Preference-Based Multi-Objective Particle Swarm Optimization Using Desirabilities

  • Sanaz Mostaghim
  • Heike Trautmann
  • Olaf Mersmann
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6239)


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.


Particle swarm optimization MOPSO desirability function desirability index preferences 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Sanaz Mostaghim
    • 1
  • Heike Trautmann
    • 2
  • Olaf Mersmann
    • 2
  1. 1.Karlsruhe Institute of TechnologyKarlsruheGermany
  2. 2.TU Dortmund UniversityDortmundGermany

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