Improved Chaotic Initialization of Particle Swarm applied to Feature Selection

This paper investigates initializations on both Chaotic Particle Swarm PSO and reduced PSO algorithm. The first initialization method is chaotic initialization with three variants of chaotic PSO. Several chaotic maps are tested. Moreover, to achieve improvement for PSO algorithm, the second initialization method called reduced size PSO (RedPSO) is characterized with limited size of features depending on the whole size of features. It has been found that the chaotic initialization method is better than random initialization for exiting methods (PSO), artificial bee colony (ABC) in term of minimal number of features and highest accuracy. Experimental results validate the chaotic initialization approach tested on UCI data.