A Comparative Performance Analysis of Gaussian Distribution Functions in Ant Swarm Optimized Rough Reducts

Pratiwi, Lustiana and Choo, Yun Huoy and Draman @ Muda, Azah Kamilah (2011) A Comparative Performance Analysis of Gaussian Distribution Functions in Ant Swarm Optimized Rough Reducts. International Journal on New Computer Architectures and Their Applications (IJNCAA), 1 (4). pp. 917-933. ISSN 2220-9085

[img] Text
Restricted to Registered users only

Download (826kB) | Request a copy


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.

Item Type: Article
Uncontrolled Keywords: probability density function, cumulative distribution function, particle swarm optimization, ant colony optimization, rough reducts
Subjects: T Technology > T Technology (General)
Q Science > Q Science (General)
Divisions: Faculty of Information and Communication Technology > Department of Industrial Computing
Depositing User: Dr. Yun-Huoy Choo
Date Deposited: 15 Dec 2011 10:19
Last Modified: 23 May 2023 11:39
URI: http://eprints.utem.edu.my/id/eprint/295
Statistic Details: View Download Statistic

Actions (login required)

View Item View Item