• DocumentCode
    1813315
  • Title

    An efficient ant colony system for solving the new Generalized Traveling Salesman Problem

  • Author

    Mou, Lianming

  • Author_Institution
    Key Lab. of Numerical Simulation of Sichuan Province, Neijiang Normal Univ., Neijiang, China
  • fYear
    2011
  • fDate
    15-17 Sept. 2011
  • Firstpage
    407
  • Lastpage
    412
  • Abstract
    The Generalized Traveling Salesman Problem (GTSP) is an extension of the classical traveling salesman problem and has many interesting applications. In this paper we present a New Generalized Traveling Salesman Problem (NGTSP), and the current GTSP is only a special case of the NGTSP. To solve effectively the NGTSP, we extend the ant colony system method from TSP to NGTSP. Meanwhile, to improve the quality of solution, a local searching technique is introduced into this method to speed up the convergence, and a novel parameter adaptive technique is also introduced into this method to avoid locking into local minima. Experimental results on numerous TSPlib instances show that the proposed method can deal with the NGTSP problems fairly well, and the developed improvement techniques is significantly effective.
  • Keywords
    optimisation; search problems; travelling salesman problems; ant colony system; local searching technique; new generalized traveling salesman problem; parameter adaptive technique; Algorithm design and analysis; Clustering algorithms; Convergence; Genetic algorithms; Numerical simulation; Partitioning algorithms; Traveling salesman problems; ACS; GTSP; NGTSP; parameter adaptive;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Cloud Computing and Intelligence Systems (CCIS), 2011 IEEE International Conference on
  • Conference_Location
    Beijing
  • Print_ISBN
    978-1-61284-203-5
  • Type

    conf

  • DOI
    10.1109/CCIS.2011.6045099
  • Filename
    6045099