Volitive Grey Wolf Optimizer

Swarm-based metaheuristics have become the most prominent method for solving optimization problems. Several operators already proposed in the literature can also be reused to expand the current metaheuristics. We present in this paper the Volitive Grey Wolf Optimizer (VGWO), a Grey Wolf Optimizer variant created by the addition of the collective volitive movement proposed in Fish School Search. The Volitive operator allows a self-regulated balance between exploration and exploitation that generates diversity when necessary. We evaluate the performance of VGWO and five other metaheuristics by simulating them in ten different problems. VGWO has overcome in most cases compared to other well-known metaheuristics. Therefore, we found that by including a self-regulating operator as the volitive collective, we can improve the quality of results provided by GWO.