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
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