Cover Image

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

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 indiv...

Full description

Saved in:
Bibliographic Details
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
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.utem.eprints.150
record_format eprints
spelling my.utem.eprints.1502023-08-17T11:51:54Z http://eprints.utem.edu.my/id/eprint/150/ An empirical study of density and distribution functions for ant swarm optimized rough reducts Pratiwi, Lustiana Choo, Yun Huoy Draman @ Muda, Azah Kamilah Q Science (General) T Technology (General) 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. Springer Berlin Heidelberg Zain , J.M. 2011 Book Chapter PeerReviewed text en http://eprints.utem.edu.my/id/eprint/150/1/AnEmpiricalStudyOfDensityAndDistributionFuncForACORR_SpringerVersion.pdf 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 http://www.springerlink.com/content/g77l142873m43016/fulltext.pdf
institution Universiti Teknikal Malaysia Melaka
building UTEM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknikal Malaysia Melaka
content_source UTEM Institutional Repository
url_provider http://eprints.utem.edu.my/
language English
topic Q Science (General)
T Technology (General)
spellingShingle Q Science (General)
T Technology (General)
Pratiwi, Lustiana
Choo, Yun Huoy
Draman @ Muda, Azah Kamilah
An empirical study of density and distribution functions for ant swarm optimized rough reducts
description 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.
author2 Zain , J.M.
author_facet Zain , J.M.
Pratiwi, Lustiana
Choo, Yun Huoy
Draman @ Muda, Azah Kamilah
format Book Chapter
author Pratiwi, Lustiana
Choo, Yun Huoy
Draman @ Muda, Azah Kamilah
author_sort Pratiwi, Lustiana
title An empirical study of density and distribution functions for ant swarm optimized rough reducts
title_short An empirical study of density and distribution functions for ant swarm optimized rough reducts
title_full An empirical study of density and distribution functions for ant swarm optimized rough reducts
title_fullStr An empirical study of density and distribution functions for ant swarm optimized rough reducts
title_full_unstemmed An empirical study of density and distribution functions for ant swarm optimized rough reducts
title_sort empirical study of density and distribution functions for ant swarm optimized rough reducts
publisher Springer Berlin Heidelberg
publishDate 2011
url 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
_version_ 1775626878953979904
score 13.211869

Similar Items