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Reference Point-Based Particle Swarm Optimization Using a Steady-State Approach

  • Richard Allmendinger
  • Xiaodong Li
  • Jürgen Branke
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5361)

Abstract

Conventional multi-objective Particle Swarm Optimization (PSO) algorithms aim to find a representative set of Pareto-optimal solutions from which the user may choose preferred solutions. For this purpose, most multi-objective PSO algorithms employ computationally expensive comparison procedures such as non-dominated sorting. We propose a PSO algorithm, Reference point-based PSO using a Steady-State approach (RPSO-SS), that finds a preferred set of solutions near user-provided reference points, instead of the entire set of Pareto-optimal solutions. RPSO-SS uses simple replacement strategies within a steady-state environment. The efficacy of RPSO-SS in finding desired regions of solutions is illustrated using some well-known two and three-objective test problems.

Keywords

Particle Swarm Optimization Algorithm Multiobjective Evolutionary Algorithm Nadir Point Particle Swarm Optimization Variant Personal Good Position 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Richard Allmendinger
    • 1
  • Xiaodong Li
    • 2
  • Jürgen Branke
    • 1
  1. 1.Institute AIFBUniversity of KarlsruheKarlsruheGermany
  2. 2.School of Computer Science and Information TechnologyRMIT UniversityMelbourneAustralia

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