Levy Flight-Based Improved Grey Wolf Optimization: A Solution for Various Engineering Problems

Bhatt, Bhargav; Sharma, Himanshu; Arora, Krishan; Joshi, Gyanendra Prasad; Shrestha, Bhanu

doi:10.3390/math11071745

Cited by 8 publications

(2 citation statements)

References 44 publications

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“…Grey wolves ritually hunt. Their hunting phases are [50] (1) Approaching the prey. (2) Circling and pestering the victim to cease moving.…”

Section: 𝑟 = 𝑟 − 𝐿 𝐿 + 𝛳𝐼 𝐿 𝑒mentioning

confidence: 99%

Data-Driven GWO-BRNN-Based SOH Estimation of Lithium-Ion Batteries in EVs for Their Prognostics and Health Management

Waseem,

Huang,

Wong

et al. 2023

Mathematics

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Due to the complexity of the aging process, maintaining the state of health (SOH) of lithium-ion batteries is a significant challenge that must be overcome. This study presents a new SOH estimation approach based on hybrid Grey Wolf Optimization (GWO) with Bayesian Regularized Neural Networks (BRNN). The approach utilizes health features (HFs) extracted from the battery charging-discharging process. Selected external voltage and current characteristics from the charging-discharging process serve as HFs to explain the aging mechanism of the batteries. The Pearson correlation coefficient, the Kendall rank correlation coefficient, and the Spearman rank correlation coefficient are then employed to select HFs that have a high degree of association with battery capacity. In this paper, GWO is introduced as a method for optimizing and selecting appropriate hyper-p parameters for BRNN. GWO-BRNN updates the population through mutation, crossover, and screening operations to obtain the globally optimal solution and improve the ability to conduct global searches. The validity of the proposed technique was assessed by examining the NASA battery dataset. Based on the simulation results, the presented approach demonstrates a higher level of accuracy. The proposed GWO-BRNN-based SOH estimation achieves estimate assessment indicators of less than 1%, significantly lower than the estimated results obtained by existing approaches. The proposed framework helps develop electric vehicle battery prognostics and health management for the widespread use of eco-friendly and reliable electric transportation.

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“…Grey wolves ritually hunt. Their hunting phases are [50] (1) Approaching the prey. (2) Circling and pestering the victim to cease moving.…”

Section: 𝑟 = 𝑟 − 𝐿 𝐿 + 𝛳𝐼 𝐿 𝑒mentioning

confidence: 99%

Data-Driven GWO-BRNN-Based SOH Estimation of Lithium-Ion Batteries in EVs for Their Prognostics and Health Management

Waseem,

Huang,

Wong

et al. 2023

Mathematics

View full text Add to dashboard Cite

show abstract

“…The remaining wolves are referred to as the ω wolves (lowest ranking). The grey wolf hierarchy plays an important role in predation, with α wolves leading the pack to surround the prey, β wolves and δ wolves attacking the prey, and ω wolves helping to attack the prey and eventually capture it [49]. The mathematical model of the GWO algorithm consists of track hunting, encircling prey, and attacking the prey.…”

Section: The Tuned P-pi Cascade Controller 41 Gwo-algorithm-based Tun...mentioning

confidence: 99%

Grey-Wolf-Optimization-Algorithm-Based Tuned P-PI Cascade Controller for Dual-Ball-Screw Feed Drive Systems

Liu,

Lu,

Yonezawa

et al. 2023

Mathematics

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Dual-ball-screw feed drive systems (DBSFDSs) are designed for most high-end manufacturing equipment. However, the mismatch between the dynamic characteristic parameters (e.g., stiffness and inertia) and the P-PI cascade control method reduces the accuracy of the DBSFDSs owing to the structural characteristic changes in the motion. Moreover, the parameters of the P-PI cascade controller of the DBSFDSs are always the same even though the two axes have different dynamic characteristics, and it is difficult to tune two-axis parameters simultaneously. A new application of the combination of the grey wolf optimization (GWO) algorithm and the P-PI cascade controller is presented to solve these problems and enhance the motion performance of DBSFDSs. The novelty is that the flexible coupling model and dynamic stiffness obtained from the motor current can better represent the two-axis coupling dynamic characteristics, and the GWO algorithm is used to adjust the P-PI controller parameters to address variations in the positions of the moving parts and reflect characteristic differences between the two axes. Comparison of simulation and experimental results validated the superiority of the proposed controller over existing ones in practical applications, showing a decrease in the tracking error of the tool center and non-synchronization error of over 34% and 39%, respectively.

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Electrical line fault prediction using a novel grey wolf optimization algorithm based on multilayer perceptron

Zhang

2024

Adv Control Appl

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Grey wolf optimization algorithm (GWO) has achieved great results in the optimization of neural network parameters. However, it has some problems such as insufficient precision, poor robustness, weak searching ability and easy to fall into local optimal solution. Therefore, a grey wolf optimization algorithm combining Levy flight and nonlinear inertia weights (LGWO) is proposed in this paper. The combination of Levy flight and nonlinear inertia weight is to improve the search efficiency and solve the problem that the search ability is weak and it is easy to fall into the local optimal solution. In summary, LGWO solves the problems of insufficient precision, poor robustness, weak searching ability and easy to fall into local optimal. This paper uses Congress on Evolutionary Computation benchmark function and combines algorithms with neural network for power line fault classification prediction to verify the effectiveness of each strategy improvement in LGWO and its comparison with other excellent algorithms (sine cosine algorithm, tree seed algorithm, wind driven optimization, and gravitational search algorithm). In the combination of neural networks and optimization algorithms, the accuracy of LGWO has been improved compared to the basic GWO, and LGWO has achieved the best performance in multiple algorithm comparisons.

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Levy Flight-Based Improved Grey Wolf Optimization: A Solution for Various Engineering Problems

Cited by 8 publications

References 44 publications

Data-Driven GWO-BRNN-Based SOH Estimation of Lithium-Ion Batteries in EVs for Their Prognostics and Health Management

Data-Driven GWO-BRNN-Based SOH Estimation of Lithium-Ion Batteries in EVs for Their Prognostics and Health Management

Grey-Wolf-Optimization-Algorithm-Based Tuned P-PI Cascade Controller for Dual-Ball-Screw Feed Drive Systems

Electrical line fault prediction using a novel grey wolf optimization algorithm based on multilayer perceptron

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