Nowadays the online learning area is actively developing as a part of machine learning. In this regard, there arises the problem of choosing an algorithm that solves the optimization problem with regard to online data processing. Since currently one of the active areas of online learning is ranking, the comparison of several state of art online optimization algorithms for the multi-armed bandit problem in case of online ranking is presented.