Optimal model discrimination designs by discrete particle swarm optimization

摘要

Since the experimenters might not have prior knowledge on which main effects or interactions were likely to be significant it is important to construct a experimental design that have the capability of screening main effects and two-factor interactions Agboto et al (2010) proposed model discrimination criteria But how to construct an optimal model discrimination design based on these criteria is a difficult question In recent years Particle Swarm Optimization has been wildly used in many aspects because of the advantages of the PSO algorithm In our study since the PSO algorithm is designed to solve the continuous optimization problems we need to modify the PSO algorithm due to particular design structure The purpose of this paper is to present the Discrete Particle Swarm Optimization algorithm to construct an optimal model discrimination design We implement our algorithm to optimize model discrimination design under the model discrimination criterion and compare results with Agboto et al (2010) and the coordinate-exchange algorithm The results show that the DPSO algorithm performs well and is compatible with other algorithms

獎項日期	2015 7月 31
原文	English
監督員	Ray-Bing Chen (Supervisor)