Bat algorithm (BA), inspired by the foraging behavior of microbats, has become a powerful swarm intelligence method for solving optimization problems over continuous and discrete spaces. Nowadays, it has been successfully applied to solve problems in almost all areas of optimization, as well as engineering practices. Due to the limited applications in discrete structure, this paper carries out an updating review on recent applications of BA for discrete optimization problems. The solution mapping procedures are explained how to convert design variables between continuous domain and discrete domain in order to activate BA to solve discrete problems. To enhance the capability and applicability of BA, other combinatorial problems have been suggested and the potential ways for modification and hybridization are provided as well. The results demonstrated that BA is a promising nature-inspired metaheuristic algorithm for solving a variety of combinatorial problems. Furthermore, it has some significant advantages over other existing algorithms.
