Discrete state transition algorithm for unconstrained integer optimization problems

we focus on a recently new intelligent optimization algorithm called discrete state transition algorithm, for solving integer optimization problems. Firstly, we summarize some key elements for discrete state transition algorithm to guide its well development. Several intelligent operators are designed for local exploitation and global exploration. Then, a dynamic adjustment strategy “risk and restore in probability” is proposed to gain global convergence with high probability. Finally, numerical experiments are carried out to test the effectiveness of the proposed algorithm, 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 robustness and adaptability of the proposed intelligent elements.