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International Conference on Parallel Problem Solving from Nature

PPSN 2010: Parallel Problem Solving from Nature, PPSN XI pp 101–110Cite as

Preference-Based Multi-Objective Particle Swarm Optimization Using Desirabilities

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6239))

Abstract

The integration of experts’ preferences is an important aspect in multi-objective optimization. Usually, one out of a set of Pareto optimal solutions has to be chosen based on expert knowledge. A combination of multi-objective particle swarm optimization (MOPSO) with the desirability concept is introduced to efficiently focus on desired and relevant regions of the true Pareto front of the optimization problem which facilitates the solution selection process. Desirability functions of the objectives are optimized, and the desirability index is used for selecting the global best particle in each iteration. The resulting MOPSO variant DF-MOPSO in most cases exclusively generates solutions in the desired area of the Pareto front. Approximations of the whole Pareto front result in cases of misspecified desired regions.

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Author information

Authors and Affiliations

  1. Karlsruhe Institute of Technology, Karlsruhe, Germany

    Sanaz Mostaghim

  2. TU Dortmund University, Dortmund, Germany

    Heike Trautmann & Olaf Mersmann

Authors
  1. Sanaz Mostaghim
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  2. Heike Trautmann
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  3. Olaf Mersmann
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Editor information

Editors and Affiliations

  1. Faculty of Electrical Engineering, Automatics, Computer Science and Electronics, Department of ComputerScience, AGH University of Science and Technology, Mickiewicza 30, 30-059, Kraków, Poland

    Robert Schaefer

  2. Departamento de Lenguajes y Ciencias de la Computación, ETSI Informática, Universidad de Málaga, Campus Teatinos, 29071, Málaga, Spain

    Carlos Cotta

  3. Department of Mathematics and Computer Science, University of Bielsko-Biala, Willowa 2, 43-309, Bielsko-Biala, Poland

    Joanna Kołodziej

  4. Fakultät für Informatik, Technische Universität Dortmund, 44221, Dortmund, Germany

    Günter Rudolph

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© 2010 Springer-Verlag Berlin Heidelberg

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Mostaghim, S., Trautmann, H., Mersmann, O. (2010). Preference-Based Multi-Objective Particle Swarm Optimization Using Desirabilities. In: Schaefer, R., Cotta, C., Kołodziej, J., Rudolph, G. (eds) Parallel Problem Solving from Nature, PPSN XI. PPSN 2010. Lecture Notes in Computer Science, vol 6239. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15871-1_11

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  • DOI: https://doi.org/10.1007/978-3-642-15871-1_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-15870-4

  • Online ISBN: 978-3-642-15871-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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