title: A Modified Particle Swarm Optimization Technique for Finding Optimal Designs for Mixture Models.
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].
authors.affiliation: Department of Biostatistics, University of California, Los Angeles, USA.
authors.forename: Weng Kee
authors.initials: WK
authors.lastname: Wong
authors.affiliation: Department of Statistics, National Cheng Kung University, Taiwan.
authors.forename: Ray-Bing
authors.initials: RB
authors.lastname: Chen
authors.affiliation: Department of Mathematics, National Taiwan University, Taipei, Taiwan.
authors.forename: Chien-Chih
authors.initials: CC
authors.lastname: Huang
authors.affiliation: Institute of Applied Mathematical Sciences, National Taiwan University, Taiwan.
authors.forename: Weichung
authors.initials: W
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D008962:Models, Theoretical