Many optimization problems in engineering and science require solutions that are globally optimal. These optimization problems are characterized by the nonconvexity of the feasible domain or the objective function and may involve continuous and/or discrete variables. In this paper we highlight some recent results and discuss current research trends on deterministic and stochastic global optimization and global continuous approaches to discrete optimization.