Recent Advances in Grey Wolf Optimizer, its Versions and Applications: Review

Makhadmeh, Sharif Naser; Al-Betar, Mohammed Azmi; Doush, Iyad Abu; Awadallah, Mohammed A.; Kassaymeh, Sofian; Mirjalili, Seyedali; Zitar, Raed Abu

doi:10.1109/access.2023.3304889

Cited by 23 publications

(6 citation statements)

References 302 publications

(470 reference statements)

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“…The hunting strategy of gray wolves involves tracking the prey, moving closer, surrounding it, and finally initiating the attack. 50…”

Section: Gray Wolf Optimization Algorithmmentioning

confidence: 99%

Nature‐ınspired algorithms for optimizing fractional order PID controllers in time‐delayed systems

Güven,

Mengi

2024

Optim Control Appl Methods

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Time‐delayed systems frequently appear, especially in sectors such as fluid flow processes, chemical procedures, and the food industry. This paper addresses the optimization of parameters for a fractional order PID (FOPID) controller, which is used to control a time‐delayed system, using five distinct algorithms inspired by nature. These algorithms are NewBAT, Cuckoo search (CS), Firefly (FF), Gray Wolf Optimizer (GWO), and Whale optimization algorithm (WOA). The FOPID controller parameters, namely KP, KI, KD, λ and μ, have been optimized using these algorithms. During the optimization process, the integral of the time absolute error (ITAE) was considered as the primary measurement criterion. In addition to this value, the maximum overshoot, settling time, time to reach the maximum value, and error values were examined. Simulations conducted with the obtained parameters tested the system's resilience to disturbances introduced at the output, and the controller responses were also evaluated during these tests. The reactions of the determined parameters to different reference inputs were analyzed, and the results are presented in graphs and tables. The efficiency and reliability of the optimization algorithms were substantiated by comprehensive statistical analyses. These analyses play a critical role in algorithm selection and objective evaluation of the results. Simulation studies were conducted in the Matlab and Simulink environments. The FOMCON Toolbox was used for fractional‐order processes.

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“…The hunting strategy of gray wolves involves tracking the prey, moving closer, surrounding it, and finally initiating the attack. 50…”

Section: Gray Wolf Optimization Algorithmmentioning

confidence: 99%

Nature‐ınspired algorithms for optimizing fractional order PID controllers in time‐delayed systems

Güven,

Mengi

2024

Optim Control Appl Methods

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“…This sensitivity may result in suboptimal solutions or hinder the reproducibility of results across different runs or problem instances [ 58 ]. Additionally, like many metaheuristic algorithms, GWO is susceptible to the risk of premature convergence to local optima, especially in complex and multimodal optimization problems This can restrict the algorithm’s ability to explore the entire search space and find globally optimal solutions, particularly in problems with irregular objective functions [ 58 , 59 , 60 ]. Furthermore, the performance of GWO may heavily depend on the choice of algorithmic parameters, such as the number of iterations and the population size, which can make it challenging to achieve consistent results across different optimization tasks [ 61 ].…”

Section: Practical Implications and Limitations Of Gwomentioning

confidence: 99%

Parametric Optimization of FDM Process for PA12-CF Parts Using Integrated Response Surface Methodology, Grey Relational Analysis, and Grey Wolf Optimization

Almuflih,

Abas,

Khan

et al. 2024

Polymers

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Efficiently managing multiple process parameters is critical for achieving optimal performance in additive manufacturing. This study investigates the relationship between eight key parameters in fused deposition modeling (FDM) and their impact on responses like average surface roughness (Ra), tensile strength (TS), and flexural strength (FS) of carbon fiber-reinforced polyamide 12 (PA 12-CF) material. The study integrates response surface methodology (RSM), grey relational analysis (GRA), and grey wolf optimization (GWO) to achieve this goal. A total of 51 experiments were planned using a definitive screening design (DSD) based on response RSM. The printing process parameters, including layer thickness, infill density, and build orientation, significantly affect Ra, TS, and FS. GRA combines responses into a single measure, grey relational grade (GRG), and a regression model is developed. GWO is then employed to optimize GRG across parameters. Comparison with GRA-optimized parameters demonstrates GWO’s ability to discover refined solutions, reducing average surface roughness to 4.63 μm and increasing tensile strength and flexural strength to 88.5 MPa and 103.12 MPa, respectively. Practical implications highlight the significance of GWO in industrial settings, where optimized parameters lead to reduced costs and improved product quality. This integrated approach offers a systematic methodology for optimizing FDM processes, ensuring robustness and efficiency in additive manufacturing applications.

