  In this chapter we give an overview of the consensus-based global
optimization algorithm and its recent variants. We recall the formulation and
analytical results of the original model, then we discuss variants using
component-wise independent or common noise. In combination with mini-batch
approaches those variants were tailored for machine learning applications.
Moreover, it turns out that the analytical estimates are dimension independent,
which is useful for high-dimensional problems. We discuss the relationship of
consensus-based optimization with particle swarm optimization, a method widely
used in the engineering community. Then we survey a variant of consensus-based
optimization that is proposed for global optimization problems constrained to
hyper-surfaces. We conclude the chapter with remarks on applications, preprints
and open problems.
