Hierarchical multi step Gray Wolf optimization algorithm for energy systems optimization DOI Creative Commons

Idriss Dagal,

AL-Wesabi Ibrahim,

Ambe Harrison

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: March 15, 2025

Gray Wolf Optimization (GWO), inspired by the social hierarchy and cooperative hunting behavior of gray wolves, is a widely used metaheuristic algorithm for solving complex optimization problems in various domains, including engineering design, image processing, machine learning. However, standard GWO can suffer from premature convergence sensitivity to parameter settings. To address these limitations, this paper introduces Hierarchical Multi-Step (HMS-GWO) algorithm. HMS-GWO incorporates novel hierarchical decision-making framework that more closely mimics observed wolf packs, enabling each type (Alpha, Beta, Delta, Omega) execute structured multi-step search process. This approach enhances exploration exploitation, improves solution diversity, prevents stagnation. The performance evaluated on benchmark suite 23 functions, showing 99% accuracy, with computational time 3 s stability score 0.9. Compared other advanced techniques such as GA, PSO, MMSCC-GWO, WCA, CCS-GWO, demonstrates significantly better performance, faster improved accuracy. While suffers convergence, mitigates issue employing process diversity. These results confirm outperforms terms both speed quality, making it promising across domains enhanced robustness efficiency.

Language: Английский

Accurate parameters extraction of photovoltaic models using Lambert W-function collaborated with AI-based Puma optimization method DOI Creative Commons
Rabeh Abbassi, Salem Saidi, Houssem Jerbi

et al.

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 104268 - 104268

Published: Feb. 1, 2025

Language: Английский

Citations

0
Hybrid Artificial Bee Colony and incremental conductance—Algorithm for enhanced MPPT in photovoltaic systems DOI Creative Commons
Ahmed G. Abo‐Khalil, Abdel‐Rahman Al‐Qawasmi, Ali Abou‐Hassan

et al.

Clean Energy Science and Technology, Journal Year: 2025, Volume and Issue: 3(2), P. 335 - 335

Published: March 24, 2025

The growing global demand for electricity necessitates efficient renewable energy solutions, with photovoltaic (PV) systems emerging as a prominent candidate. This study presents novel hybrid Maximum Power Point Tracking (MPPT) algorithm that integrates the Artificial Bee Colony (ABC) optimization method Incremental Conductance (IC) technique, ensuring 100% accurate identification of Global (GMPP) under partial shading conditions. Unlike standalone MPPT methods, proposed approach leverages exploratory capabilities ABC search while utilizing IC fast and precise tracking, achieving convergence time 0.37 s minimal power oscillations 2.7%. Experimental validation demonstrates algorithm’s superior performance, attaining efficiency, significantly outperforming (74%) (99.5%) methods. ABC-IC consistently tracks GMPP, delivering 60 W optimal irradiation (1000 W/m2) surpassing conventional techniques such P&O, FA, PSO in terms speed, robustness, adaptability to dynamic innovative integration bio-inspired deterministic strategies offers highly reliable solution maximizing PV harvesting real-world environments.

Language: Английский

Citations

0
Global peak operation of solar photovoltaic and wind energy systems: Current trends and innovations in enhanced optimization control techniques DOI

Saranya Pulenthirarasa,

Priya Ranjan Satpathy, Vigna K. Ramachandaramurthy

et al.

IFAC Journal of Systems and Control, Journal Year: 2025, Volume and Issue: unknown, P. 100304 - 100304

Published: March 1, 2025

Language: Английский

Citations

0
Hierarchical multi step Gray Wolf optimization algorithm for energy systems optimization DOI Creative Commons

Idriss Dagal,

AL-Wesabi Ibrahim,

Ambe Harrison

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: March 15, 2025

Gray Wolf Optimization (GWO), inspired by the social hierarchy and cooperative hunting behavior of gray wolves, is a widely used metaheuristic algorithm for solving complex optimization problems in various domains, including engineering design, image processing, machine learning. However, standard GWO can suffer from premature convergence sensitivity to parameter settings. To address these limitations, this paper introduces Hierarchical Multi-Step (HMS-GWO) algorithm. HMS-GWO incorporates novel hierarchical decision-making framework that more closely mimics observed wolf packs, enabling each type (Alpha, Beta, Delta, Omega) execute structured multi-step search process. This approach enhances exploration exploitation, improves solution diversity, prevents stagnation. The performance evaluated on benchmark suite 23 functions, showing 99% accuracy, with computational time 3 s stability score 0.9. Compared other advanced techniques such as GA, PSO, MMSCC-GWO, WCA, CCS-GWO, demonstrates significantly better performance, faster improved accuracy. While suffers convergence, mitigates issue employing process diversity. These results confirm outperforms terms both speed quality, making it promising across domains enhanced robustness efficiency.

Language: Английский

Citations

0