Proposing an Advanced Trending-based Grey Wolf Optimizer for Single-objective Optimization Problems DOI
AmirHossein Mokabberi, Mehdi Golsorkhtabaramiri,

Ramzan Abbasnezhad Varzi

et al.

Published: Feb. 21, 2024

optimization algorithms play a crucial role in solving complex problems various domains. Single-objective aim to discover the most optimal solution for particular objective function, commonly distinguished by single criterion or goal. Grey Wolf optimizer (GWO) is swarm-based algorithm that has gained attention due its simplicity and efficiency problems. In this article, we propose an advanced version of GWO, which referred as Advanced Trending-based (ATGWO), specifically tailored single-objective The motivation behind modification stems from need improve performance metrics original GWO avoid local optimum. By altering algorithm's coefficients, enhance convergence rate, exploration, exploitation abilities. To evaluate proposed ATGWO algorithm, conduct simulations using 7 multimodal benchmark functions. results suggest although excels accuracy, it more delay comparison with GWO. This study paves way future research about algorithms.

Language: Английский

Prediction of hydrogen solubility in aqueous solution using modified mixed effects random forest based on particle swarm optimization for underground hydrogen storage DOI
Grant Charles Mwakipunda,

Norga Alloyce Komba,

Allou Koffi Franck Kouassi

et al.

International Journal of Hydrogen Energy, Journal Year: 2024, Volume and Issue: 87, P. 373 - 388

Published: Sept. 9, 2024

Language: Английский

Citations

11
Two-Stage Neural Network Optimization for Robust Solar Photovoltaic Forecasting DOI Open Access

Jinyeong Oh,

Dayeong So,

Jaehyeok Jo

et al.

Electronics, Journal Year: 2024, Volume and Issue: 13(9), P. 1659 - 1659

Published: April 25, 2024

Neural networks (NNs) have shown outstanding performance in solar photovoltaic (PV) power forecasting due to their ability effectively learn unstable environmental variables and complex interactions. However, NNs are limited practical industrial application the energy sector because optimization of model structure or hyperparameters is a time-consuming task. This paper proposes two-stage NN method for robust PV forecasting. First, dataset divided into training test sets. In set, several models with different numbers hidden layers constructed, Optuna applied select optimal hyperparameter values each model. Next, optimized layer used generate estimation prediction fivefold cross-validation on sets, respectively. Finally, random forest values, from set as input predict final power. As result experiments Incheon area, proposed not only easy but also outperforms models. case point, New-Incheon Sonae dataset—one three various locations—the achieved an average mean absolute error (MAE) 149.53 kW root squared (RMSE) 202.00 kW. These figures significantly outperform benchmarks attention mechanism-based deep learning models, scores 169.87 MAE 232.55 RMSE, signaling advance that expected make significant contribution South Korea’s industry.

Language: Английский

Citations

6
Optimal Design of Renewable Driven Polygeneration System: A Novel Approach Integrating TRNSYS-GenOpt Linkage DOI Creative Commons
Muhammad Shoaib Saleem, Naeem Abas

Cleaner Engineering and Technology, Journal Year: 2024, Volume and Issue: unknown, P. 100856 - 100856

Published: Dec. 1, 2024

Language: Английский

Citations

5
The Effect of Environmental Smart Technology and Renewable Energy on Carbon Footprint: A Sustainability Perspective from the MENA Region DOI Creative Commons
Hind Alofaysan

Energies, Journal Year: 2024, Volume and Issue: 17(11), P. 2624 - 2624

Published: May 29, 2024

This paper looks at the changing impact of renewable energy and green innovation on carbon footprint eight MENA nations between 2000 2020. We investigate this by using panel Q-ARDL model for first time, we find that, with various impacts across different quantiles, a rise in greatly boosts environmental sustainability short run. In long run, effect becomes increasingly more noticeable. According to our analysis, chosen countries quickly embraced storage, solar hydrogen, other technology pathways diversify their mix, which was turning point fight against climate change. Although these factors have been separately examined studies, research merges them into single non-parametric model. is significant as it provides empirical evidence efficiency policies, will guide policymakers stakeholders developing strategies achieve sustainable development goals.

Language: Английский

Citations

2
Innovative Multi-Generation System Producing Liquid Hydrogen and Oxygen: Thermo-Economic Analysis and Optimization Using machine learning optimization technique DOI
Ehsanolah Assareh,

Haider shaker baji,

Le Cao Nhien

et al.

Energy, Journal Year: 2024, Volume and Issue: 311, P. 133458 - 133458

Published: Oct. 13, 2024

Language: Английский

Citations

1
Proposing an Advanced Trending-based Grey Wolf Optimizer for Single-objective Optimization Problems DOI
AmirHossein Mokabberi, Mehdi Golsorkhtabaramiri,

Ramzan Abbasnezhad Varzi

et al.

Published: Feb. 21, 2024

optimization algorithms play a crucial role in solving complex problems various domains. Single-objective aim to discover the most optimal solution for particular objective function, commonly distinguished by single criterion or goal. Grey Wolf optimizer (GWO) is swarm-based algorithm that has gained attention due its simplicity and efficiency problems. In this article, we propose an advanced version of GWO, which referred as Advanced Trending-based (ATGWO), specifically tailored single-objective The motivation behind modification stems from need improve performance metrics original GWO avoid local optimum. By altering algorithm's coefficients, enhance convergence rate, exploration, exploitation abilities. To evaluate proposed ATGWO algorithm, conduct simulations using 7 multimodal benchmark functions. results suggest although excels accuracy, it more delay comparison with GWO. This study paves way future research about algorithms.

Language: Английский

Citations

0