TY - GEN
T1 - Kohonen Self-Organizing Map based Route Planning
T2 - 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2021
AU - Guan, Qingshu
AU - Hong, Xiaopeng
AU - Ke, Wei
AU - Zhang, Liangfei
AU - Sun, Guanghui
AU - Gong, Yihong
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - In this paper, we revisit the long-standing Traveling Salesman Problem (TSP) and focus on the challenging, yet practical route planning problem with limited computational resources. We make contributions to TSP, one of the most famous NP-hard problems by providing a new improved approximate solution, which we term TOpology Preserving Self-Organizing Map (TOPSOM). TOPSOM well preserves the topology of the node map to be traversed by maintaining the continuity of nodes and the distances between them. In addition, to satisfy the requirements of convex hull, we design an elastic competitive Hebbian learning rule. TOPSOM can solve large-scale TSPs with high precision and high efficiency with limited computational costs. Extensive experimental results on mainstream route planning benchmarks including TSPLIB and National TSP's show that our method consistently outperforms baseline methods, by up to 7.7% in terms of the Percent Deviation of Mean solution to best known solution.
AB - In this paper, we revisit the long-standing Traveling Salesman Problem (TSP) and focus on the challenging, yet practical route planning problem with limited computational resources. We make contributions to TSP, one of the most famous NP-hard problems by providing a new improved approximate solution, which we term TOpology Preserving Self-Organizing Map (TOPSOM). TOPSOM well preserves the topology of the node map to be traversed by maintaining the continuity of nodes and the distances between them. In addition, to satisfy the requirements of convex hull, we design an elastic competitive Hebbian learning rule. TOPSOM can solve large-scale TSPs with high precision and high efficiency with limited computational costs. Extensive experimental results on mainstream route planning benchmarks including TSPLIB and National TSP's show that our method consistently outperforms baseline methods, by up to 7.7% in terms of the Percent Deviation of Mean solution to best known solution.
UR - https://www.scopus.com/pages/publications/85124333430
U2 - 10.1109/IROS51168.2021.9636025
DO - 10.1109/IROS51168.2021.9636025
M3 - 会议稿件
AN - SCOPUS:85124333430
T3 - IEEE International Conference on Intelligent Robots and Systems
SP - 7969
EP - 7976
BT - 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2021
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 27 September 2021 through 1 October 2021
ER -