期刊名称：International Journal of New Computer Architectures and their Applications
印刷版ISSN：2220-9085
出版年度：2011
卷号：1
期号：4
页码：894-910
出版社：Society of Digital Information and Wireless Communications
摘要：This paper proposed to generate solution for Particle Swarm Optimization (PSO) algorithms using Ant Colony Optimization approach, which will satisfy the Gaussian distributions to enhance PSO performance. Coexistence, cooperation, and individual contribution to food searching by a particle (ant) as a swarm (ant) survival behavior, depict the common characteristics of both algorithms. Solution vector of ACO is presented by implementing density and distribution function to search for a better solution and to specify a probability functions for every particle (ant). Applying a simple pheromone-guided mechanism of ACO as local search is to handle P ants equal to the number of particles in PSO and generate components of solution vector, which satisfy Gaussian distributions. To describe relative probability of different random variables, PDF and CDF are capable to specify its own characterization of Gaussian distributions. The comparison is based on the experimental result to increase higher fitness value and gain better reducts, which has shown that PDF is better than CDF in terms of generating smaller number of reducts, improved fitness value, lower number of iterations, and higher classification accuracy.
关键词：probability density function; cumulative ; distribution function; particle swarm ; optimization; ant colony optimization; rough ; reducts
