• DocumentCode
    2020455
  • Title

    Solving Weapon-Target Assignment Problems by a New Ant Colony Algorithm

  • Author

    Shang, Gao

  • Author_Institution
    Sch. of Electron. & Inf., Jiangsu Univ. of Sci. & Technol., Zhenjiang
  • Volume
    1
  • fYear
    2008
  • fDate
    17-18 Oct. 2008
  • Firstpage
    221
  • Lastpage
    224
  • Abstract
    A new ant colony algorithm for weapon-target assignment (WTA) problems is proposed. The proposed algorithm is a parallel mechanism based on ant colony optimization (ACO) and has cooperative interactions among ant colonies. It has both the advantage of ACO, the ability to find feasible solutions and to avoid premature convergence, and the advantage of heuristics, the ability to conduct fine-tuning to find better solutions. A comparison of the proposed algorithm with several existing search approaches shows that the new algorithm outperforms its competitors on all tested WTA problems.
  • Keywords
    military systems; optimisation; ant colony algorithm; ant colony optimization; weapon-target assignment problems; Algorithm design and analysis; Ant colony optimization; Biological cells; Computational intelligence; Genetic algorithms; Laboratories; Large-scale systems; Neural networks; Simulated annealing; Weapons; Weapon-Target Assignment; ant colony optimization; genetic algorithm;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computational Intelligence and Design, 2008. ISCID '08. International Symposium on
  • Conference_Location
    Wuhan
  • Print_ISBN
    978-0-7695-3311-7
  • Type

    conf

  • DOI
    10.1109/ISCID.2008.28
  • Filename
    4725595