Hybrid Particle Guide Selection Methods in Multi-Objective Particle Swarm Optimization

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Second IEEE International Conference on e-Science and Grid Computing (e-Science'06)

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	x
	@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

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/E-SCIENCE.2006.86

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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