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 opp...
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....
Traditional grey wolf optimizers (GWOs) have difficulty balancing convergence and diversity when used for multimodal optimization problems (MMOPs), resulting in low-quality solutions and slow convergence. To address these drawbacks of GWOs, a fuzzy s...
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 ea...
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...
The Grey Wolf Optimizer (GWO) is a well-known metaheuristic algorithm that currently has an extremely wide range of applications. However, with the increasing demand for accuracy, its shortcomings of low exploratory and population diversity are incre...
Backpropagation Neural Networks (BPNNs) are widely used in fault diagnosis and parameter prediction due to their simple structure and strong universal approximation capabilities. However, BPNNs suffer from slow convergence and susceptibility to poor...
The quest for an efficient nature-inspired optimization technique has continued over the last few decades. In this paper, a hybrid nature-inspired optimization technique has been proposed. The hybrid algorithm has been constructed using Mean Grey Wol...
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 co...
Metaheuristic algorithms are widely used for optimization in both research and the industrial community for simplicity, flexibility, and robustness. However, multi-modal optimization is a difficult task, even for metaheuristic algorithms. Two importa...
Process planning optimization is a well-known NP-hard combinatorial problem extensively studied in the scientific community. Its main components include operation sequencing, selection of manufacturing resources and determination of appropriate setup...
Grey Wolf Optimizer (GWO) has been widely applied in many fields due to its advantages of fast convergence speed, simple parameter settings, and easy implementation. However, with the deepening of algorithm research, GWO has also exposed some problem...
For wireless sensor network (WSN) coverage problems, since the sensing range of sensor nodes is a circular area with symmetry, taking symmetry into account when deploying nodes will help simplify problem solving. In addition, in view of two specific...
Features extracted from the electromyography (EMG) signal normally consist of irrelevant and redundant features. Conventionally, feature selection is an effective way to evaluate the most informative features, which contributes to performance enhance...
The knapsack problem is one of the most widely researched NP-complete combinatorial optimization problems and has numerous practical applications. This paper proposes a quantum-inspired differential evolution algorithm with grey wolf optimizer (QDGWO...
The grey wolf optimization (GWO) algorithm is a simple and effective meta-heuristic algorithm that mimics the leadership of grey wolves and the social behavior of wolves in nature. However, the updating of GWO population positions only relies on the...
This paper presents a comparison of optimizers for tuning a fractional-order proportional-integral-derivative (FOPID) and proportional-integral-derivative (PID) controllers, which were applied to a DC/DC boost converter. Grey wolf optimizer (GWO) and...
A Model-based predictive current control (MBPCC) has recently become a powerful advanced control technology in industrial drives. However, MBPCC relies on the knowledge of the system model and parameters, being, therefore, very sensitive to parameter...
Permanent magnet synchronous motors (PMSM), with their high efficiency and power density, are widely used in industrial applications. For PMSM speed servo systems, fractional-order proportional-integral (FOPI) controllers demonstrate superior robustn...
Construction duration and schedule robustness are of great importance to ensure efficient construction. However, the current literature has neglected the importance of schedule robustness. Relatively little attention has been paid to schedule robustn...
Effectively managing agricultural practices is crucial for maximizing yield, reducing investment costs, preserving soil health, ensuring sustainability, and mitigating environmental impact. This study proposes an adaptation of the Grey Wolf Optimizer...
Dissolved gas analysis (DGA) is a widely used method for transformer internal fault diagnosis. However, the traditional DGA technology, including Key Gas method, Dornenburg ratio method, Rogers ratio method, International Electrotechnical Commission...
Considering the crucial influence of feature selection on data classification accuracy, a grey wolf optimizer based on quantum computing and uncertain symmetry rough set (QCGWORS) was proposed. QCGWORS was to apply a parallel of three theories to fea...
This paper proposes an improved Predictive Torque Control (PTC) of a PMSM based on the Grey Wolf Optimizer (GWO) for smooth torque operation in Electric Bus applications (EBs). The embedded GWO is used to resolve the torque tracking tasks with minima...
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...
