An empirical study of density and distribution functions for ant swarm optimized rough reducts

• Main Authors: Pratiwi, Lustiana, Choo, Yun Huoy, Draman @ Muda, Azah Kamilah
• Other Authors: Zain , J.M.
• Format: Book Chapter
• Language: English
• Published: Springer Berlin Heidelberg 2011
• Subjects: Q Science (General); T Technology (General)
• Online Access: http://eprints.utem.edu.my/id/eprint/150/1/AnEmpiricalStudyOfDensityAndDistributionFuncForACORR_SpringerVersion.pdf; http://eprints.utem.edu.my/id/eprint/150/; http://www.springerlink.com/content/g77l142873m43016/fulltext.pdf

Ant Swarm Optimization refers to the hybridization of Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) algorithms to enhance optimization performance. It is used in rough reducts calculation for identifying optimally significant attributes set. Coexistence, cooperation, and individual contribution to food searching by a particle (ant) as a swarm (colony) survival behavior, depict the common characteristics of both PSO and ACO algorithms. Ant colony approach in Ant Swarm algorithm generates local solutions which satisfy the Gaussian distribution for global optimization using PSO algorithm. The density and distribution functions are two common types of Gaussian distribution representation. However, the description and comparison of both functions are very limited. Hence, this paper compares the solution vector of ACO is represented by both density and distribution function to search for a better solution and to specify a probability functions for every particle (ant), and generate components of solution vector, which satisfy Gaussian distributions. To describe relative probability of different random variables, Probability Density Function (PDF) and the Cumulative Density Function (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.