A Discrete State Transition Algorithm for Generalized Traveling Salesman Problem
Abstract
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.
Keywords
Cite
@article{arxiv.1304.7607,
title = {A Discrete State Transition Algorithm for Generalized Traveling Salesman Problem},
author = {Xiaolin Tang and Chunhua Yang and Xiaojun Zhou and Weihua Gui},
journal= {arXiv preprint arXiv:1304.7607},
year = {2015}
}
Comments
8 pages, 1 figure