An Inclusive Survey on Marine Predators Algorithm: Variants and Applications

Rai, Rebika; Dhal, Krishna Gopal; Das, Arunita; Ray, Swarnajit

doi:10.1007/s11831-023-09897-x

Cited by 18 publications

(16 citation statements)

References 214 publications

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“…MPA is an optimization technique inspired by nature that adheres to principles that naturally control the best foraging tactics and encounter rates between predators and prey in marine environments. The hunting and foraging strategies of marine predators, such as sharks and dolphins, serve as the basis for MPA [ 43 , 44 ]. The four key phases used for the simulation are predation, reproduction, migration, and exploration.…”

Section: Methodsmentioning

confidence: 99%

WBM-DLNets: Wrapper-Based Metaheuristic Deep Learning Networks Feature Optimization for Enhancing Brain Tumor Detection

et al. 2023

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This study presents wrapper-based metaheuristic deep learning networks (WBM-DLNets) feature optimization algorithms for brain tumor diagnosis using magnetic resonance imaging. Herein, 16 pretrained deep learning networks are used to compute the features. Eight metaheuristic optimization algorithms, namely, the marine predator algorithm, atom search optimization algorithm (ASOA), Harris hawks optimization algorithm, butterfly optimization algorithm, whale optimization algorithm, grey wolf optimization algorithm (GWOA), bat algorithm, and firefly algorithm, are used to evaluate the classification performance using a support vector machine (SVM)-based cost function. A deep-learning network selection approach is applied to determine the best deep-learning network. Finally, all deep features of the best deep learning networks are concatenated to train the SVM model. The proposed WBM-DLNets approach is validated based on an available online dataset. The results reveal that the classification accuracy is significantly improved by utilizing the features selected using WBM-DLNets relative to those obtained using the full set of deep features. DenseNet-201-GWOA and EfficientNet-b0-ASOA yield the best results, with a classification accuracy of 95.7%. Additionally, the results of the WBM-DLNets approach are compared with those reported in the literature.

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Section: Methodsmentioning

confidence: 99%

WBM-DLNets: Wrapper-Based Metaheuristic Deep Learning Networks Feature Optimization for Enhancing Brain Tumor Detection

et al. 2023

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“…The marine predators algorithm (MPA) mimics the best foraging methods and encounters rates of predators and prey in maritime ecosystems. It mimics the hunting and feeding habits of marine predators, such as sharks and dolphins 61,62 . MPA simulations are divided into four major phases: predation, reproduction, migration, and exploration.…”

Section: Methodsmentioning

confidence: 99%

“…It mimics the hunting and feeding habits of marine predators, such as sharks and dolphins. 61,62 MPA simulations are divided into four major phases: predation, reproduction, migration, and exploration. Predation refers to the active pursuit and collection of food supplies.…”

Section: Marine Predators Algorithmmentioning

confidence: 99%

Deep learning network selection and optimized information fusion for enhanced COVID‐19 detection

Ali,

Zafar,

Tanveer

et al. 2023

Int J Imaging Syst Tech

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This study proposes a wrapper‐based technique to improve the classification performance of chest infection (including COVID‐19) detection using X‐rays. Deep features were extracted using pretrained deep learning models. Ten optimization techniques, including poor and rich optimization, path finder algorithm, Henry gas solubility optimization, Harris hawks optimization, atom search optimization, manta‐ray foraging optimization, equilibrium optimizer, slime mold algorithm, generalized normal distribution optimization, and marine predator algorithm, were used to determine the optimal features using a support vector machine. Moreover, a network selection technique was used to select the deep learning models. An online chest infection detection X‐ray scan dataset was used to validate the proposed approach. The results suggest that the proposed wrapper‐based automatic deep learning network selection and feature optimization framework has a high classification rate of 97.7%. The comparative analysis further validates the credibility of the framework in COVID‐19 and other chest infection classifications, suggesting that the proposed approach can help doctors in clinical practice.

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“…The metaheuristic algorithm with relevant forecast model parameters can also increase the forecast performance of time sequence. Recently, different optimization algorithms have been presented for different optimization problems, such as Archimedean optimization algorithm (AOA) [33], Northern goshawk optimization (NGO) [34], Marine predators algorithm (MPA) [35], and Hunter prey optimizer (HPO) [36].…”

Section: Hybrid Modelmentioning

confidence: 99%

Hybrid Neural Network Wind Speed Prediction Based on Secondary Decomposition and Weighted Averaging

Bi,

Bai,

Hou

et al. 2024

Preprint

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The randomicity and fluctuation of the wind speed will influence the precision of the forecast. To improve the precision of wind speed forecast, this paper presents a new method of combined wind speed forecast based on the second decomposition and weighted average. First, the ICEEMDAN decomposition method is used to get different sub-sequences, and then the fuzzy entropy is used to judge the degree of confusion of the sub-sequences. In this paper, the ARIMA model is used to predict the minimum fuzzy entropy. And the other subsequences are decomposed by BPNN, VMD and predicted by NAR and BP neural network with suitable weighting ratio for weighted average and PSO-LSTM neural network respectively, and ultimately all the predicted values are superimposed to get the final prediction. Experiments were conducted using three datasets and eight comparison models to verify the validity of this model. The prediction analysis was carried out using the actual measured data of a wind farm in Inner Mongolia, and the results indicated that (1) using fuzzy entropy can effectively improve the prediction precision; (2) the prediction accuracy of the combined prediction method of neural network based on secondary decomposition was greatly improved and the prediction results were more reliable; (3) Decompose one of the subsequences with VMD, predict it with NAR and BP neural network, and choose appropriate weight ratio for weighted average prediction will achieve better prediction results; (4) the root mean square error (RMSE) of the hybrid model on the three wind speed 1 datasets were 0.28777, 0.22786 and 0.17128, which are lower than the comparison values of other models. So, it is workable to use this hybrid model in wind speed prediction.

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An Inclusive Survey on Marine Predators Algorithm: Variants and Applications

Cited by 18 publications

References 214 publications

WBM-DLNets: Wrapper-Based Metaheuristic Deep Learning Networks Feature Optimization for Enhancing Brain Tumor Detection

WBM-DLNets: Wrapper-Based Metaheuristic Deep Learning Networks Feature Optimization for Enhancing Brain Tumor Detection

Deep learning network selection and optimized information fusion for enhanced COVID‐19 detection

Hybrid Neural Network Wind Speed Prediction Based on Secondary Decomposition and Weighted Averaging

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