Reference point-based particle swarm optimization using a steady-state approach

Richard Allmendinger, Xiaodong Li, Jürgen Branke

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publicationSimulated Evolution and Learning - 7th International Conference, SEAL 2008, Proceedings
PublisherSpringer Nature
Pages200-209
Number of pages10
Volume5361
ISBN (Electronic) 9783540896944
ISBN (Print)3540896937, 9783540896937
DOIs
Publication statusPublished - 2008
Event7th International Conference on Simulated Evolution and Learning, SEAL 2008 - Melbourne, Australia
Duration: 7 Dec 200810 Dec 2008

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5361 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference7th International Conference on Simulated Evolution and Learning, SEAL 2008
Country/TerritoryAustralia
CityMelbourne
Period7/12/0810/12/08

Keywords

  • Particle swarm optimization Algorithm
  • Multiobjective evolutionary algorithm
  • Nadir Point
  • Particle swarm optimization Variant

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