Double-Swarm Grey Wolf Optimizer with Covariance and Dimension Learning for Engineering Optimization Problems

Grey Wolf Optimizer (GWO) has been widely applied in many fields due to its advantages of fast convergence speed, simple parameter settings, and easy implementation. However, with the deepening of algorithm research, GWO has also exposed some problems, such as being prone to becoming stuck in local optima and insufficient convergence accuracy. To address the above issues, a double-swarm Grey Wolf Optimizer with covariance and dimension learning (CDL-DGWO) is proposed. In the CDL-DGWO, firstly, chaotic grouping is used to divide the grey wolf population into two sub-swarms, forming a symmetric cooperative search framework, thereby improving the diversity of the population. Meanwhile, covariance and dimension learning strategies are utilized to improve the hunting behavior of grey wolves; the global search capability and the stability of the algorithm are thereby enhanced. Moreover, CDL-DGWO is validated on 23 benchmark problems and the CEC2017 test set. The results indicate that the CDL-DGWO algorithm outperforms swarm intelligence optimization algorithms such as Particle Swarm Optimization (PSO), Moth Flame Optimization (MFO), and other variants of GWO. Finally, the CDL-DGWO algorithm addresses three engineering design problems that are representative of real-world scenarios. The statistical analysis of the experimental outcomes demonstrates the feasibility and practicality of the proposed methodology.