A Novel Grey Wolf Optimizer for Solving Optimization Problems

摘要

As a well-known bio-inspired optimization algorithm, the gray wolf optimizer mimics the social dominant hierarchy and social interactions of gray wolves in nature. Inspired by this hierarchy, this study attempted to present a novel gray wolf optimizer in which the wolves are classified into four groups, namely alpha, beta, delta, and omega. These classes may include male or female wolves or both. The gender of wolves and their superior classes determine the updating position of wolves in each class. After allocating each wolf to one of the alpha, beta, and delta classes, the position of the other wolves is updated with respect to these classes. To evaluate the performance of the proposed method, a set of benchmark functions were used. The results showed that the proposed gray wolf optimizer outperforms the conventional wolf optimizer in most cases.