• DocumentCode
    116234
  • Title

    Enhanced Particle Swarm Optimization algorithm with resuse guided retrieval capabilities

  • Author

    Nouaouria, Nabila ; Boukadoum, Mounir ; Proulx, Robert

  • Author_Institution
    Dept. of Comput. Sci., UQAM, Montreal, QC, Canada
  • fYear
    2014
  • fDate
    18-20 Aug. 2014
  • Firstpage
    392
  • Lastpage
    399
  • Abstract
    This paper presents a novel associative memory model to perform the retrieval stage in a case based reasoning system. The described approach makes no prior assumption of a specific organization of the case memory, thus leading to a generic recall process. This is made possible by using Particle Swarm Optimization (PSO) to compute the neighborhood of a new problem, followed by direct access to the cases it contains. The fitness function of the PSO stage has a reuse semantic that combines similarity and adaptability as criteria for optimal case retrieval. The model was experimented on two proprietary databases and compared to the flat memory model for performance. The obtained results are very promising.
  • Keywords
    associative processing; case-based reasoning; information retrieval; particle swarm optimisation; PSO; case based reasoning system; enhanced particle swarm optimization algorithm; flat memory model; novel associative memory model; optimal case retrieval; resuse guided retrieval capabilities; Adaptation models; Computational modeling; Mathematical model; Organizations; Particle swarm optimization; Search problems; Wind speed; Case Based Reasoning (CBR); Particle Swarm Optimization (PSO); Reuse-Guided-Retrieval (RGR);
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Cognitive Informatics & Cognitive Computing (ICCI*CC), 2014 IEEE 13th International Conference on
  • Conference_Location
    London
  • Print_ISBN
    978-1-4799-6080-4
  • Type

    conf

  • DOI
    10.1109/ICCI-CC.2014.6921489
  • Filename
    6921489