaction capability of the position information is proposed to improve the algorithm’s ability to jump out of the local optimum and accelerate the convergence speed. In addition, it is demonstrated that the proposed AGWO algorithm has global convergence. By selecting real fault waveforms of the excitation system for case validation, the results show that the proposed AGWO has a better convergence performance compared to the grey wolf optimizer (GWO), particle swarm optimization (PSO), whale optimization algorithm (WOA), and marine predator algorithm (MPA). Specifically, compared to the FNN and GWO-FNN models, the AGWO-FNN model improves average diagnostic accuracy on the test set by 4.2% and 2.5%, respectively. Therefore, the proposed AGWO-FNN effectively enhances the accuracy of fault diagnosis in the excitation system and exhibits stronger diagnostic capability.
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Kamalova, Navruzov, Qian, and Lee. "Multi-Robot Exploration Based on Multi-Objective Grey Wolf Optimizer." Applied Sciences 9, no. 14 (2019): 2931. http://dx.doi.org/10.3390/app9142931.
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In this paper, we used multi-objective optimization in the exploration of unknown space. Exploration is the process of generating models of environments from sensor data. The goal of the exploration is to create a finite map of indoor space. It is common practice in mobile robotics to consider the exploration as a single-objective problem, which is to maximize a search of uncertainty. In this study, we proposed a new methodology of exploration with two conflicting objectives: to search for a new place and to enhance map accuracy. The proposed multiple-objective exploration uses the Multi-Objective Grey Wolf Optimizer algorithm. It begins with the initialization of the grey wolf population, which are waypoints in our multi-robot exploration. Once the waypoint positions are set in the beginning, they stay unchanged through all iterations. The role of updating the position belongs to the robots, which select the non-dominated waypoints among them. The waypoint selection results from two objective functions. The performance of the multi-objective exploration is presented. The trade-off among objective functions is unveiled by the Pareto-optimal solutions. A comparison with other algorithms is implemented in the end.
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P. Vijayalakshmi, P. M. Gomathi. "Intrusion Detection System in Wireless Sensor Network using Improved Whale Optimization and Enhanced Fuzzy Neural Network." Communications on Applied Nonlinear Analysis 32, no. 9s (2025): 1451–71. https://doi.org/10.52783/cana.v32.4168.
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Wireless sensor networks (WSNs) are regularly employed in risky, uncontrolled situations. WSNs are vulnerable to physical intrusion and security threats. Strong security measures must thus be implemented to secure networks where detecting intrusions are generally acknowledged as one of the most effective security methods for protecting a network from malicious assaults and illegal access. Recent study proposes an improved IDS based on modified binary grey wolf optimizer with support vector machine (GWOSVM-IDS). Optimal wolf counts are found using 3,5,7 wolves. The suggested technique attempts to enhance accuracies of intrusion detections while minimizing processing times with lower false alarm rates and feature counts created by IDS in WSNs. However in existing work sensor nodes consumes more energy to perform packet transmission. More energy consumption may lead to network failure because of this reason energy is the very important parameter in WSNs. Additionally, Grey Wolf Optimizer (GWO) performs poorly in local searches and has a slow convergence rate, both of which might affect intrusion detection effectiveness. Support vector machine (SVM) is unsuitable for managing huge data sets. Increased feature counts per data points during training results in poor SVM performances. To address these challenges, the suggested study suggests node clustering, which is accomplished with weighted KMC. Cluster heads (CHs) will be chosen using Mutation Based Improved Butterfly Optimization (MBIBO). To construct secure communications in WSNs, Improved Whale Optimizations (IWO) for feature selections from input network security laboratory dataset is developed, which reduces time consumption and increases intrusion detection efficiency. Experimental findings demonstrate efficacy of the suggested models in terms of packet delivery ratios, end-to-end latencies, throughputs, and attack detection rates.
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Sharma, Ravi, and Kapil Sharma. "An optimal nuclei segmentation method based on enhanced multi-objective GWO." Complex & Intelligent Systems 8, no. 1 (2021): 569–82. http://dx.doi.org/10.1007/s40747-021-00547-y.
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AbstractIn breast cancer image analysis, reliable segmentation of the nuclei is still an open-ended research problem. In this paper, a new clustering-based nuclei segmentation method is presented. First, the proposed method pre-processes the histopathology image through SLIC method. Then, a novel variant of multi-objective grey wolf optimizer is employed to group the obtained super-pixels into optimal clusters. Lastly, the optimal cluster with minimum value is segmented as the nuclei region. The experimental results demonstrates that the proposed variant of multi-objective grey wolf algorithm surpasses the existing multi-objective algorithms over ten standard multi-objective benchmark functions belonging to different categories. Particularly, the proposed variant has achieved best fitness value of more than 0.90 on 90% of the considered functions. Further, the nuclei segmentation accuracy of the proposed method is validated on H&E-stained estrogen receptor positive (ER+) breast cancer images. Experimental results illustrates that the proposed method has attained dice-coefficient value of more than 0.52 on 80% of the images. This illustrates that the proposed method is efficient in producing efficacious segmenting over histology images of Breast cancer.
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Sharma, Ravi, and Kapil Sharma. "An optimal nuclei segmentation method based on enhanced multi-objective GWO." Complex & Intelligent Systems 8, no. 1 (2021): 569–82. http://dx.doi.org/10.1007/s40747-021-00547-y.
