I. Introduction
By simulating the underlying mechanisms of self-organized behaviors in nature, bio-inspired optimization algorithms have proved to be promising techniques for highly complicated optimization problems [1], [2]. They differ greatly from conventional mathematical methods in that they are developed and implemented by mimicking biological behaviors in nature. Bio-inspired optimization algorithms have drawn great attention from researchers all over the world, as evidenced by the increasing number of conferences, workshops and papers in this field [1]–[3]. Rigorous theoretical analyses have not been conducted for most of the existing bio-inspired optimization algorithms—such as ant colony optimization (ACO), particle swarm optimization (PSO) and artificial bee colony optimization (ABC)—but the superior performance of these algorithms in terms of intrinsic parallelism, self-organization and strong robustness demonstrate broad prospects for future development [4]–[10]. It is thus important for students to have an overall understanding of bio-inspired optimization algorithms, but their complicated principles and equations often make them difficult for students to understand in traditional classroom settings [11].