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

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

研究成果: Article同行評審

22 引文 斯高帕斯(Scopus)

摘要

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].

原文English
文章編號e0124720
期刊PloS one
10
發行號6
DOIs
出版狀態Published - 2015 六月 19

All Science Journal Classification (ASJC) codes

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

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