Sustainable Electrification—Advances and Challenges in Electrical-Distribution Networks: A Review

Sustainability, Год журнала: 2024, Номер 16(2), С. 698 - 698

Опубликована: Янв. 12, 2024

This paper provides a thorough exploration of the evolution and contemporary trends in electrical-distribution networks, with focus on smart grids context Industry 4.0. Beginning traditional components electrical grids, study highlights transition towards sustainable energy sources integration renewables. Key include economic operation, application distributed resources, significance photovoltaic solar energy. The unfolds seven sections, examining smart-electrical-network architecture, technology progression, efficiency, carbon-emission-reduction challenges, future perspectives, concluding insights. Each section delves into specific layers aspects, such as data management, infrastructure, automation, consumer interaction. intricate role meters their impact management is explored, providing comprehensive overview current state directions networks.

Язык: Английский

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Adaptive Marine Predator Optimization Algorithm (AOMA)-Deep Supervised Learning Classification (DSLC) Based IDS Framework for MANET Security

Intelligent and Converged Networks, Год журнала: 2024, Номер 5(1), С. 1 - 18

Опубликована: Март 1, 2024

Due to the dynamic nature and node mobility, assuring security of Mobile Ad-hoc Networks (MANET) is one difficult challenging tasks today.In MANET, Intrusion Detection System (IDS) crucial because it aids in identification detection malicious attacks that impair network's regular operation.Different machine learning deep methodologies are used for this purpose conventional works ensure increased MANET.However, still has significant flaws, including algorithmic complexity, lower system performance, a higher rate misclassification.Therefore, goal paper create an intelligent IDS framework significantly enhancing MANET through use models.Here, minmax normalization model applied preprocess given cyber-attack datasets normalizing attributes or fields, which increases overall intrusion performance classifier.Then, novel Adaptive Marine Predator Optimization Algorithm (AOMA) implemented choose optimal features improving speed classifier.Moreover, Deep Supervise Learning Classification (DSLC) mechanism utilized predict categorize type based on proper training operations.During evaluation, results proposed AOMA-DSLC methodology validated compared using various measures benchmarking datasets.

Язык: Английский

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Enhanced marine predator algorithm for global optimization and engineering design problems

Advances in Engineering Software, Год журнала: 2023, Номер 184, С. 103517 - 103517

Опубликована: Июнь 28, 2023

Язык: Английский

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

Bioengineering, Год журнала: 2023, Номер 10(4), С. 475 - 475

Опубликована: Апрель 14, 2023

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

Язык: Английский

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A parallel compact Marine Predators Algorithm applied in time series prediction of Backpropagation neural network (BNN) and engineering optimization

Mathematics and Computers in Simulation, Год журнала: 2024, Номер 220, С. 65 - 88

Опубликована: Янв. 19, 2024

Язык: Английский

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Optimizing Gaussian process regression (GPR) hyperparameters with three metaheuristic algorithms for viscosity prediction of suspensions containing microencapsulated PCMs

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Авг. 31, 2024

Abstract Suspensions containing microencapsulated phase change materials (MPCMs) play a crucial role in thermal energy storage (TES) systems and have applications building materials, textiles, cooling systems. This study focuses on accurately predicting the dynamic viscosity, critical thermophysical property, of suspensions MPCMs MXene particles using Gaussian process regression (GPR). Twelve hyperparameters (HPs) GPR are analyzed separately classified into three groups based their importance. Three metaheuristic algorithms, namely genetic algorithm (GA), particle swarm optimization (PSO), marine predators (MPA), employed to optimize HPs. Optimizing four most significant (covariance function, basis standardization, sigma) within first group any algorithms resulted excellent outcomes. All achieved reasonable R-value (0.9983), demonstrating effectiveness this context. The second explored impact including additional, moderate-significant HPs, such as fit method, predict method optimizer. While resulting models showed some improvement over group, PSO-based model exhibited noteworthy enhancement, achieving higher (0.99834). Finally, third was examine potential interactions between all twelve comprehensive approach, employing GA, yielded an optimized with highest level target compliance, reflected by impressive 0.999224. developed cost-effective efficient solution reduce laboratory costs for various systems, from TES management.

