Improvement of Artificial Hummingbird Algorithm with Greedy Fitness-Distance Balance Method and Its Application to Load Frequency Control.
- 1Department of Energy Systems Engineering, Karadeniz Technical University, 61830 Trabzon, Türkiye.
- 2Bulancak Vocational School, Giresun University, 28300 Giresun, Türkiye.
Abstract
This paper proposes an improved Artificial Hummingbird Algorithm (AHA) for solving numerical optimization problems. The greedy Fitness-Distance Balance (FDB) selection method is integrated into the guided, territorial, and migration search operators of the AHA. This establishes a balance between exploration and exploitation by enhancing AHA's exploration capabilities. The performance of the proposed FDB-AHA is evaluated using CEC 2017 and 2020 problems which include single-mode, multi-mode, hybrid, and composition functions in search spaces of dimensions 30, 50, and 100. Furthermore, the results of the FDB-AHA are compared with 11 other advanced search algorithms using the Friedman statistical test method on the CEC 2017 and 2020 problems. As a result of these comparisons, the FDB-AHA was found to be the most effective method, while the AHA ranked ninth. These findings confirm the effectiveness of the improvements made in the AHA. Moreover, the FDB-AHA has been used to determine the optimal solutions for load frequency control in three different power systems. In this context, three-term (PID), five-term (FOPID), and six-term (FOPI-FOPD) controllers are optimized and compared with existing studies in the literature for time-domain analysis. Furthermore, load variation and robustness analysis are also performed. Finally, the practicality of the controllers optimized by FDB-AHA is verified using the real-time simulator OP5707. Simulation results show that FDB-AHA is a highly efficient algorithm for solving all optimization problems.
Related
- An Application of Geometric Riemannian Pricing Algorithm for Valuing Catastrophe Bonds in Incomplete Markets: Market-Consistent Financial Entropy and the Split of Frequency and Severity Risk Premia
- A Study on a Sector-Shaped Cantilever Diaphragm for Frequency-Response Improvement of an Acoustic Sensor Based on Piezoresistive Effect in MOSFET
- Three-frequency temporal phase unwrapping using a novel four-fringe-based 1H+1M+2L algorithm
- Quantum similarity estimation with hybrid discrete-continuous encoding: algorithm and application to atomtronics precision measurements
- Evaluation of an Artificial Intelligence and Machine Learning Algorithm to Enhance Angiographic Coronary Visibility in Morbid Obesity: A Retrospective Observational Study
BibTeX
@article{aslanov2026improvement,
title = {{Improvement of Artificial Hummingbird Algorithm with Greedy Fitness-Distance Balance Method and Its Application to Load Frequency Control.}},
author = {Tural Aslanov and Bora Çavdar and Ömür Akyazı and Kenan Yanmaz},
journal = {Biomimetics (Basel, Switzerland)},
year = {2026},
month = oct,
doi = {10.3390/biomimetics11100754},
url = {https://europepmc.org/article/MED/42930306},
note = {CC BY},
}
RIS
TY - JOUR
TI - Improvement of Artificial Hummingbird Algorithm with Greedy Fitness-Distance Balance Method and Its Application to Load Frequency Control.
AU - Tural Aslanov
AU - Bora Çavdar
AU - Ömür Akyazı
AU - Kenan Yanmaz
PY - 2026
DA - 2026/10/19
JO - Biomimetics (Basel, Switzerland)
DO - 10.3390/biomimetics11100754
UR - https://europepmc.org/article/MED/42930306
AB - This paper proposes an improved Artificial Hummingbird Algorithm (AHA) for solving numerical optimization problems. The greedy Fitness-Distance Balance (FDB) selection method is integrated into the guided, territorial, and migration search operators of the AHA. This establishes a balance between exploration and exploitation by enhancing AHA's exploration capabilities. The performance of the proposed FDB-AHA is evaluated using CEC 2017 and 2020 problems which include single-mode, multi-mode, hybrid, and composition functions in search spaces of dimensions 30, 50, and 100. Furthermore, the results of the FDB-AHA are compared with 11 other advanced search algorithms using the Friedman statistical test method on the CEC 2017 and 2020 problems. As a result of these comparisons, the FDB-AHA was found to be the most effective method, while the AHA ranked ninth. These findings confirm the effectiveness of the improvements made in the AHA. Moreover, the FDB-AHA has been used to determine the optimal solutions for load frequency control in three different power systems. In this context, three-term (PID), five-term (FOPID), and six-term (FOPI-FOPD) controllers are optimized and compared with existing studies in the literature for time-domain analysis. Furthermore, load variation and robustness analysis are also performed. Finally, the practicality of the controllers optimized by FDB-AHA is verified using the real-time simulator OP5707. Simulation results show that FDB-AHA is a highly efficient algorithm for solving all optimization problems.
SN - 2313-7673
ER -