This paper proposes an easily understandable Grey Wolf Optimizer (GWO) applied to the optimal tuning of the parameters of Takagi-Sugeno proportional-integral fuzzy controllers (T-S PI-FCs). GWO is employed for solving optimization problems focused on...
At the current stage of the integration of renewable energy sources into the power systems of many countries, requirements for compliance with established technical characteristics are being applied to power generation. One such requirement is the in...
Path planning is crucial for safely and efficiently navigating unmanned aerial vehicles (UAVs) toward operational goals. Often, this is a complex, multi-constraint, and non-linear optimization problem, and metaheuristic algorithms are frequently used...
The purpose of this work is to perform the joint design of a classification system including both a radar sensor and an image processing software. Experimental data were generated with a three-dimensional scanner. The criterion which rules the design...
Poland is facing demanding challenges to achieve a sustainable energy mix in the near future. Crucial and tough decisions must be made about the direction of the national energy economy, safety, and environmental impact. Considering the electricity a...
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....
Unmanned aerial vehicles (UAVs) are increasingly favored for emergency search and rescue operations due to their high adaptability to harsh environments and low operational costs. However, the dynamic nature of search path endpoints, influenced by ta...
This paper proposes a golden sine grey wolf optimizer (GSGWO) that can be adapted to the obstacle-crossing function to solve the path planning problem of obstacle-crossable robot. GSGWO has been improved from the gray wolf optimizer (GWO), which prov...
With the development of the Internet and big data, more and more consumer behavior data are used in different forecasting problems, which greatly improve the performance of prediction. As the main travel tool, the sales of automobiles will change wit...
Due to the present trend in the wind industry to operate in deep seas, floating offshore wind turbines (FOWTs) are an area of study that is expanding. FOWT platforms cause increased structural movement, which can reduce the turbine’s power prod...
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 prop...
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 excitati...
The hybrid renewable energy system is a promising and significant technology for clean and sustainable island power supply. Among the abundant ocean energy sources, tidal current energy appears to be very valuable due to its excellent predictability...
Supplier selection and order quantity allocation have a strong influence on a company’s profitability and the total cost of finished products. From an optimization perspective, the processes of selecting the right suppliers and allocating order...
Optimal energy management has become a challenging task to accomplish in today’s advanced energy systems. If energy is managed in the most optimal manner, tremendous societal benefits can be achieved such as improved economy and less environmen...
Economic load dispatch (ELD) provides significant benefits to the operation of the power system. It appears to be a complex nonconvex optimization problem subject to several equal and unequal constraints. The greedy sine-cosine nonhierarchical gray w...
Wireless sensor network (WSN) nodes are devices with limited power, and rational utilization of node energy and prolonging the network lifetime are the main objectives of the WSN’s routing protocol. However, irrational considerations of heterog...
The power scheduling problem in a smart home (PSPSH) refers to the timely scheduling operations of smart home appliances under a set of restrictions and a dynamic pricing scheme(s) produced by a power supplier company (PSC). The primary objectives of...
Three-dimensional registration with the affine transform is one of the most important steps in 3D reconstruction. In this paper, the modified grey wolf optimizer with behavior considerations and dimensional learning (BCDL-GWO) algorithm as a registra...
Metaheuristic algorithms (MAs) now are the standard in engineering optimization. Progress in computing power has favored the development of new MAs and improved versions of existing methods and hybrid MAs. However, most MAs (especially hybrid algorit...
Coal-fired power plants are widely used to achieve a power balance in grids with renewable energy, which leads to new requirements for speediness in load dispatch. This paper presents a nondominated-sorting grey wolf optimizer algorithm (NSGWO) for t...
The utilization of unmanned systems has witnessed a steady surge in popularity owing to its tremendous potential for a wide range of applications. In particular, the coordination among multiple vehicle systems has been demonstrated to possess unparal...
In this paper, an adaptive speed controller of the electrical drive is presented. The main part of the control structure is based on the Recurrent Wavelet Neural Network (RWNN). The mechanical part of the plant is considered as an elastic connection...
Due to the nonlinear and non-stationary characteristics of the carbon price, it is difficult to predict the carbon price accurately. This paper proposes a new novel hybrid model for carbon price prediction. The proposed model consists of an extreme-p...
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 fo...
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