A Novel Grey Wolf Optimisation based CNN Classifier for Hyperspectral Image classification

Ladi, Sandeep Kumar; Panda, Geetanjali; Dash, Ratnakar; Ladi, Pradeep Kumar; Dhupar, Rohan

doi:10.1007/s11042-022-12628-2

Cited by 10 publications

(2 citation statements)

References 67 publications

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“…In [28], a new well-organized solid oxide fuel cell (SOFC) stack model identification method was proposed based on GWO. In recent years, it has been extensively favored by researchers and widely tailored for various optimizations, such as parameter optimization [29], image classification [30], and controller design [31]. However, the global search capability of GWO requires an appropriate trade-off between exploration and exploitation.…”

Section: Introductionmentioning

confidence: 99%

A Hybridization Grey Wolf Optimizer to Identify Parameters of Helical Hydraulic Rotary Actuator

et al. 2023

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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. The mutation and crossover principles of the DE algorithm are introduced into the GWO algorithm, and the opposition-based optimization learning technology is combined to update the GWO population to increase the population diversity. The algorithm is then benchmarked against nine typical test functions and compared with other state-of-the-art meta-heuristic algorithms such as particle swarm optimization (PSO), GWO, and DE. The results show that the proposed H-GWO algorithm can provide very competitive results. On this basis, the forgetting factor recursive least squares (FFRLS) method and the proposed H-GWO algorithm are combined to establish a parameter identification algorithm to identify parameters of the helical hydraulic rotary actuator (HHRA) with nonlinearity and uncertainty questions. In addition, the proposed method is verified by practical identification experiments. After comparison with the least squares (LS), recursive least squares (RLS), FFRLS, PSO, and GWO results, it can be concluded that the proposed method (H-GWO) has higher identification accuracy.

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Section: Introductionmentioning

confidence: 99%

A Hybridization Grey Wolf Optimizer to Identify Parameters of Helical Hydraulic Rotary Actuator

et al. 2023

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“…GWO optimized six hyperparameters in CNN to make up for the shortcomings of traditional optimization methods, such as time-consuming and laborious. Experiments showed that the classification accuracy of the algorithm on the specified datasets was more than 99.00% ( Ladi et al., 2022 ). The early diagnosis of Alzheimer’s disease based on CNN was proposed.…”

Section: Introductionmentioning

confidence: 99%

Image classification and identification for rice leaf diseases based on improved WOACW_SimpleNet

Lü

Zhang²,

Zeng³

et al. 2022

Front. Plant Sci.

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In view of the problem that manual selection of hyperparameters may lead to low performance and large consumption of manpower cost of the convolutional neural network (CNN), this paper proposes a nonlinear convergence factor and weight cooperative self-mapping chaos optimization algorithm (WOACW) to optimize the hyperparameters in the identification and classification model of rice leaf disease images, such as learning rate, training batch size, convolution kernel size and convolution kernel number. Firstly, the opposition-based learning is added to the whale population initialization with improving the diversity of population initialization. Then the algorithm improves the convergence factor, increases the weight coefficient, and calculates the self-mapping chaos. It makes the algorithm have a strong ability to find optimization in the early stage of iteration and fast convergence rate. And disturbance is carried out to avoid falling into local optimal solution in the late stage of iteration. Next, a polynomial mutation operator is introduced to correct the current optimal solution with a small probability, so that a better solution can be obtained in each iteration, thereby enhancing the optimization performance of the multimodal objective function. Finally, eight optimized performance benchmark functions are selected to evaluate the performance of the algorithm, the experiment results show that the proposed WOACW outperforms than 5 other common improved whale optimization algorithms. The WOACW_SimpleNet is used to identify rice leaf diseases (rice blast, bacterial leaf blight, brown spot disease, sheath blight and tungro disease), and the experiment results show that the identification average recognition accuracy rate reaches 99.35%, and the F1-score reaches 99.36%.

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Hyperspectral Images: A Succinct Analytical Deep Learning Study

Kumar

Panda

Tripathy

2023

Studies in Big Data

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A Novel Grey Wolf Optimisation based CNN Classifier for Hyperspectral Image classification

Cited by 10 publications

References 67 publications

A Hybridization Grey Wolf Optimizer to Identify Parameters of Helical Hydraulic Rotary Actuator

A Hybridization Grey Wolf Optimizer to Identify Parameters of Helical Hydraulic Rotary Actuator

Image classification and identification for rice leaf diseases based on improved WOACW_SimpleNet

Hyperspectral Images: A Succinct Analytical Deep Learning Study

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