Optimal Experimental Designs via Particle Swarm Optimization Methods

• Activity Number: 653
• Type: Contributed
• Date/Time: Thursday, August 2, 2012 : 10:30 AM to 12:20 PM
• Sponsor: Quality and Productivity Section
• Author(s): Ray-Bing Chen*+ and Shin-Perng Chang and Weichung Wang Wang and Weng-Kee Wong
• Companies: National Cheng Kung University and Tokyo University and National Taiwan University and University of California at Los Angeles Fielding School of Public Health
• Keywords: Continuous optimal design ; equivalence theorem ; Fisher information matrix ; minimax optimality criteria ; regression model

Abstract

Particle swarm optimization (PSO) method is relatively new, simple yet powerful and widely used in applied fields. However PSO does not seem to have made an impact in mainstream statistical applications hitherto. We propose variants of the PSO method to find optimal experimental designs for both linear and nonlinear regression problems. We show that the PSO method can simply generate many types of optimal designs very quickly, including optimal designs under a non-differentiable criterion such as minimax optimal designs where effective algorithms to generate such designs have remained elusive to date.