IntraClusTSP—An Incremental Intra-Cluster Refinement Heuristic Algorithm for Symmetric Travelling Salesman Problem

The Symmetric Traveling Salesman Problem (sTSP) is an intensively studied NP-hard problem. It has many important real-life applications such as logistics, planning, manufacturing of microchips and DNA sequencing. In this paper we propose a cluster level incremental tour construction method called In...

Full description

Saved in:
Bibliographic Details
Published inSymmetry (Basel) Vol. 10; no. 12; p. 663
Main Authors Kovács, László, Iantovics, László, Iakovidis, Dimitris
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.12.2018
Subjects
Algorithms
Chained Lin-Kernighan refinement algorithm
Cluster analysis
Clustering
Cost control
Decomposition
Dynamic programming
Gene sequencing
Genetic algorithms
Heuristic
Heuristic methods
Intra-cluster Refinement
Linear programming
Logistics
Methods
Mutation
Nearest Neighbour method
optimal number of clusters
Optimization
similar elements
Structural hierarchy
symmetric distance matrix
Symmetric Traveling Salesman Problem
symmetry
Traveling salesman problem
Online AccessGet full text

Cover

More Information
Summary:The Symmetric Traveling Salesman Problem (sTSP) is an intensively studied NP-hard problem. It has many important real-life applications such as logistics, planning, manufacturing of microchips and DNA sequencing. In this paper we propose a cluster level incremental tour construction method called Intra-cluster Refinement Heuristic (IntraClusTSP). The proposed method can be used both to extend the tour with a new node and to improve the existing tour. The refinement step generates a local optimal tour for a cluster of neighbouring nodes and this local optimal tour is then merged into the global optimal tour. Based on the performed evaluation tests the proposed IntraClusTSP method provides an efficient incremental tour generation and it can improve the tour efficiency for every tested state-of-the-art methods including the most efficient Chained Lin-Kernighan refinement algorithm. As an application example, we apply IntraClusTSP to automatically determine the optimal number of clusters in a cluster analysis problem. The standard methods like Silhouette index, Elbow method or Gap statistic method, to estimate the number of clusters support only partitional (single level) clustering, while in many application areas, the hierarchical (multi-level) clustering provides a better clustering model. Our proposed method can discover hierarchical clustering structure and provides an outstanding performance both in accuracy and execution time.
ISSN:2073-8994
2073-8994
DOI:10.3390/sym10120663