Discrete State Transition Algorithm for Unconstrained Integer Optimization Problems

TL;DR

This paper introduces a discrete state transition algorithm for unconstrained integer optimization, featuring novel operators and a dynamic adjustment strategy, demonstrating adaptability across various combinatorial problems.

Contribution

It proposes a new intelligent optimization algorithm with specific operators and a risk-restoration strategy, showing improved performance and versatility for unconstrained integer problems.

Findings

Effective in solving various combinatorial problems

Demonstrates high probability of capturing global solutions

Shows adaptability and flexibility across problem types

Abstract

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.