Multilevel threshold image segmentation for COVID-19 chest radiography: A framework using horizontal and vertical multiverse optimization

Computers in Biology and Medicine, Год журнала: 2022, Номер 146, С. 105618 - 105618

Опубликована: Май 18, 2022

Язык: Английский

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Geyser Inspired Algorithm: A New Geological-inspired Meta-heuristic for Real-parameter and Constrained Engineering Optimization

Journal of Bionic Engineering, Год журнала: 2023, Номер 21(1), С. 374 - 408

Опубликована: Сен. 26, 2023

Язык: Английский

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Young’s double-slit experiment optimizer : A novel metaheuristic optimization algorithm for global and constraint optimization problems

Computer Methods in Applied Mechanics and Engineering, Год журнала: 2022, Номер 403, С. 115652 - 115652

Опубликована: Ноя. 4, 2022

Язык: Английский

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A Global Best-guided Firefly Algorithm for Engineering Problems

Journal of Bionic Engineering, Год журнала: 2023, Номер 20(5), С. 2359 - 2388

Опубликована: Май 17, 2023

Язык: Английский

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Slime Mould Algorithm: A Comprehensive Survey of Its Variants and Applications

Archives of Computational Methods in Engineering, Год журнала: 2023, Номер 30(4), С. 2683 - 2723

Опубликована: Янв. 12, 2023

Язык: Английский

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Simulated annealing-based dynamic step shuffled frog leaping algorithm: Optimal performance design and feature selection

Neurocomputing, Год журнала: 2022, Номер 503, С. 325 - 362

Опубликована: Июнь 28, 2022

Язык: Английский

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SFE: A Simple, Fast, and Efficient Feature Selection Algorithm for High-Dimensional Data

IEEE Transactions on Evolutionary Computation, Год журнала: 2023, Номер 27(6), С. 1896 - 1911

Опубликована: Янв. 23, 2023

In this article, a new feature selection (FS) algorithm, called simple, fast, and efficient (SFE), is proposed for high-dimensional datasets. The SFE algorithm performs its search process using agent two operators: 1) nonselection 2) selection. It comprises phases: exploration exploitation. the phase, operator global in entire problem space irrelevant, redundant, trivial, noisy features changes status of from selected mode to nonselected mode. exploitation searches with high impact on classification results successful FS However, after reducing dimensionality dataset, performance cannot be increased significantly. these situations, an evolutionary computational method could used find more subset reduced space. To overcome issue, article proposes hybrid SFE-PSO (particle swarm optimization) optimal subset. efficiency effectiveness are compared 40 Their performances were six recently algorithms. obtained indicate that algorithms significantly outperform other can as effective selecting

Язык: Английский

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Hierarchical Harris hawks optimizer for feature selection

Journal of Advanced Research, Год журнала: 2023, Номер 53, С. 261 - 278

Опубликована: Янв. 20, 2023

Feature selection is a typical NP-hard problem. The main methods include filter, wrapper-based, and embedded methods. Because of its characteristics, the wrapper method must swarm intelligence algorithm, performance in feature closely related to algorithm's quality. Therefore, it essential choose design suitable algorithm improve based on wrapper. Harris hawks optimization (HHO) superb approach that has just been introduced. It high convergence rate powerful global search capability but an unsatisfactory effect dimensional problems or complex problems. we introduced hierarchy HHO's ability deal with selection. To make obtain good accuracy fewer features run faster selection, improved HHO named EHHO. On 30 UCI datasets, (EHHO) can achieve very classification less running time features. We first conducted extensive experiments 23 classical benchmark functions compared EHHO many state-of-the-art metaheuristic algorithms. Then transform into binary (bEHHO) through conversion function verify extraction data sets. Experiments show better speed minimum than other peers. At same time, HHO, significantly weakness dealing functions. Moreover, datasets repository, bEHHO comparative Compared original bHHO, excellent also bHHO time.

Язык: Английский

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A novel reinforcement learning based tuna swarm optimization algorithm for autonomous underwater vehicle path planning

Mathematics and Computers in Simulation, Год журнала: 2023, Номер 209, С. 55 - 86

Опубликована: Фев. 10, 2023

Язык: Английский

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Fault diagnosis using variational autoencoder GAN and focal loss CNN under unbalanced data

Structural Health Monitoring, Год журнала: 2024, Номер unknown

Опубликована: Июль 24, 2024

For the poor model generalization and low diagnostic efficiency of fault diagnosis under imbalanced distributions, a novel method using variational autoencoder generation adversarial network improved convolutional neural network, named VGAIC-FDM, is proposed in this paper. First, to capture local features vibration signals, continuous wavelet transform employed convert original one-dimensional signals into time–frequency images. Second, for data dimensionality reduction simplification, images are processed grayscale generate single-channel Then, sample augmentation performed on balance dataset by network. Finally, generated fused trained focus-loss-optimized CNN classifier achieve unbalanced conditions. The experimental results show that VGAIC-FDM effectively captures potential spatial distribution real samples alleviates impact caused inconsistent difficulty classification. As result, it enhances performance when dealing with datasets, leading higher accuracy F1-score values.

Язык: Английский

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