The Normalized-PSO and Its Application in Attribute Weighted Optimal Problem

Abstract

Traditional PSO(Particle Swarm Optimization) algorithm has the problems of particle cross-border and premature convergence while solving the normalized constrained optimization problem. Our paper uses the attractor and spatial zoom method, and presented the normalized PSO algorithm. Secondly, each attribute is treated equally in the traditional classification algorithm, without considering the differences in attribute measure and contribution, which cause the problem of low classification accuracy. Our paper introduce the use of the normalized PSO algorithm to solve the optimal attribute normalized weighted distance. For example, in KNN classifier, leave-one-out experimental results with multiple UCI data sets show that the classification accuracy using normalization PSO algorithm to calculate normalized weighted distance is higher than using PSO algorithm and traditional none-attribute weighted classifiers.