This work presents a performance study of the Stochastic Spiral Optimization (SSO) algorithm, an improved version of the deterministic spiral optimization (DSO), and an objective comparison utilizing a group of five population-based optimization algorithms. Performance tests proved that the stochastic spiral dynamic improves the exploration and exploitation features, producing lower errors into several benchmarking functions. Withal, it was found that the SSO's effectiveness has remarkable invariance to its control parameters.