Discrete state transition algorithm for unconstrained integer optimization problems
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. HighlightsA systematic formulation of discrete state transition algorithm is firstly proposed, including the state space representation and five key elements.A dynamic adjustment strategy called "risk and restoration in probability" is designed to improve the ability to escape from local optima.The proposed algorithm is successfully integrated with several classical integer optimization problems.
Neurocomputing