• DocumentCode
    175730
  • Title

    A novel oppositional biogeography-based optimization for combinatorial problems

  • Author

    Qingzheng Xu ; Lemeng Guo ; Na Wang ; Jin Pan ; Lei Wang

  • Author_Institution
    Xi´an Commun. Inst., Xi´an, China
  • fYear
    2014
  • fDate
    19-21 Aug. 2014
  • Firstpage
    412
  • Lastpage
    418
  • Abstract
    In this paper, a novel definition of opposite path is proposed. Its core feature is that the node sequence of candidate paths and the distances between adjacent nodes in the tour are considered simultaneously. In a sense, the path and its corresponding opposite path have the same (or similar, at least) distance from the optimal path in the current population. Based on an accepted framework for employing opposition-based learning, the Oppositional Biogeography-Based Optimization using the Current Optimum, called COOBBO algorithm, is introduced to solve combinatorial problem, such as traveling salesman problems. The performance of COOBBO on 8 benchmark problems is demonstrated and compared with other optimization algorithms. Simulation results illustrate that the excellent performance of our proposed algorithm is attributed to the distinct definition of opposite path.
  • Keywords
    learning (artificial intelligence); travelling salesman problems; COOBBO algorithm; adjacent nodes; candidate paths; combinatorial problems; current optimum; node sequence; opposite path definition; opposition-based learning; oppositional biogeography-based optimization; traveling salesman problems; Algorithm design and analysis; Benchmark testing; Cities and towns; Optimization; Sociology; Statistics; Traveling salesman problems; biogeography-based optimization; discrete domain; opposition-based learning; traveling salesman problems;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Natural Computation (ICNC), 2014 10th International Conference on
  • Conference_Location
    Xiamen
  • Print_ISBN
    978-1-4799-5150-5
  • Type

    conf

  • DOI
    10.1109/ICNC.2014.6975871
  • Filename
    6975871