Application of Meta-Heuristic Algorithms for Training Neural Networks and Deep Learning Architectures: A Comprehensive Review

Neural Processing Letters, Год журнала: 2022, Номер 55(4), С. 4519 - 4622

Опубликована: Окт. 31, 2022

Язык: Английский

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Sparrow search algorithm with adaptive t distribution for multi-objective low-carbon multimodal transportation planning problem with fuzzy demand and fuzzy time

Expert Systems with Applications, Год журнала: 2023, Номер 238, С. 122042 - 122042

Опубликована: Окт. 12, 2023

Язык: Английский

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Storm-time ionospheric model over Yunnan-Sichuan area of China based on the SSA-ConvLSTM-BiLSTM algorithm

GPS Solutions, Год журнала: 2025, Номер 29(2)

Опубликована: Март 10, 2025

Язык: Английский

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Optimizing RNNs for EMG Signal Classification: A Novel Strategy Using Grey Wolf Optimization

Bioengineering, Год журнала: 2024, Номер 11(1), С. 77 - 77

Опубликована: Янв. 13, 2024

Accurate classification of electromyographic (EMG) signals is vital in biomedical applications. This study evaluates different architectures recurrent neural networks for the EMG associated with five movements right upper extremity. A Butterworth filter was implemented signal preprocessing, followed by segmentation into 250 ms windows, an overlap 190 ms. The resulting dataset divided training, validation, and testing subsets. Grey Wolf Optimization algorithm applied to gated unit (GRU), long short-term memory (LSTM) architectures, bidirectional networks. In parallel, a performance comparison support vector machines (SVMs) performed. results obtained first experimental phase revealed that all RNN evaluated reached 100% accuracy, standing above 93% achieved SVM. Regarding speed, LSTM ranked as fastest architecture, recording time 0.12 ms, GRU 0.134 Bidirectional showed response 0.2 while SVM had longest at 2.7 second phase, slight decrease accuracy models observed, 98.46% LSTM, 96.38% GRU, 97.63% network. findings this highlight effectiveness speed task.

Язык: Английский

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Novel hybrid success history intelligent optimizer with Gaussian transformation: application in CNN hyperparameter tuning

Cluster Computing, Год журнала: 2023, Номер 27(3), С. 3717 - 3739

Опубликована: Ноя. 6, 2023

Язык: Английский

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Phase prediction of high-entropy alloys based on machine learning and an improved information fusion approach

Computational Materials Science, Год журнала: 2024, Номер 239, С. 112976 - 112976

Опубликована: Март 29, 2024

Язык: Английский

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A Spectral Convolutional Neural Network Model Based on Adaptive Fick’s Law for Hyperspectral Image Classification

Computers, materials & continua/Computers, materials & continua (Print), Год журнала: 2024, Номер 79(1), С. 19 - 46

Опубликована: Янв. 1, 2024

Hyperspectral image classification stands as a pivotal task within the field of remote sensing, yet achieving highprecision remains significant challenge.In response to this challenge, Spectral Convolutional Neural Network model based on Adaptive Fick's Law Algorithm (AFLA-SCNN) is proposed.The (AFLA) constitutes novel metaheuristic algorithm introduced herein, encompassing three new strategies: weight factor, Gaussian mutation, and probability update policy.With adaptive can adjust weights according change in number iterations improve performance algorithm.Gaussian mutation helps avoid falling into local optimal solutions improves searchability algorithm.The strategy exploitability adaptability algorithm.Within AFLA-SCNN model, AFLA employed optimize two hyperparameters SCNN namely, "numEpochs" "miniBatchSize", attain their values.AFLA's initially validated across 28 functions 10D, 30D, 50D for CEC2013 29 CEC2017.Experimental results indicate AFLA's marked superiority over nine other prominent optimization algorithms.Subsequently, was compared with (FLA-SCNN), Harris Hawks Optimization (HHO-SCNN), Differential Evolution (DE-SCNN), (SCNN) Support Vector Machines (SVM) using Indian Pines dataset Pavia University dataset.The experimental show that outperforms models terms Accuracy, Precision, Recall, F1-score University.Among them, Accuracy reached 99.875%, 98.022%.In conclusion, our proposed deemed significantly enhance precision hyperspectral classification.

Язык: Английский

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An enhanced sparrow search swarm optimizer via multi-strategies for high-dimensional optimization problems

Swarm and Evolutionary Computation, Год журнала: 2024, Номер 88, С. 101603 - 101603

Опубликована: Май 18, 2024

Язык: Английский

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Early detection of monkeypox: Analysis and optimization of pretrained deep learning models using the Sparrow Search Algorithm

Results in Engineering, Год журнала: 2024, Номер 24, С. 102985 - 102985

Опубликована: Сен. 30, 2024

Язык: Английский

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Modeling Short-Term Drought for SPEI in Mainland China Using the XGBoost Model

Atmosphere, Год журнала: 2025, Номер 16(4), С. 419 - 419

Опубликована: Апрель 4, 2025

Accurate drought prediction is crucial for optimizing water resource allocation, safeguarding agricultural productivity, and maintaining ecosystem stability. This study develops a methodological framework short-term forecasting using SPEI time series (1979–2020) evaluates three predictive models: (1) baseline XGBoost model (XGBoost1), (2) feature-optimized variant incorporating Pearson correlation analysis (XGBoost2), (3) an enhanced CPSO-XGBoost integrating hybrid particle swarm optimization with dual mechanisms of binary feature selection parameter tuning. Key findings reveal spatiotemporal patterns: temporal-scale dependencies show all models exhibit limited capability at SPEI-1 (R2: 0.32–0.41, RMSE: 0.68–0.79) but achieve progressive accuracy improvement, peaking SPEI-12 where attains optimal performance 0.85–0.90, 0.33–0.43) 18.7–23.4% error reduction versus baselines. Regionally, humid zones (South China/Central-Southern) demonstrate peak (R2 ≈ 0.90, RMSE < 0.35), while arid regions (Northwest Desert/Qinghai-Tibet Plateau) dramatic improvement from 0.35, > 1.0) to 0.85, 52%). Multivariate probability density confirms the model’s robustness through capture nonlinear atmospheric-land interactions reduced parameterization uncertainties via intelligence optimization. The CPSO-XGBoost’s superiority stems synergistic optimization: enhances input relevance adaptive tuning improves computational efficiency, collectively addressing climate variability challenges across diverse terrains. These establish advanced early warning systems, providing critical support climate-resilient management risk mitigation spatiotemporally predictions.

Язык: Английский

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