Multi-Objective Particle Swarm Optimizers: An Experimental Comparison

  • Juan J. Durillo
  • José García-Nieto
  • Antonio J. Nebro
  • Carlos A. Coello Coello
  • Francisco Luna
  • Enrique Alba
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5467)


Particle Swarm Optimization (PSO) has received increasing attention in the optimization research community since its first appearance in the mid-1990s. Regarding multi-objective optimization, a considerable number of algorithms based on Multi-Objective Particle Swarm Optimizers (MOPSOs) can be found in the specialized literature. Unfortunately, no experimental comparisons have been made in order to clarify which MOPSO version shows the best performance. In this paper, we use a benchmark composed of three well-known problem families (ZDT, DTLZ, and WFG) with the aim of analyzing the search capabilities of six representative state-of-the-art MOPSOs, namely, NSPSO, SigmaMOPSO, OMOPSO, AMOPSO, MOPSOpd, and CLMOPSO. We additionally propose a new MOPSO algorithm, called SMPSO, characterized by including a velocity constraint mechanism, obtaining promising results where the rest perform inadequately.


Particle Swarm Optimization Multi-Objective Optimization Comparative Study 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Juan J. Durillo
    • 1
  • José García-Nieto
    • 1
  • Antonio J. Nebro
    • 1
  • Carlos A. Coello Coello
    • 2
  • Francisco Luna
    • 1
  • Enrique Alba
    • 1
  1. 1.Department of Computer ScienceUniversity of MálagaSpain
  2. 2.Department of Computer ScienceCINVESTAV-IPNMexico

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