State transition algorithm for traveling salesman problem

TL;DR

This paper introduces a discrete state transition algorithm with specialized operators for solving the traveling salesman problem, demonstrating superior efficiency and effectiveness compared to existing methods like simulated annealing and ant colony optimization.

Contribution

The paper proposes a novel discrete state transition algorithm with specific operators for TSP, including convergence analysis and no parameter tuning, improving efficiency and search capability.

Findings

Outperforms simulated annealing and ant colony optimization in speed and accuracy

Consumes less time and has stronger search ability

Demonstrates strong adaptability in solving TSP

Abstract

Discrete version of state transition algorithm is proposed in order to solve the traveling salesman problem. Three special operators for discrete optimization problem named swap, shift and symmetry transformations are presented. Convergence analysis and time complexity of the algorithm are also considered. To make the algorithm simple and efficient, no parameter adjusting is suggested in current version. Experiments are carried out to test the performance of the strategy, and comparisons with simulated annealing and ant colony optimization have demonstrated the effectiveness of the proposed algorithm. The results also show that the discrete state transition algorithm consumes much less time and has better search ability than its counterparts, which indicates that state transition algorithm is with strong adaptability.