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AbstractIn breast cancer image analysis, reliable segmentation of the nuclei is still an open-ended research problem. In this paper, a new clustering-based nuclei segmentation method is presented. First, the proposed method pre-processes the histopathology image through SLIC method. Then, a novel variant of multi-objective grey wolf optimizer is employed to group the obtained super-pixels into optimal clusters. Lastly, the optimal cluster with minimum value is segmented as the nuclei region. The experimental results demonstrates that the proposed variant of multi-objective grey wolf algorithm surpasses the existing multi-objective algorithms over ten standard multi-objective benchmark functions belonging to different categories. Particularly, the proposed variant has achieved best fitness value of more than 0.90 on 90% of the considered functions. Further, the nuclei segmentation accuracy of the proposed method is validated on H&E-stained estrogen receptor positive (ER+) breast cancer images. Experimental results illustrates that the proposed method has attained dice-coefficient value of more than 0.52 on 80% of the images. This illustrates that the proposed method is efficient in producing efficacious segmenting over histology images of Breast cancer.
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Rajendra, K., S. Subramanian, N. Karthik, K. Naveenkumar, and S. Ganesan. "Grey Wolf Optimizer and Cuckoo Search Algorithm for Electric Power System State Estimation with Load Uncertainty and False Data." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 2s (2023): 59–67. http://dx.doi.org/10.17762/ijritcc.v11i2s.6029.
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State estimate serves a crucial purpose in the control centre of a modern power system. Voltage phasor of buses in such configurations is referred to as state variables that should be determined during operation. A precise estimation is needed to define the optimal operation of all components. So many mathematical and heuristic techniques can be used to achieve the aforementioned objective. An enhanced power system state estimator built on the cuck search algorithm is described in this work. Several scenarios, including the influence of load uncertainty and the likelihood of false data injection as significant challenges in electrical energy networks, are proposed to analyse the operation of estimators. The ability to identify and correct false data is also assessed in this regard. Additionally, the performance of the presented estimator is compared to that of the weighted least squares, Cuckoo Search algorithm and grey wolf Optimizer. The findings demonstrate that the grey wolf Optimizer overcomes the primary shortcomings of the conventional approaches, including accuracy and complexity, and is also better able to identify and rectify incorrect data. On IEEE 14-bus and 30-bus test systems, simulations are run to show how well the method works.
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Bakaya-Kyahurwa, E., and L. K. Tartibu. "Multi-objective optimization of a solar photovoltaic thermal water collector using Grey Wolf Optimizer." International Conference on Artificial Intelligence and its Applications 2023 (November 9, 2023): 227–33. http://dx.doi.org/10.59200/icarti.2023.032.
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Solar photovoltaic thermal (PV-T) collectors offer enhanced overall efficiency owing to reduced heat losses at the aperture plane and the potential for increased irradiance per unit area in comparison to larger prototypes. In spite of its promise, investigations into optimizing PV-T collector performance remain limited. This paper introduces a novel approach: the multi-objective optimization of a hybrid PV-T water system with a channel-type absorber design, utilizing the Grey Wolf Optimizer. The proposed approach employs regression models to depict key performance metrics—average thermal and electrical efficiencies. Optimal parameters, including rate of flow of cooling fluid, temperature at inlet, and slope of the solar panel, are determined and reported. In this paper, Grey Wolf Optimization algorithm, has been used to systematically explore the interaction of various PV-T parameter values. Optimal values, in line with the defined objective function(s) were obtained. This approach enabled the researchers to concurrently ascertain the comprehensive performance of the collector. Remarkably, the optimization process revealed a unique insight. Despite the inherently conflicting nature of thermal and electrical efficiencies as observed in single-objective optimization outcomes, the multi-objective MOGWO approach unveiled a solution where compromise was attained.
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Hou, Yuxiang, Huanbing Gao, Zijian Wang, and Chuansheng Du. "Improved Grey Wolf Optimization Algorithm and Application." Sensors 22, no. 10 (2022): 3810. http://dx.doi.org/10.3390/s22103810.
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This paper proposed an improved Grey Wolf Optimizer (GWO) to resolve the problem of instability and convergence accuracy when GWO is used as a meta-heuristic algorithm with strong optimal search capability in the path planning for mobile robots. We improved chaotic tent mapping to initialize the wolves to enhance the global search ability and used a nonlinear convergence factor based on the Gaussian distribution change curve to balance the global and local searchability. In addition, an improved dynamic proportional weighting strategy is proposed that can update the positions of grey wolves so that the convergence of this algorithm can be accelerated. The proposed improved GWO algorithm results are compared with the other eight algorithms through several benchmark function test experiments and path planning experiments. The experimental results show that the improved GWO has higher accuracy and faster convergence speed.
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Duan, Yonghui, Chen Li, Xiang Wang, Yibin Guo, and Hao Wang. "Forecasting Influenza Trends Using Decomposition Technique and LightGBM Optimized by Grey Wolf Optimizer Algorithm." Mathematics 13, no. 1 (2024): 24. https://doi.org/10.3390/math13010024.
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Influenza is an acute respiratory infectious disease marked by its high contagiousness and rapid spread, caused by influenza viruses. Accurate influenza prediction is a critical issue in public health and serves as an essential tool for epidemiological studies. This paper seeks to improve the prediction accuracy of influenza-like illness (ILI) proportions by proposing a novel predictive model that integrates a data decomposition technique with the Grey Wolf Optimizer (GWO) algorithm, aiming to overcome the limitations of current prediction methods. Firstly, the most suitable indicators were selected using Spearman correlation coefficient. Secondly, a GWO-LightGBM model was established to obtain the residuals between the predicted and actual values. The residual sequence from the GWO-LightGBM model was then decomposed and corrected using the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) method, which led to the development of the GWO-LightGBM-CEEMDAN model. The incorporation of the Baidu Index was shown to enhance the precision of the proposed model’s predictions. The proposed model outperforms comparison models in terms of evaluation metrics such as RMSE and MAPE. Additionally, our study found that the revised Baidu Index indicators show a notable association with ILI trends.