Язык: Английский

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Recent Developments in Equilibrium Optimizer Algorithm: Its Variants and Applications

Archives of Computational Methods in Engineering, Год журнала: 2023, Номер 30(6), С. 3791 - 3844

Опубликована: Апрель 12, 2023

Язык: Английский

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Analysis of Marine Predators Algorithm using BIAS toolbox and Generalized Signature Test

Alexandria Engineering Journal, Год журнала: 2024, Номер 95, С. 38 - 49

Опубликована: Март 29, 2024

The Marine Predators Algorithm (MPA) is a prominent Nature-Inspired Optimization (NIOA) that has garnered significant research interest due to its effectiveness. It draws inspiration from the foraging behaviors of marine predators, predominantly using Lévy or Brownian approach for strategy. Despite acclaim, structural bias within MPA not been thoroughly investigated, marking gap in current research. This absence targeted forms core rationale behind initiating this study. Structural recently identified NIOAs, causing population revisit specific regions search space without gaining new information. As result, it may lead increased computational costs and slow down rate convergence. Therefore, identifying essential better understand mechanism MPA. To ascertain presence any bias, two introduced models are employed: BIAS toolbox Generalized Signature Test. These examinations reveal notable MPA, towards center space. Also, possible future directions discussed. Our findings provide valuable insights into dynamics algorithm, fostering development new, unbiased, efficient algorithms.

Язык: Английский

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Enhancing Speaker Recognition Models with Noise-Resilient Feature Optimization Strategies

Acoustics, Год журнала: 2024, Номер 6(2), С. 439 - 469

Опубликована: Май 14, 2024

This paper delves into an in-depth exploration of speaker recognition methodologies, with a primary focus on three pivotal approaches: feature-level fusion, dimension reduction employing principal component analysis (PCA) and independent (ICA), feature optimization through genetic algorithm (GA) the marine predator (MPA). study conducts comprehensive experiments across diverse speech datasets characterized by varying noise levels counts. Impressively, research yields exceptional results different classifiers. For instance, TIMIT babble dataset (120 speakers), fusion achieves remarkable identification accuracy 92.7%, while various techniques combined K nearest neighbor (KNN) linear discriminant (LD) classifiers result in verification equal error rate (SV EER) 0.7%. Notably, this 93.5% SV EER 0.13% (630 speakers) using KNN classifier optimization. On white 630 accuracies 93.3% 83.5%, along values 0.58% 0.13%, respectively, were attained utilizing PCA (PCA-MPA) Furthermore, voxceleb1 dataset, PCA-MPA 95.2% 1.8%. These findings underscore significant enhancement computational speed performance facilitated strategies.

Язык: Английский

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A comprehensive survey of honey badger optimization algorithm and meta-analysis of its variants and applications

Franklin Open, Год журнала: 2024, Номер 8, С. 100141 - 100141

Опубликована: Авг. 10, 2024

Metaheuristic algorithms are commonly used in solving complex and NP-hard optimization problems various fields. These have become popular because of their ability to explore exploit solutions problem domains. Honey Badger Algorithm (HBA) is a population-based metaheuristic algorithm inspired by the dynamic hunting strategy honey badgers, utilizing digging-seeking techniques. Since its introduction 2020, HBA has garnered widespread attention been applied across This review aims comprehensively survey improvement application problems. Additionally, conducts meta-analysis HBA's improvements, hybridization since introduction. According result survey, 52 studies presented improved using chaotic maps, levy flight mechanism, adaptive mechanisms, transfer functions, multi-objective mechanism opposition based learning techniques, 20 hybrid with other metaheuristics 101 uses original for wide acceptance within research community stems from straightforwardness, ease use, efficient computational time, accelerated convergence speed, high efficacy, capability address different kind issues, distinguishing it well-known approches presented.

Язык: Английский

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