Nature-Inspired Meta-heuristic Algorithms for Generating Optimal Experimental Designs

Abstract

Nature-Inspired Meta-heuristic Algorithms for Generating Optimal Experimental Designs Weng Kee Wong1, Weichung Wang2, Ray-Bing Chen3 1Department of Biostatistics, School of Public Health, University of California at Los Angeles, USA 2Department of Mathematics, National Taiwan University, Taiwan ROC 3Department of Statistics, National Cheng Kung University, Taiwan ROC ?:? Particle swarm optimization (PSO) is a relatively new, simple and powerful way to search for an optimal solution. It is widely used to solve optimization problems in many fields including computer science, engineering and finance. The method works quite magically and frequently finds the optimal solution or a nearly optimal solution after a few iterations. There is virtually no assumption required for the method to perform well and the user only needs to input a few easy to work with tuning parameters.? After I present a brief review of the theory of optimal design of experiments and recent advances in the field, I use several nonlinear models in the biological sciences to demonstrate that PSO can find different kinds of optimal designs quickly, including mini-max types of optimal designs where effective algorithms to find such designs have remained elusive until now. Dose response studies will serve as illustrative applications. ?