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Second IEEE International Conference on e-Science and Grid Computing (e-Science'06)
Hybrid Particle Guide Selection Methods in Multi-Objective Particle Swarm Optimization
Amsterdam, Netherlands
December 04-December 06
ISBN: 0-7695-2734-5
ASCII Text
x 
 
David Ireland, Andrew Lewis, Sanaz Mostaghim, Jun Wei Lu, "Hybrid Particle Guide Selection Methods in Multi-Objective Particle Swarm Optimization," e-Science and Grid Computing, International Conference on, pp. 116, Second IEEE International Conference on e-Science and Grid Computing (e-Science'06), 2006.
 
 
BibTex
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@article{ 10.1109/E-SCIENCE.2006.86,
author = {David Ireland and Andrew Lewis and Sanaz Mostaghim and Jun Wei Lu},
title = {Hybrid Particle Guide Selection Methods in Multi-Objective Particle Swarm Optimization},
journal ={e-Science and Grid Computing, International Conference on},
volume = {0},
year = {2006},
isbn = {0-7695-2734-5},
pages = {116},
doi = {http://doi.ieeecomputersociety.org/10.1109/E-SCIENCE.2006.86},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - e-Science and Grid Computing, International Conference on
TI - Hybrid Particle Guide Selection Methods in Multi-Objective Particle Swarm Optimization
SN - 0-7695-2734-5
SP
EP
A1 - David Ireland,
A1 - Andrew Lewis,
A1 - Sanaz Mostaghim,
A1 - Jun Wei Lu,
PY - 2006
KW - null
VL - 0
JA - e-Science and Grid Computing, International Conference on
ER -
 
David Ireland, Griffith University, Australia
Andrew Lewis, Griffith University, Australia
Sanaz Mostaghim, University of Karlsruhe, Germany
Jun Wei Lu, Griffith University, Australia
This paper presents quantitative comparison of the performance of different methods for selecting the guide particle for multi-objective particle swarm optimization (MOPSO). Two principal methods are compared: the recently described Sigma method, and a new "Centroid" method. Drawing on the different dominant behaviors exhibited by the different selection methods, a variety of hybridizations of these is proposed to develop a more robust optimization algorithm. Statistical analysis of the hybrid methods demonstrates their contribution to improved performance of the optimization algorithm.
Citation:
David Ireland, Andrew Lewis, Sanaz Mostaghim, Jun Wei Lu, "Hybrid Particle Guide Selection Methods in Multi-Objective Particle Swarm Optimization," e-science, pp.116, Second IEEE International Conference on e-Science and Grid Computing (e-Science'06), 2006
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