Nature-inspired metaheuristic algorithms have become a popular topic among many researchers for the development of novel evolutionary operators based on behaviors found in diverse living organisms in their natural environment. The Cheetah Optimizer (CO) is presented as a metaheuristic algorithm combining different strategies based on the hunting behaviors of the cheetahs in their natural habitat. Despite its capabilities for solving high-complexity optimization problems applicable to real-life optimization schemes, this algorithm faces several challenges, such as a slow convergence of the individual and susceptibility to getting trapped in local optimal zones. This chapter presents a hybridization of the Cheetah Optimizer employing Opposition-Based Learning (OBL) and a diversity control. The dynamical strategies selection combined with mechanisms for controlling the diversity of the population potentializes the exploration and exploitation patterns of the algorithm considerably, getting faster convergence rates and reducing the probability of getting trapped in local optimal solutions.