Application of Grey Wolf Optimization Algorithm: Recent Trends, Issues, and Possible Horizons

Dada, Emmanuel Gbenga; Bassi, Joseph Stephen; Oyewola, David Opeoluwa; Fadele, Alaba Ayotunde; Chiroma, Haruna; Abdulhamid, Shafií Muhammad

doi:10.35378/gujs.820885

Cited by 26 publications

(14 citation statements)

References 73 publications

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“…The GWO technique is applicable in WEDM process for finding the best set of parameter, robotics, image processing, wireless sensor networks, robotics, PID controller tuning, environmental applications, economic load dispatch problems, feature selection, clustering etc. 32…”

Section: Methodsmentioning

confidence: 99%

Unified supervised learning and optimization technique for wire electrical discharge machining of various grades of alloys: stochastic algorithm combined neural network approach

Biswas,

Singh,

Mukherjee

2023

Proceedings of the Institution of Mechanical Engineers, Part C:

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In the field of machining different grades of alloys using wire electrical discharge machining (WEDM), developing efficient techniques has been a challenge. Typically, specific supervised learning and optimization (SLO) methods are created for individual alloys, resulting in a time-consuming process. To address this issue, a unified SLO approach capable of handling various grades of alloys with different compositions and physical characteristics is proposed. In this study, three stochastic optimization tools are combined with a multi-layer neural network to determine the optimal SLO strategy. Five different alloy grades are considered, with inputs consisting of six variables, including chemical composition, and physical properties. The neural network architecture used is 19-500-500-4, and it is combined with a gray wolf stochastic algorithm, proving to be the most effective SLO methodology. The results indicate that regardless of the alloy grade, the pulse on time significantly affects the material removal rate, kerf width, and thickness of the recast layer (RL). Surface roughness is primarily influenced by alloy properties such as density, specific heat, and elements like Cr, Mo, and Ni. Additionally, the recast layer is dependent on factors such as V, Ti, thermal conductivity, and thermal diffusivity of the materials. Validation experiments demonstrate that the mean squared error of all responses for each alloy remains within 5.0%. This unified SLO approach provides a promising solution for optimizing the machining process of different alloy grades, reducing the need for separate techniques for each material.

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Section: Methodsmentioning

confidence: 99%

Unified supervised learning and optimization technique for wire electrical discharge machining of various grades of alloys: stochastic algorithm combined neural network approach

Biswas,

Singh,

Mukherjee

2023

Proceedings of the Institution of Mechanical Engineers, Part C:

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“…GWOs [ 47 ] are motivated by grey wolves’ intelligent hunting tactics and social structure. It is common for grey wolves to live in groups of 5 to 12 individuals.…”

Section: Metaheuristic Computation For Controller Tuningmentioning

confidence: 99%

Stabilization and tracking control of underactuated ball and beam system using metaheuristic optimization based TID-F and PIDD2–PI control schemes

Zafar,

Malik,

Ali

et al. 2024

PLoS ONE

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In this paper, we propose two different control strategies for the position control of the ball of the ball and beam system (BBS). The first control strategy uses the proportional integral derivative-second derivative with a proportional integrator PIDD2-PI. The second control strategy uses the tilt integral derivative with filter (TID-F). The designed controllers employ two distinct metaheuristic computation techniques: grey wolf optimization (GWO) and whale optimization algorithm (WOA) for the parameter tuning. We evaluated the dynamic and steady-state performance of the proposed control strategies using four performance indices. In addition, to analyze the robustness of proposed control strategies, a comprehensive comparison has been performed with a variety of controllers, including tilt integral-derivative (TID), fractional order proportional integral derivative (FOPID), integral–proportional derivative (I-PD), proportional integral-derivative (PI-D), and proportional integral proportional derivative (PI-PD). By comparing different test cases, including the variation in the parameters of the BBS with disturbance, we examine step response, set point tracking, disturbance rejection analysis, and robustness of proposed control strategies. The comprehensive comparison of results shows that WOA-PIDD2-PI-ISE and GWO-TID-F- ISE perform superior. Moreover, the proposed control strategies yield oscillation-free, stable, and quick response, which confirms the robustness of the proposed control strategies to the disturbance, parameter variation of BBS, and tracking performance. The practical implementation of the proposed controllers can be in the field of under actuated mechanical systems (UMS), robotics and industrial automation. The proposed control strategies are successfully tested in MATLAB simulation.

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“…An optimization model for CFRP-based system design was developed using the gray wolf optimization (GWO) algorithm. This metaheuristic algorithm, inspired by gray wolf behavior, has been successfully applied in various optimization problems (Mirjalili et al, 2014;Emmanuel et al, 2022). Based on the provided explanations, the second section presents a laboratory model, the process of numerical modeling along with parametric study scenarios, and the optimal design model of CFRP plates for Strengthening RC slab-column connections.…”

Section: Introductionmentioning

confidence: 99%

Optimal Design of CFRP Plates For Strengthening RC Flat Slab-Column Connections: Parametric Study and GWO-Based Optimization

Kohneposhi,

Azizian,

Haddad

et al. 2024

Preprint

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The present study assesses the efficacy of carbon-fiber-reinforced polymer (CFRP) plates in enhancing the strengthening of reinforced concrete (RC) flat slab-column connections under both punching shear and seismic loading conditions. A comprehensive parametric analysis is conducted using the Abaqus finite element software, encompassing 33 distinct models with variations in key parameters including loading conditions, number of fiber layers, orientation, thicknesses, and dimensions of the CFRP plate. The numerical model is validated through experimental testing of a laboratory-scale prototype, demonstrating a mean error of 5.2% relative to experimental data, indicating satisfactory accuracy. Furthermore, an optimal design approach for CFRP plates is developed employing the gray wolf optimization (GWO) algorithm. The primary objective of this optimization framework is to mitigate damage in the slab-column connection under loading. Computational simulations are performed simultaneously in Abaqus and MATLAB to determine the optimal geometric parameters of CFRP for retrofitting the connection. The results demonstrate that CFRP plates with optimal parameters can reduce more than 90% of the damage incurred at the slab-column connection. The results, together with the CFRP optimization model developed using GWO, can be practically utilized for the optimal design of CFRP plates to reinforce structures.

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Application of Grey Wolf Optimization Algorithm: Recent Trends, Issues, and Possible Horizons

Cited by 26 publications

References 73 publications

Unified supervised learning and optimization technique for wire electrical discharge machining of various grades of alloys: stochastic algorithm combined neural network approach

Unified supervised learning and optimization technique for wire electrical discharge machining of various grades of alloys: stochastic algorithm combined neural network approach

Stabilization and tracking control of underactuated ball and beam system using metaheuristic optimization based TID-F and PIDD2–PI control schemes

Optimal Design of CFRP Plates For Strengthening RC Flat Slab-Column Connections: Parametric Study and GWO-Based Optimization

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