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Documents authored by Mostaghim, Sanaz

Document
DOI: 10.4230/DagSemProc.09041.5
09041 Working Group on EMO for Robust Multiobjective Optimization (1st Round)

Authors: Joerg Fliege, Nicola Beume, Juergen Branke, Heinrich Braun, Nirupam Chakraborti, Kalyanmoy Deb, Sabine Helwig, Joshua Knowles, Martin Middendorf, Sanaz Mostaghim, Silvia Poles, Salazar Daniel, Shukla Pradymn, and El-Ghazli Talbi

Published in: Dagstuhl Seminar Proceedings, Volume 9041, Hybrid and Robust Approaches to Multiobjective Optimization (2009)

Abstract
This group explored various robust methodologies for multiobjective optimization.
Cite as

Joerg Fliege, Nicola Beume, Juergen Branke, Heinrich Braun, Nirupam Chakraborti, Kalyanmoy Deb, Sabine Helwig, Joshua Knowles, Martin Middendorf, Sanaz Mostaghim, Silvia Poles, Salazar Daniel, Shukla Pradymn, and El-Ghazli Talbi. 09041 Working Group on EMO for Robust Multiobjective Optimization (1st Round). In Hybrid and Robust Approaches to Multiobjective Optimization. Dagstuhl Seminar Proceedings, Volume 9041, pp. 1-5, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2009)

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@InProceedings{fliege_et_al:DagSemProc.09041.5,
  author =	{Fliege, Joerg and Beume, Nicola and Branke, Juergen and Braun, Heinrich and Chakraborti, Nirupam and Deb, Kalyanmoy and Helwig, Sabine and Knowles, Joshua and Middendorf, Martin and Mostaghim, Sanaz and Poles, Silvia and Salazar Daniel and Shukla Pradymn and Talbi, El-Ghazli},
  title =	{{09041 Working Group on EMO for Robust Multiobjective Optimization (1st Round)}},
  booktitle =	{Hybrid and Robust Approaches to Multiobjective Optimization},
  pages =	{1--5},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2009},
  volume =	{9041},
  editor =	{Kalyanmoy Deb and Salvatore Greco and Kaisa Miettinen and Eckart Zitzler},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.09041.5},
  URN =		{urn:nbn:de:0030-drops-20030},
  doi =		{10.4230/DagSemProc.09041.5},
  annote =	{Keywords: Robust multiobjective optimization}
}
Document
DOI: 10.4230/DagSemProc.04461.4
A New Approach on Many Objective Diversity Measurement

Authors: Sanaz Mostaghim and Jürgen Teich

Published in: Dagstuhl Seminar Proceedings, Volume 4461, Practical Approaches to Multi-Objective Optimization (2005)

Abstract
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).
Cite as

Sanaz Mostaghim and Jürgen Teich. A New Approach on Many Objective Diversity Measurement. In Practical Approaches to Multi-Objective Optimization. Dagstuhl Seminar Proceedings, Volume 4461, pp. 1-15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2005)

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@InProceedings{mostaghim_et_al:DagSemProc.04461.4,
  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 =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.04461.4},
  URN =		{urn:nbn:de:0030-drops-2543},
  doi =		{10.4230/DagSemProc.04461.4},
  annote =	{Keywords: Multi-objective Optimization, Particle Swarm Optimization}
}
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