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“…The Gray Wolf Optimization Algorithm is a pack-wise optimization algorithm that attempts to model the social hierarchy and hunting behavior of gray wolves from nature in order to find the most appropriate solution to the problem [63]. Since its proposal, it has performed well in all kinds of optimization problems [64], especially in the parameter optimization of the nuclear limit learning machine. Using GWO as a parameter optimization treatment works well [65].…”

Section: Establishment Of the Sunflower Origin Identification Model 2...mentioning

confidence: 99%

Sunflower Origin Identification Based on Multi-Source Information Fusion Technique of Kernel Extreme Learning Machine

Suo,

Liu,

et al. 2024

Agronomy

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This study constructs a model for the rapid identification of the origins of edible sunflower (Helianthus) using Kernel Extreme Learning Machine (KELM) with multi-source information fusion technology. Near-infrared spectroscopy (NIRS) and nuclear magnetic resonance spectroscopy (NMRS) were utilized to analyze 180 sunflower samples from the Xinjiang, Heilongjiang, and Inner Mongolia regions. Initially, the identification models for the origin of sunflowers using NIR and NMR data were compared between two algorithms: the Extreme Learning Machine (ELM) and KELM, combined with various spectral preprocessing methods. The experiment found that the NIR spectral model preprocessed with standard normal variate (SNV) using the KELM algorithm was the most accurate, achieving accuracies of 98.7% in the training set and 97.2% in the test set. The spin-echo NMR spectral model preprocessed with non-local means (NLMs) using the KELM algorithm was the second best, with accuracies of 98.4% in the training set and 96.4% in the test set. To further improve the accuracy of the identification models, innovative sunflower origin identification models were developed based on data layer fusion and feature layer fusion using NIRS and NMRS. In the data layer fusion model, the KELM algorithm model was optimal, achieving a test set accuracy and F1 score of 98.2% and 98.18%, respectively, an improvement of 1.0% over the best single data source model. In the feature layer fusion model, four types of feature-layer information-fusion identification models were established using two feature extraction algorithms, Competitive Adaptive Reweighted Sampling (CARS) and Variable Importance Projection (VIP), combined with joint feature and simple merging feature strategies. The CARS-KELM algorithm combined with the joint feature method was found to be the best, achieving 100% accuracy in both the training and test sets, an improvement of 2.8% over the best single data source model. Identifying the origin of edible sunflower using NIRS and NMRS is demonstrated as feasible by the results. The best single-spectrum sunflower origin identification model was achieved using the KELM algorithm with SNV preprocessing. The feature layer fusion method combining NIRS and NMRS data is suitable for handling the task of sunflower origin identification. This method significantly improves the recognition accuracy of the model compared to a single model, achieving fast and accurate origin identification of edible sunflowers. The research results provide a new method for rapid identification of sunflower origin.

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Recent Advances in Grey Wolf Optimizer, its Versions and Applications: Review

Cited by 23 publications

References 302 publications

Nature‐ınspired algorithms for optimizing fractional order PID controllers in time‐delayed systems

Nature‐ınspired algorithms for optimizing fractional order PID controllers in time‐delayed systems

Parametric Optimization of FDM Process for PA12-CF Parts Using Integrated Response Surface Methodology, Grey Relational Analysis, and Grey Wolf Optimization

Sunflower Origin Identification Based on Multi-Source Information Fusion Technique of Kernel Extreme Learning Machine

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