Adaptively tuned particle swarm optimization with application to spatial design. Issue 1 (17th April 2017)

• Record Type: Journal Article
• Authors: Simpson, Matthew; Wikle, Christopher K.; Holan, Scott H.
• Is Part Of: Stat. Volume 6:Issue 1(2017)
• Page Start: 145
• Page End: 159
• Publication Date: 2017-04-17
• Subjects: geostatistics -- kriging -- optimal design -- optimization -- particle swarm -- spatial prediction; Statistics -- Periodicals; 519.2
• DOI: 10.1002/sta4.142
• Languages: English
• ISSNs: 2049-1573

Abstract

Abstract : Particle swarm optimization (PSO) algorithms are a class of heuristic optimization algorithms that are attractive for complex optimization problems. We propose using PSO to solve spatial design problems, e.g. choosing new locations to add to an existing monitoring network. Additionally, we introduce two new classes of PSO algorithms that perform well in a wide variety of circumstances, called adaptively tuned PSO and adaptively tuned bare bones PSO. To illustrate these algorithms, we apply them to a common spatial design problem: choosing new locations to add to an existing monitoring network. Specifically, we consider a network in the Houston, TX, area for monitoring ambient ozone levels, which have been linked to out‐of‐hospital cardiac arrest rates. Published 2017. This article has been contributed to by US Government employees and their work is in the public domain in the USA