An Investigation of Hybrid Tabu Search for the Traveling Salesman Problem

  • Dan Xu
  • Thomas Weise
  • Yuezhong Wu
  • Jörg Lässig
  • Raymond Chiong
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 562)


The Traveling Salesman Problem (TSP) is one of the most well-known problems in combinatorial optimization. Due to its \({\mathcal {NP}}\)-hardness, research has focused on approximate methods like metaheuristics. Tabu Search (TS) is a very efficient metaheuristic for combinatorial problems. We investigate four different versions of TS with different tabu objects and compare them to the Lin-Kernighan (LK) heuristic as well as the recently developed Multi-Neighborhood Search (MNS). LK is currently considered to be the best approach for solving the TSP, while MNS has shown to be highly competitive. We then propose new hybrid algorithms by hybridizing TS with Evolutionary Algorithms and Ant Colony Optimization. These hybrids are compared to similar hybrids based on LK and MNS. This paper presents the first statistically sound and comprehensive comparison taking the entire optimization processes of (hybrid) TS, LK, and MNS into consideration based on a large-scale experimental study. We show that our new hybrid TS algorithms are highly efficient and comparable to the state-of-the-art algorithms along this line of research.


Traveling Salesman Problem Tabu Search Evolutionary Algorithms Ant Colony Optimization Memetic Algorithms 



We acknowledge support from the Fundamental Research Funds for the Central Universities, the National Natural Science Foundation of China under Grant 6115 0110488, Special Financial Grant 201104329 from the China Postdoctoral Science Foundation, the Chinese Academy of Sciences (CAS) Fellowship for Young International Scientists 2011Y1GB01, and the European Union 7th Framework Program under Grant 247619. The experiments reported in this paper were executed on the supercomputing system in the Supercomputing Center of University of Science and Technology of China.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Dan Xu
    • 1
  • Thomas Weise
    • 1
  • Yuezhong Wu
    • 1
  • Jörg Lässig
    • 2
  • Raymond Chiong
    • 3
  1. 1.Joint USTC-Birmingham Research Institute in Intelligent Computation and Its Applications (UBRI), School of Computer Science and TechnologyUniversity of Science and Technology of ChinaHefeiChina
  2. 2.Department of Computer Science, Enterprise Application Development GroupUniversity of Applied Sciences Zittau/GörlitzGörlitzGermany
  3. 3.School of Design, Communication and IT, Faculty of Science and ITThe University of NewcastleCallaghanAustralia

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