• DocumentCode
    2765825
  • Title

    Ant Colony Optimizer as an Adaptive Classifier

  • Author

    Tayade, Achal ; Ragha, Leena

  • Author_Institution
    Dept. of Comput. Eng., Ramrao Adik Inst. of Technol., Navi Mumbai, India
  • fYear
    2012
  • fDate
    19-20 Oct. 2012
  • Firstpage
    1
  • Lastpage
    6
  • Abstract
    The widespread popularity of Optimization Algorithm in many fields such as Optimization, Pattern Recognition, Feature Extraction, Feature Selection etc. is mainly due to their ability to solve optimization problems in path planning. Out of many kinds of optimization algorithms, Ant Colony Optimization Algorithm is one of the most popular optimization algorithms. Many algorithms that dynamically construct solution to Ant Colony Optimization have increased in recent years. Several ant colony optimization algorithms present a promising performance on combinatorial optimization problem. Among them, Max Min Ant System performs comparatively better for Travelling Salesman Problem as compared to Ant System and Ant Colony System. A method is proposed for Classification using Ant Colony Optimizer as an Adaptive Classifier. The Classifier using ACO may give more efficient and effective method for classification in the adaptive environment.
  • Keywords
    ant colony optimisation; combinatorial mathematics; adaptive classifier; ant colony optimizer; ant colony system; combinatorial optimization problem; feature extraction; feature selection; max min ant system; optimization algorithm; path planning; pattern recognition; travelling salesman problem; Ant colony optimization; Cities and towns; Classification algorithms; Computers; Equations; Heuristic algorithms; Optimization; Adaptive Classifier; Ant Colony Optimization; Combinatorial Optimization Problem; Optimization Algorithm; Path Planning;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Communication, Information & Computing Technology (ICCICT), 2012 International Conference on
  • Conference_Location
    Mumbai
  • Print_ISBN
    978-1-4577-2077-2
  • Type

    conf

  • DOI
    10.1109/ICCICT.2012.6398106
  • Filename
    6398106