  A recently new intelligent optimization algorithm called discrete state
transition algorithm is considered in this study, for solving unconstrained
integer optimization problems. Firstly, some key elements for discrete state
transition algorithm are summarized to guide its well development. Several
intelligent operators are designed for local exploitation and global
exploration. Then, a dynamic adjustment strategy ``risk and restoration in
probability" is proposed to capture global solutions with high probability.
Finally, numerical experiments are carried out to test the performance of the
proposed algorithm compared with other heuristics, and they show that the
similar intelligent operators can be applied to ranging from traveling salesman
problem, boolean integer programming, to discrete value selection problem,
which indicates the adaptability and flexibility of the proposed intelligent
elements.
