In recent years, large-scale optimization problems have become increasingly common in various fields, such as machine learning and data analysis, generating increased references to both computational cost and accurate solutions. The Grey Wolf Optimizer (GWO) is an efficient collective intelligence algorithm; however, its performance may be limited when high-permissive problems are allowed or in environments with strong multimodal landscapes. In this paper, we propose the Parallel Balanced Grey Wolf Optimizer (ParallelBGWO), a parallel extension of GWO that aims to improve the balance between exploration and exploitation of the search space. The proposed algorithm divides the total population into multiple subpopulations, which cooperate through information exchange. This cooperation reduces the probability of premature convergence and enhances the global search. The parallel implementation exploits modern multi-core computing architectures, achieving a significant reduction in execution time, while at the same time maintaining or improving the quality of the final solutions. Experimental evaluations on high-dimensional benchmark functions show that ParallelBGWO exhibits faster convergence and a reduced number of objective function evaluations compared to the classical version of GWO and other prominent methods. The results highlight ParallelBGWO as an efficient approach for demanding global optimization problems.
Kyrou et al. (Mon,) studied this question.