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
Link To Document :
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