A novel hybrid biological optimisation algorithm for tackling reservoir optimal operation problem
Year: 2025
Type: article
Abstract: This study introduces the Hybrid Grey-Wolf-Coati Optimiser (HGWCO), a novel metaheuristic algorithm designed for solving constrained optimisation problems. HGWCO integrates the hierarchical leadership structure of the Grey Wolf Optimiser (GWO) with the dynamic population search behavior of the Coati Optimisation Algorithm (CoatiOA), addressing the critical challenge of balancing global exploration and local exploitation in high-dimensional optimisation problems. To evaluate its effectiveness, HGWCO was tested on 10 benchmark functions from the CEC2020 suite and four real-world engineering optimisation problems, including reservoir operation. The results show that HGWCO ranked first in 19 out of 50 CEC2020 test scenarios and demonstrated stable performance in four real-world engineering problems, maintaining consistency in optimal values, mean, and variance. It also outperformed 25 algorithms in tasks like pressure vessel design (PVD) and the traveling salesman problem (TSP). In reservoir operation optimisation, HGWCO surpassed compared metaheuristics, ensuring stable convergence with more practical optimisation results.
Source: Ain Shams Engineering Journal
Authors: Xinlong Le, Kang Ling, Liwei Zhou, Yunliang Wen
Institution: Huazhong University of Science and Technology
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,200
Award: G8703871023
OpenAlex ID: W4408459558