• DocumentCode
    2838102
  • Title

    A Quantum-Inspired Ant Colony Optimization for robot coalition formation

  • Author

    Yu, Zhang ; Shuhua, Liu ; Shuai, Fu ; Di, Wu

  • Author_Institution
    Sch. of Comput. Sci., Northeast Normal Univ., Changchun, China
  • fYear
    2009
  • fDate
    17-19 June 2009
  • Firstpage
    626
  • Lastpage
    631
  • Abstract
    A quantum-inspired ant colony optimization (QACO), based on the concept and principles of quantum computing is proposed in this paper to improve the ability to search and optimization of ant colony optimization (ACO). Each ant is a quantum individual and instead of Q-bit code, we use the probability of choosing robots, and QACO is successfully applied to solve robot coalition formation. The simulated results show that QACO has the better diversity of population and ability to search and optimization, and performs well, even with a small population, without premature convergence as compared to ACO.
  • Keywords
    combinatorial mathematics; multi-robot systems; optimisation; probability; quantum computing; search problems; Q-bit code; QACO; combinatorial optimization problem; multirobot system; premature convergence; probability; quantum computing; quantum-inspired ant colony optimization; robot coalition formation; search problem; Ant colony optimization; Cameras; Computational modeling; Computer science; Convergence; Mobile robots; Quantum computing; Quantum mechanics; Robot sensing systems; Robot vision systems; Large-scale robots; QACO; Robot Coalition Formation; Task Allocation; task allocation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Control and Decision Conference, 2009. CCDC '09. Chinese
  • Conference_Location
    Guilin
  • Print_ISBN
    978-1-4244-2722-2
  • Electronic_ISBN
    978-1-4244-2723-9
  • Type

    conf

  • DOI
    10.1109/CCDC.2009.5194884
  • Filename
    5194884