License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
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DOI: 10.4230/DagSemProc.04461.4
URN: urn:nbn:de:0030-drops-2543
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Mostaghim, Sanaz ; Teich, J├╝rgen

A New Approach on Many Objective Diversity Measurement

04461.MostaghimSanaz.Paper.254.pdf (0.3 MB)


In multi-objective particle swarm optimization (MOPSO) methods, selecting the best {it local guide} (the global best particle)
for each particle of the population from a set of Pareto-optimal solutions has a great impact on the
convergence and diversity of solutions, especially when optimizing problems with high number of objectives.
here, we introduce the Sigma method as a new method for finding best local guides for each particle of the population.
The Sigma method is implemented
and is compared with another method, which uses the strategy of an existing MOPSO method for
finding the local guides.
These methods are examined for different test functions and the results are compared with the results of a multi-objective
evolutionary algorithm (MOEA).

BibTeX - Entry

  author =	{Mostaghim, Sanaz and Teich, J\"{u}rgen},
  title =	{{A New Approach on Many Objective Diversity Measurement}},
  booktitle =	{Practical Approaches to Multi-Objective Optimization},
  pages =	{1--15},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2005},
  volume =	{4461},
  editor =	{J\"{u}rgen Branke and Kalyanmoy Deb and Kaisa Miettinen and Ralph E. Steuer},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{},
  URN =		{urn:nbn:de:0030-drops-2543},
  doi =		{10.4230/DagSemProc.04461.4},
  annote =	{Keywords: Multi-objective Optimization, Particle Swarm Optimization}

Keywords: Multi-objective Optimization, Particle Swarm Optimization
Collection: 04461 - Practical Approaches to Multi-Objective Optimization
Issue Date: 2005
Date of publication: 10.08.2005

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