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

Pratiwi, Lustiana and Choo, Yun Huoy and Draman @ Muda, Azah Kamilah (2011) An empirical study of density and distribution functions for ant swarm optimized rough reducts. In: Software Engineering and Computer Systems. Springer Berlin Heidelberg, pp. 590-604. ISBN 978-364222190-3

Text AnEmpiricalStudyOfDensityAndDistributionFuncForACORR_SpringerVersion.pdf Restricted to Registered users only Download (341kB) | Request a copy

Abstract

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.

Item Type:	Book Chapter
Uncontrolled Keywords:	Probability density function, Cumulative distribution function, Particle swarm optimization, Ant colony optimization, Rough reducts
Subjects:	Q Science > Q Science (General) T Technology > T Technology (General)
Divisions:	Faculty of Information and Communication Technology > Department of Industrial Computing
Depositing User:	Dr. Yun-Huoy Choo
Date Deposited:	24 Oct 2011 00:42
Last Modified:	17 Aug 2023 11:51
URI:	http://eprints.utem.edu.my/id/eprint/150
Statistic Details:	View Download Statistic

Actions (login required)

View Item