Particle Swarm Optimization

TLDR

Particle swarm optimization has been applied to structural design problems but has a much wider range of possible applications. The paper aims to demonstrate how particle swarm optimization works and to present improvements to the algorithm along with conclusions and recommendations on its utility. The study presents numerical experiments on continuous and discrete problems to assess the improved particle swarm optimization algorithm. The experiments show that particle swarm optimization can locate constrained minima in continuous problems with high precision, though at higher computational cost than gradient methods, and that its true potential lies in discrete or discontinuous applications, where it can also benefit from efficient parallel computation. Contact information: Suite 100, Colorado Springs.

Abstract

Gerhard Venter (gventer_vrand.conl) *Vanderpla(ds Research and Development, bit.1767 S 8th St'reef. Suite 100, Colorado Springs. CO 80906Jaroslaw Sobieszczanski-Sobieski (j.sobieski:_larc.nasa.gov) *A_4SA Lcmgley Research Ce,_terMS 240, Hampton, I:4 23681-2199The purpose of this paper is to show how the search algorithm, known as par-ticle swarm optimization performs. Here, particle swarm optimization ks appliedto structural design problems, but the method.has a much wider range of possi-ble applications. The paper's new contributions are improvements to the particleswarm optimization algorithm and conclusions and recommendations as to theutility of the algorithm. Results of numerical experiments for both continuousand discrete applications are presented in the paper. The results indicate that theparticle swarm optimization algorithm does locate the constrained minimum de-sign in continuous applications with very good precision, albeit at a much highercomputational cost than that of a typical gradient based optimizer. However, thetrue potential of particle swarm optimization is primarily in applications withdiscrete and/or discontinuous functions and variables. Additionally, particleswarm optimization has the potential of e3_icient computation with very largenumbers of concurrently operating processors.