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Gwo Algorithm, These challenges include low convergence accuracy, slow iteration speed, and vulnerability to local optima. It uses two main aspects of grey wolves’ lives: Hierarchy of grey wolves: α, β, δ, and ω wolves Hunting strategy: ⋆ Searching Aug 28, 2024 · The GWO algorithm was employed as a training algorithm for Multi-layer perceptron (Feedforward Neural Networks) in 2015 [2]. Simulation results show that the proposed IPSO-GWO is able to find an optimal path much faster than traditional PSO-GWO based methods. This method is highly cited and recognized. To address this, we propose an Improved Adaptive Grey Wolf Optimization (IAGWO), which significantly enhances The EPDE-GWO algorithm is compared with Genetic Algorithm (GA), Simulated Annealing (SA), Dung Beetle Optimizer (DBO), and Particle Swarm Optimization (PSO). PDF | The development of Grey Wolf Optimisation (GWO) Algorithm was motivated by the biological behaviours of swarm of wolves hunting for prey. , 2014). In multi-UAV scenarios, compared to GWO, IGnT, IGnaa, IGngw, and IGnH algorithms, it significantly optimizes path length, fitness value, and collision frequency. A hybrid LSTM-GRU framework for lung cancer classification using GWO-WOA algorithm for hyperparameter tuning and BPSO for feature selection By Mohmod M. Therefore, as they catch the hunt (approaching the solution), they may create an intensity in the same or certain regions. Mar 15, 2025 · Gray Wolf Optimization (GWO), inspired by the social hierarchy and cooperative hunting behavior of gray wolves, is a widely used metaheuristic algorithm for solving complex optimization problems Let's learn the mathematical models for GWO Watch this video to learn how and what I have designed the mathematical equatiosn for the GWO algorithm. To improve the prediction accuracy of compressive strength for superabsorbent polymer (SAP)-internally cured manufactured sand concrete, a prediction model was developed by optimizing the training process of the radial basis function (RBF) neural network using grey wolf optimization (GWO). In Hand, Foot, and Mouth Disease (HFMD) control, conventional SEIR/SIR models rely on static parameters, which limits their adaptability to dynamic real‑world conditions. One of the most widely referenced Swarm Intelligence (SI) algorithms is the Grey Wolf Optimizer (GWO), which is based on the pack hunting and natural leadership organization of grey wolves. Grey Wolf Optimization (GWO) is a nature-inspired metaheuristic algorithm that mimics the leadership hierarchy and hunting behavior of grey wolves in the wild. An improved grey wolf optimizer (IGWO) with evolution and elimination mechanism was proposed so as to . The Grey Wolf Optimization (GWO) is a highly effective meta-heuristic algorithm leveraging swarm intelligence to tackle real-world optimization problems. An improved grey wolf optimizer for training q-Gaussian Radial Basis Functional-link nets was proposed by Muangkote [3]. Our approach combines two methods by replacing a particle of the PSO with small possibility by a particle partially improved with the GWO. [44] enhanced GWO with an adaptive convergence factor strategy for UAV trajectory planning. First, a brief literature Grey Wolf Optimizer (GWO) algorithm is a relatively new algorithm in the field of swarm intelligence for solving continuous optimization problems as w… A discussion on the properties of GWO algorithm and how it minimises the different problems in the different applications is presented, as well as an analysis on the research trend of GWO optimisation technique in various applications from year 2014 to 2017. In [19], the authors proposed a new MPPT method via INC that combines the GWO algorithm with the Levy flight function, which incorporates the PV system MPPT control strategy. Accurate prediction of outlet water temperature in fractures is essential to significantly reduce engineering costs and energy consumption. Each approach combines GWO with Particle Swarm Optimization (PSO) by implementing GWO followed by PSO. The GWO algorithm offers several advantages in optimization and machine learning applications Simplicity The algorithm is easy to understand and implement due to its intuitive nature-inspired concepts. The algorithm is designed based on the social dominance structure of grey wolves, where the pack is led by Considering the advantages of grey wolf optimization (GWO), an optimization algorithm for mobile power allocation is proposed. With GWO, PV systems first see the global optimum and then track to the nearby optimal. Second, the simulation process of GWO is controlled by one key parameter (Section 2. We Grey Wolf Optimizer (GWO) developed by Mirjalili et al. After different comparisons, it shows that GWO algorithm can obtain a shorter running time and a smaller OP. This chapter describes the grey wolf optimization (GWO) algorithm as one of the new meta-heuristic algorithms. Four types of grey wo… To address these limitations, we propose a novel feature selection algorithm called grey wolf optimizer with self-repulsion strategy (GWO-SRS). Purpose: This paper aims to apply grey wolf optimizer (GWO) algorithm for steady state analysis of self-excited induction generators (SEIGs) supplying isolated loads. Three-dimensional Path Planning for Multiple UCAVs Based on the MP-GWO Algorithm This study introduces a hybrid LSTM–GRU framework optimized using a Grey Wolf–Whale Optimization (GWO–WOA) algorithm for hyperparameter tuning and Binary Particle Swarm Optimization (BPSO To assess the effectiveness of the proposed IPSO-GWO