Large scale salp-based grey wolf optimization for feature selection and global optimization

Qaraad, Mohammed; Amjad, Souad; Hussein, Nazar K.; Elhosseini, Mostafa A.

doi:10.1007/s00521-022-06921-2

Cited by 38 publications

(19 citation statements)

References 47 publications

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“…The goal of feature selection is to eliminate these unnecessary features from the model before training to increase the model’s success. Many redundant or irrelevant features increase the computational burden (Tiwari and Chaturvedi 2022 ), resulting in the “curse of dimensionality.” Feature selection (FS) helps select the optimal classifier by choosing the most relevant features to decision-making (Qaraad et al 2022 ). The number of possible solutions exponentially increases as the number of features increases in FS, which is an NP-hard combinatorial problem.…”

Section: Methodsmentioning

confidence: 99%

“…Feature selection methods fall into three main categories: (1) filters, (2) wrappers, and (3) hybrid or embedded methods (Hancer et al 2022 ; Tiwari and Chaturvedi 2022 ). In conjunction with statistical data analysis metrics such as correlation and distance, filter-based methods such as principal component analysis, F-scores, and information gains identify subsets of features in the data (Qaraad et al 2022 ). Despite their speed, the methods do not depend on the learning algorithm.…”

Section: Methodsmentioning

confidence: 99%

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Deep neural network prediction of modified stepped double-slope solar still with a cotton wick and cobalt oxide nanofluid

Sharshir

Elhelow

Kabeel

et al. 2022

Environ Sci Pollut Res

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This research work intends to enhance the stepped double-slope solar still performance through an experimental assessment of combining linen wicks and cobalt oxide nanoparticles to the stepped double-slope solar still to improve the water evaporation and water production. The results illustrated that the cotton wicks and cobalt oxide (Co3O4) nanofluid with 1wt% increased the hourly freshwater output (HP) and instantaneous thermal efficiency (ITE). On the other hand, this study compares four machine learning methods to create a prediction model of tubular solar still performance. The methods developed and compared are support vector regressor (SVR), decision tree regressor, neural network, and deep neural network based on experimental data. This problem is a multi-output prediction problem which is HP and ITE. The prediction performance for the SVR was the lowest, with 70 (ml/m2 h) mean absolute error (MAE) for HP and 4.5% for ITE. Decision tree regressor has a better prediction for HP with 33 (ml/m2 h) MAE and almost the same MAE for ITE. Neural network has a better prediction for HP with 28 (ml/m2 h) MAE and a bit worse prediction for ITE with 5.7%. The best model used the deep neural network with 1.94 (ml/m2 h) MAE for HP and 0.67% MAE for ITE.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Deep neural network prediction of modified stepped double-slope solar still with a cotton wick and cobalt oxide nanofluid

Sharshir

Elhelow

Kabeel

et al. 2022

Environ Sci Pollut Res

View full text Add to dashboard Cite

show abstract

“…The embedded methods embed feature selection into the learner training process. The filter techniques select relevant features purely based on the inherent relationships among the features, without requiring the integration of any learning method [ 27 , 28 ]. The Wrapper methods combine a search strategy with a learning method to identify the optimal subset of features for classification performance by utilizing a subset of features obtained through the search strategy to train a classifier [ 29 , 30 ].…”

Section: Related Workmentioning

confidence: 99%

Binary Restructuring Particle Swarm Optimization and Its Application

Zhu,

Liu,

Chen

et al. 2023

Biomimetics

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Restructuring Particle Swarm Optimization (RPSO) algorithm has been developed as an intelligent approach based on the linear system theory of particle swarm optimization (PSO). It streamlines the flow of the PSO algorithm, specifically targeting continuous optimization problems. In order to adapt RPSO for solving discrete optimization problems, this paper proposes the binary Restructuring Particle Swarm Optimization (BRPSO) algorithm. Unlike other binary metaheuristic algorithms, BRPSO does not utilize the transfer function. The particle updating process in BRPSO relies solely on comparison results between values derived from the position updating formula and a random number. Additionally, a novel perturbation term is incorporated into the position updating formula of BRPSO. Notably, BRPSO requires fewer parameters and exhibits high exploration capability during the early stages. To evaluate the efficacy of BRPSO, comprehensive experiments are conducted by comparing it against four peer algorithms in the context of feature selection problems. The experimental results highlight the competitive nature of BRPSO in terms of both classification accuracy and the number of selected features.

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“…To reduce the evaluation cost, a rapid evaluation approach was also developed. To overcome the problems of low precision and global optimization, the convergence speed is accelerated in high-dimensional data where Salp Swarm Optimization (SSO) is combined with GWO (Qaraad et al, 2022). Initially, the leader's position in the chain population is updated using SSO's intensification capabilities.…”

Section: Related Workmentioning

confidence: 99%

An improved binary grey wolf optimizer for constrained engineering design problems

Majumdar,

Bhattacharya,

Mitra

et al. 2023

Expert Systems

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An Improved binary Non‐Linear Convergent Bi‐phase Mutated Grey Wolf Optimizer (IbGWO) is proposed for solving feature selection problems with two main goals reducing irrelevant features and maximizing accuracy. We used stratified ‐fold cross‐validation that performs stratified sampling on the data to avoid overfitting problems. The fitness function used in the proposed algorithm allows choosing the solution with the minimum number of features if more than one feature has the same highest accuracy. When stratified cross‐validation is performed, the split datasets contain the same share of the feature of interest as the actual dataset. During stratified sampling, the cross‐validation result minimizes the generalization error to a considerable extent, with a smaller variance. Feature selection could be seen as an optimization problem that efficiently removes irrelevant data from high‐dimensional data to reduce computation time and improve learning accuracy. This paper proposes an improved Non‐Linear Convergent Bi‐Phase Mutated Binary Grey Wolf Optimizer (IbGWO) algorithm for feature selection. The bi‐phase mutation enhances the rate of exploitation of GWO, where the first mutation phase minimizes the number of features and the second phase adds more informative features for accurate feature selection. A non‐linear tangent trigonometric function is used for convergence to generalize better while handling heterogeneous data. To accelerate the global convergence speed, an inertia weight is added to control the position updating of the grey wolves. Feature‐weighted K‐Nearest Neighbor is used to enhance classification accuracy, where only relevant features are used for feature selection. Experimental results confirm that IbGWO outperforms other algorithms in terms of average accuracy of 0.8716, average number of chosen features of 6.13, average fitness of 0.1717, and average standard deviation of 0.0072 tested on different datasets and in terms of statistical analysis. IbGWO is also benchmarked using unimodal, multimodal, and IEEE CEC 2019 functions, where it outperforms other algorithms in most cases. Three classical engineering design problems are also solved using IbGWO, which significantly outperforms other algorithms. Moreover, the overtaking percentage of the proposed algorithm is .

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Large scale salp-based grey wolf optimization for feature selection and global optimization

Cited by 38 publications

References 47 publications

Deep neural network prediction of modified stepped double-slope solar still with a cotton wick and cobalt oxide nanofluid

Deep neural network prediction of modified stepped double-slope solar still with a cotton wick and cobalt oxide nanofluid

Binary Restructuring Particle Swarm Optimization and Its Application

An improved binary grey wolf optimizer for constrained engineering design problems

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