A modified Particle Swarm Optimization technique for finding optimal designs for mixture models

Weng Kee Wong, Ray Bing Chen, Chien Chih Huang, Weichung Wang

Research output: Contribution to journalArticlepeer-review

21 Citations (Scopus)

Abstract

Particle Swarm Optimization (PSO) is a meta-heuristic algorithm that has been shown to be successful in solving a wide variety of real and complicated optimization problems in engineering and computer science. This paper introduces a projection based PSO technique, named ProjPSO, to efficiently find different types of optimal designs, or nearly optimal designs, for mixture models with and without constraints on the components, and also for related models, like the log contrast models. We also compare the modified PSO performance with Fedorov's algorithm, a popular algorithm used to generate optimal designs, Cocktail algorithm, and the recent algorithm proposed by [1].

Original languageEnglish
Article numbere0124720
JournalPloS one
Volume10
Issue number6
DOIs
Publication statusPublished - 2015 Jun 19

All Science Journal Classification (ASJC) codes

  • Biochemistry, Genetics and Molecular Biology(all)
  • Agricultural and Biological Sciences(all)
  • General

Fingerprint Dive into the research topics of 'A modified Particle Swarm Optimization technique for finding optimal designs for mixture models'. Together they form a unique fingerprint.

Cite this