algorithm, extensive simulations were carried out using the FogWorkflowSim framework—an environment specifically developed to capture the complexities of workflow execution within fog-cloud architectures. The GWO algorithm mimics the leadership hierarchy and hunting mechanism of grey wolves in nature. Three improved binary Grey Wolf Optimization (GWO) approaches are proposed in this paper to optimize the feature selection process by enhancing the feature selection accuracy while selecting the least possible number of features. This | Find, read and cite all the research you These findings underscore the potential of WOA and GWO algorithms to enhance PV system performance, offering robust and efficient solutions for optimizing energy output in both simulation and real Let's consider one of the newest modern optimization algorithms - Grey Wolf Optimization. The experimental results demonstrate that EPDE-GWO reduces path length by 24. However, when confronted with large-scale problems, GWO encounters hurdles in convergence speed and problem-solving capabilities. This is unique as it follows the leadership hierarchy of the grey wolves. Mar 1, 2014 · The GWO algorithm mimics the leadership hierarchy and hunting mechanism of grey wolves in nature. The GWO is a metaheuristic algorithm that belongs to the third category (Nature-inspired). Grey wolves are well known for pack hunting and no other SI methods The grey wolf optimizer (GWO) is a novel type of swarm intelligence optimization algorithm. 1). in 2014 [1]. In this paper, the detailed description of GWO is presented along with different development in standard GWO and its applications. First, a brief literature review is presented and then the natural process of the GWO algorithm is described. The increasing trend of applying GWO shows that although it is a simple algorithm with few control parameters, it effectively solves optimization problems, particularly in various IoT applications. Request PDF | Grey Wolf Optimization (GWO) Algorithm | This chapter describes the grey wolf optimization (GWO) algorithm as one of the new meta-heuristic algorithms. The review revealed that opportunities still exists fo development of Abstract The Grey Wolf Optimizer (GWO) is a nature-inspired optimization algorithm based on the social hierarchy and hunting behavior of grey wolves in the wild. in 2014. Figure 3 shows the classification of the theoretical aspects of GWO. [43] first applied GWO to UAV path planning, achieving strong performance in accuracy and stability. GWO算法自2014年提出以来，受到了广泛关注和研究。 它不仅在学术界被广泛研究和引用，而且在工程优化、机器学习、图像处理等多个领域得到了成功应用。 GWO最初只是作者在研究其他优化算法时的灵感，但最终却发展成为一个独立且强大的优化工具。 Hello, in this video, you will learn about the grey wolf optimization algorithm. This algorithm is inspired by the hunting process found in Grey Wolves. The GWO algorithm offers several significant benefits, including simple implementation, rapid convergence, and superior convergence outcomes, leading to its effective application in diverse fields for Previous article Grey wolf optimization- Introduction talked about inspiration of grey wolf optimization, and its mathematical modelling and algorithm. Also, the opti-mization process and a pseudo code of the GWO algorithm are presented in this chapter. To address this, we Finally, simulations are conducted under the three-dimensional cooperative trajectory planning model for multiple UCAVs, with results indicating that the proposed algorithm demonstrates better cooperativeness compared to the GWO, PSO, and EA algorithms, significantly improving the solution accuracy and convergence speed. in 2014 (Mirjalili et al. The proposed GWO approach carries out an comparison with other optimization algorithms. May 22, 2018 · The GWO algorithm mimics the leadership hierarchy and hunting mechanism of grey wolves in nature. In this article we will implement grey wolf optimization (GWO) for two fitness functions - Rastrigin function and Sphere function. We have evaluated our approach on five different benchmark functions and This review discusses GWO algorithm based on three classifications: Theoretical aspects of GWO which includes the GWO modifications, hybridized versions of GWO, parallel versions of GWO and multi-objective versions of GWO. Experimental results demonstrate that IHS-GWO achieves superior optimization accuracy and stability across various benchmark functions of CEC2017. We improved chaotic tent mapping to initialize the wolves to enhance the global search ability and used a nonlinear convergence factor based on the As a newly proposed optimization algorithm based on the social hierarchy and hunting behavior of gray wolves, grey wolf algorithm (GWO) has gradually become a popular method for solving the In this paper, two novel meta-heuristic algorithms are introduced to solve global optimization problems inspired by the Grey Wolf Optimizer (GWO) algorithm. Precisely, this article presents a state of the art review of the GWO algorithm, its progress, and applications in more complex real-world problem-solving. The enhanced optimization algorithm is then employed to fine-tune the key hyperparameters of a long short-term memory neural network, leading to a more accurate and stable situational prediction GWO is an adaptable algorithm that efficiently addresses highly nonlinear, multivariable, and multimodal optimization challenges. In this paper, we have introduced a differential perturbation operator into the gray wolf optimization (GWO) algorithm using three randomly selected omega wolves which assist the three leader wolve 文章浏览阅读10w+次，点赞73次，收藏612次。灰狼优化算法 (GWO)是一种群智能优化算法，受到灰狼捕食行为启发。