Incorporating a priori preferences in a vector PSO algorithm to find arbitrary fractions of the pareto front of multiobjective design problems

Siu Lau Ho, Shiyou Yang, Guangzheng Ni

Research output: Journal article publicationJournal articleAcademic researchpeer-review

17 Citations (Scopus)


To incorporate the knowledge or preference of a decision maker or domain expert into a vector optimizer in the search for a series of subsets of the entire Pareto optimal solutions, a vector particle swarm optimization (PSO) algorithm that implements the reference point-based approach together with a desirability function is proposed. The fitness assignment strategy and the neighborhood relationship of the PSO algorithm are redefined to facilitate the realization of the aforementioned objective. To validate and demonstrate the advantages of the proposed algorithm, its applications on two different multiobjective problems are reported.
Original languageEnglish
Article number4526890
Pages (from-to)1038-1041
Number of pages4
JournalIEEE Transactions on Magnetics
Issue number6
Publication statusPublished - 1 Jun 2008


  • Desirability function
  • Multiobjective design
  • Particle swarm optimization (PSO) algorithm
  • Reference point

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Electrical and Electronic Engineering

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