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

Sustainability, Journal Year: 2024, Volume and Issue: 16(2), P. 698 - 698

Published: Jan. 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.

Language: Английский

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Enhanced marine predator algorithm for global optimization and engineering design problems

Advances in Engineering Software, Journal Year: 2023, Volume and Issue: 184, P. 103517 - 103517

Published: June 28, 2023

Language: Английский

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

Bioengineering, Journal Year: 2023, Volume and Issue: 10(4), P. 475 - 475

Published: April 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.

Language: 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, Journal Year: 2024, Volume and Issue: 220, P. 65 - 88

Published: Jan. 19, 2024

Language: 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, Journal Year: 2024, Volume and Issue: 14(1)

Published: Aug. 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.

Language: Английский

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Recent Developments in Equilibrium Optimizer Algorithm: Its Variants and Applications

Archives of Computational Methods in Engineering, Journal Year: 2023, Volume and Issue: 30(6), P. 3791 - 3844

Published: April 12, 2023

Language: Английский

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

Alexandria Engineering Journal, Journal Year: 2024, Volume and Issue: 95, P. 38 - 49

Published: March 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.

Language: Английский

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A Novel Routing Protocol for UWSN Using Energy Efficient Marine Predator Optimization Technique

Lecture notes in electrical engineering, Journal Year: 2025, Volume and Issue: unknown, P. 333 - 344

Published: Jan. 1, 2025

Language: Английский

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Evolutionary Cost Analysis and Computational Intelligence for Energy Efficiency in Internet of Things-Enabled Smart Cities: Multi-Sensor Data Fusion and Resilience to Link and Device Failures

Smart Cities, Journal Year: 2025, Volume and Issue: 8(2), P. 64 - 64

Published: April 9, 2025

This work presents an innovative, energy-efficient IoT routing protocol that combines advanced data fusion grouping and strategies to effectively tackle the challenges of management in smart cities. Our employs hierarchical Data Fusion Head (DFH), relay DFHs, marine predators algorithm, latter which is a reliable metaheuristic algorithm incorporates fitness function optimizes parameters such as how closely Sensor Nodes (SNs) group (DFG) are gathered together, distance sink node, proximity SNs within group, remaining energy (RE), Average Scale Building Occlusions (ASBO), Primary DFH (PDFH) rotation frequency. A key innovation our approach introduction techniques minimize redundant transmissions enhance quality DFG. By consolidating from multiple using algorithms, reduces volume transmitted information, leading significant savings. supports both direct routing, where fused flow straight multi-hop PDF chosen based on influential cost considers RE, ASBO. Given proposed efficient failure recovery strategies, redundancy management, techniques, it enhances overall system resilience, thereby ensuring high performance even unforeseen circumstances. Thorough simulations comparative analysis reveal protocol’s superior across metrics, namely, network lifespan, consumption, throughput, average delay. When compared most recent relevant protocols, including Particle Swarm Optimization-based clustering (PSO-EEC), linearly decreasing inertia weight PSO (LDIWPSO), Optimized Fuzzy Clustering Algorithm (OFCA), Novel PSO-based Protocol (NPSOP), achieves very promising results. Specifically, extends lifespan by 299% over PSO-EEC, 264% LDIWPSO, 306% OFCA, 249% NPSOP. It also consumption 254% relative 247% against 253% The throughput improvements reach 67% 59% 53% 50% fusing optimizing sets new benchmark for DFG, offering robust solution diverse deployments.

Language: Английский

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

International Journal of Imaging Systems and Technology, Journal Year: 2023, Volume and Issue: 34(2)

Published: Nov. 22, 2023

Abstract 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 pretrained deep learning models. Ten optimization techniques, including poor and rich optimization, path finder algorithm, Henry gas solubility Harris hawks atom search manta‐ray foraging equilibrium optimizer, slime mold generalized normal distribution marine predator used determine optimal support vector machine. Moreover, network selection was select An online X‐ray scan dataset validate proposed approach. The results suggest that automatic feature framework has high rate 97.7%. comparative analysis further validates credibility in COVID‐19 other classifications, suggesting approach can help doctors clinical practice.

Language: Английский

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