该算法通过模仿灰狼社会等级结构和捕猎行为来进行优化搜索，具有良好的收敛性和易用性。 grey wolf packs . It was introduced by Seyedali Mirjalili in 2014 [1] as a swarm intelligence -based technique for solving optimization problems. Amrir, 6 hours ago Real-time dynamic prediction of HFMD transmission using SEIRQ-ARIMA hybrid model optimized by multi-stage ABC-GWO algorithm The water-rock heat exchange behavior in rock fractures is considered a critical factor in geothermal engineering design during the research and utilization of hot dry rocks. We tried to explain the Grey Wolf Optimization 1. Also, the optimization process and a pseudo In this study, we propose a new hybrid algorithm fusing the exploitation ability of the particle swarm optimization (PSO) with the exploration ability of the grey wolf optimizer (GWO). Four types of grey wolves such as alpha, beta, delta, and omega are employed for simulating the leadership hierarchy. In this study, an enhanced hybrid Grey Wolf Optimization algorithm (HI-GWO) is proposed to address the challenges encountered in traditional swarm intelligence algorithms for mobile robot path planning. proposed a hybrid harmony search and GWO named GWO-HS with an opposition learning strategy to solve global optimization problems. Zhang et al. Grey wolf optimization Grey wolf optimization (GWO) is a metaheuristic algorithm that is inspired by the behavior of grey wolves in leadership and hunting (Mirjalili et al. First, GWO is an efficient optimization algorithm that has a simple structure (Figure 1). This paper presents recent progr ss on Grey Wolf Optimization (GWO) algorithm, its variants and their applications, issues, and likely prospects. The optimized solutions of GWO were compared with other optimization algorithms in the literature, and it was found that the GWO could lead to an excellent optimum solution Grey wolf optimizer (GWO) is an efficient optimizer stimulated by the hunting mechanism of wolves. It is inspired by grey wolves, also called Canis lupus. Grey wolves live in packs with a strict social dominance hierarchy, which is emulated in the GWO algorithm through four distinct levels . A multi-field triaxial experimental system was employed to perform convective heat Summary In this study, we propose a new hybrid algorithm fusing the exploitation ability of the particle swarm optimization (PSO) with the exploration ability of the grey wolf optimizer (GWO). Introduction Grey Wolf Optimizer (GWO) was proposed by Seyedali Mirjalili et al. In the GWO algorithm, wolves are likely to be located in regions close to each other. The GWO algorithm was employed to optimize the hidden layer center points, Gaussian function width, and In the standard Grey Wolf Optimization (GWO) algorithm, the position update weights for Alpha, Beta, and Delta wolves are fixed at equal values (each occupying one-third), neglecting the influence of individual fitness disparities of leaders on guiding the search direction. This is one of the top algorithms for use in training neural networks, smooth functions with many variables. grey wolf algorithm example. However, such datasets often contain redundant, noisy, and imbalanced attributes that limit the performance of traditional classifiers. Sh. 6%, prevents premature convergence, and exhibits strong global search capabilities. 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. This study introduces a hybrid LSTM-GRU framework optimized using a Grey Wolf-Whale Optimization (GWO-WOA) algorithm for hyperparameter tuning and Binary Particle Swarm Optimization (BPSO) for feature selection. Introduction The GWO algorithm mimics the leadership hierarchy and hunting mechanism of gray wolves in nature proposed by Mirjalili et al. Jul 23, 2025 · Grey wolf optimizer (GWO) is a population-based meta-heuristics algorithm that simulates the leadership hierarchy and hunting mechanism of grey wolves in nature, and it’s proposed by Seyedali Mirjalili et al. The algorithm classifies a population of possible solutions into four types of wolves α, β, δ, and ω. Our approach combines two methods by replacing a particle of the Pso with small possibility by a particle partially improved with the GWO. In this algorithm, the information sharing property of ABC is hybridized by the original hunting strategy of GWO to improve the exploration ability. Four types of grey wolves such as alpha, beta, delta, and omega are employed for simulating the The impact of algorithm settings on the optimization performance of GWO was explored, and it was found that GWO could provide the best performance by using 40 wolfs. It is a nature-inspired swarm metaheuristic optimization algorithm. In this case, the mechanism to Abstract This chapter describes the grey wolf optimization (GWO) algorithm as one of the new meta-heuristic algorithms. Keywords olf Optimisation (GWO) Algorithm was motivated by the biological behaviours of swarm of wolves hunting for prey. The HI-GWO algorithm introduces several key improvements to overcome these The proposed IPSO-GWO algorithm is first tested by function optimization using ten benchmark functions and then applied for optimal robot path planning in a simulated environment. The original behavior on test functions makes this algorithm one of the most interesting among the ones considered earlier. Our specific contribution is the development and validation of a DNN-Grey Wolf Optimizer (GWO) algorithm, where the DNN serves as a surrogate model trained on a hybrid dataset of experimental measurements and high-fidelity simulations, explicitly incorporating measurement uncertainty. zasqz, wby3, j06bg, ngmqe, rwddo, qyuuft, eastcg, clfw2d, wxgdj, 0zqtl,