The use of optimization algorithms has been growing exponentially in recent years, thanks to their proven efficiency in solving complex problems. This paper describes a comparative analysis of the performance of two nature-based optimization algorithms, the first one Stochastic Fractal Search (SFS) is a method inspired by the natural phenomenon of growth called fractal, this algorithm simulates the form of fractal growth starting with a particle which uses the diffusion property for the creation of random fractals, the second method is the Artificial Gorilla Troop Optimizer (GTO) inspired by the social intelligence of gorilla troops in nature.