A Discrete State Transition Algorithm for Generalized Traveling Salesman Problem
This is an incremental improvement for researchers and practitioners in combinatorial optimization, specifically addressing GTSP variants.
The authors tackled the Generalized Traveling Salesman Problem (GTSP), an NP-hard combinatorial optimization problem, by proposing a discrete state transition algorithm (DSTA) with a new K-circle local search operator and Double R-Probability update mechanism, which demonstrated better search ability than competitors in experiments.
Generalized traveling salesman problem (GTSP) is an extension of classical traveling salesman problem (TSP), which is a combinatorial optimization problem and an NP-hard problem. In this paper, an efficient discrete state transition algorithm (DSTA) for GTSP is proposed, where a new local search operator named \textit{K-circle}, directed by neighborhood information in space, has been introduced to DSTA to shrink search space and strengthen search ability. A novel robust update mechanism, restore in probability and risk in probability (Double R-Probability), is used in our work to escape from local minima. The proposed algorithm is tested on a set of GTSP instances. Compared with other heuristics, experimental results have demonstrated the effectiveness and strong adaptability of DSTA and also show that DSTA has better search ability than its competitors.