The use of metaheuristics is widespread for optimization in both scientific and industrial problems due to several reasons, including flexibility, simplicity, and robustness. Grey Wolf Optimizer (GWO) is one of the most recent and popular algorithms in this area. In this work, opposition-based learning (OBL) is combined with GWO to enhance its exploratory behavior while maintaining a fast convergence rate. Spearman's correlation coefficient is used to determine the omega (ω) wolves (wolves with the lowest social status in the pack) on which to perform opposition learning. Instead of opposing all the dimensions in the wolf, a few dimensions of the wolf are selected on which opposition is applied. This assists with avoiding unnecessary exploration and achieving a fast convergence without deteriorating the probability of finding optimum solutions. The proposed algorithm is tested on 23 optimization functions. An extensive comparative study demonstrates the superiority of the proposed method. The source code for this algorithm is available at "https://github.com/dhargupta-souvik/sogwo"

由于具有多种原因，包括灵活性，简单性和鲁棒性，元启发法在科学和工业问题上的优化已广泛使用。灰狼优化器（GWO）是该领域中最新，最流行的算法之一。在这项工作中，基于对立的学习（OBL）与GWO相结合，以增强其探索行为，同时保持快速的收敛速度。Spearman相关系数用于确定ω（ω）在其上进行对立学习的狼（群居中社会地位最低的狼）。而不是对着狼的所有尺寸，而是选择在其上应用了对立的狼的几个尺寸。这有助于避免不必要的探索并实现快速收敛，而不会降低找到最佳解的可能性。该算法在23个优化函数上进行了测试。广泛的比较研究证明了该方法的优越性。该算法的源代码可在“ https://github.com/dhargupta-souvik/sogwo”中找到。