.png){kind=icon}
.png){kind=icon}
Return
Grey Wolf Optimization Algorithm Based on Follow-Controlled Learning Strategy
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2023.3314514.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This paper analyzes the OBL strategy's impact on optimizing the GWO algorithm and identifies three shortcomings. the specific limitations of the OBL optimization approach. To address these three shortcomings and enhance both global optimization and local exploration capabilities of GWO, this paper introduces a follow-controlled opposition learning strategy. then, the paper analyzes the control parameter C of the grey wolf algorithm;
investigate its impact on global optimization and local exploration. Based on these properties, a new control parameter C is proposed. The proposed learning strategy and control parameter C are introduced in;
the traditional grey wolf algorithm;
obtain the FCGWO algorithm. Finally, this paper conducts a comparative analysis of the FCGWO algorithm in comparison;
other meta-heuristic algorithms, as well as the enhanced grey wolf algorithm, utilizing 23 benchmark test functions and 2 engineering problems. The results indicate that FCGWO effectively avoids the shortcomings of the traditional OBL, while also outperforming other algorithms significantly in terms of solution quality.
Keywords:
Statistics
Sociology
Optimization
Behavioral sciences
Convergence
Particle swarm optimization
Metaheuristics
Meta-heuristic algorithm
grey wolf optimizer
opposition-based learning
optimization