A population-based incremental learning method for robust optimal solutions

Siu Lau Ho, Shiyou Yang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

4 Citations (Scopus)


A population-based incremental learning (PBIL) method is proposed to search for both robust and global optimal solutions of an inverse problem in which there are inevitable tolerances on the decision variables. To reduce the computational costs of the proposed algorithm, a methodology for evaluating the expectancy measures and a philosophy for worst-case solutions are proposed. Moreover, a novel mechanism for selecting the performance metrics is introduced to enable the algorithm to find both global and robust optimal solutions in a single run. Two numerical examples are reported to validate the proposed algorithm.
Original languageEnglish
Article number5512950
Pages (from-to)3189-3192
Number of pages4
JournalIEEE Transactions on Magnetics
Issue number8
Publication statusPublished - 1 Aug 2010


  • Inverse problem
  • population-based incremental learning (PBIL)
  • robust solution
  • uncertainty

ASJC Scopus subject areas

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


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