Finding effective courses of action using particle swarm optimization
Faculty / School
Faculty of Computer Sciences (FCS)
Department
Department of Computer Science
Was this content written or created while at IBA?
Yes
Document Type
Conference Paper
Publication Date
11-14-2008
Conference Name
2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)
Conference Location
Hong Kong, China
Conference Dates
1-6 June 2008
ISBN/ISSN
55749090049 (Scopus)
First Page
1135
Last Page
1140
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Keywords
Tin, Evolutionary computation, Particle swarm optimization, Silicon, Delay, Bayesian methods, Pediatrics
Abstract / Description
The paper applies particle swarm optimization (PSO) technique to identify effective courses of action (COAs) in a dynamic uncertain situation. The uncertain situation is modeled using Timed Influence Nets (TINs), an instance of Dynamic Bayesian Networks. The TIN-based framework aids a system analyst in connecting a set of actionable events and a set of desired effects through chains of cause and effect relationships. The purpose of building these TIN models is to analyze several courses of action (COAs) and identify the ones that maximize the likelihood of achieving the desired effect(s). The paper attempts to automate this identification process of the best COA. It does so by exploring the solution space, consisting of potential courses of action, using PSO. The paper also compares the performance of PSO with that of an evolutionary algorithm (EA). The results suggest there is not a significant difference between the performances of the two techniques but PSO takes less time compared to EA.
DOI
https://doi.org/10.1109/CEC.2008.4630939
Recommended Citation
Haider, S., & Levis, A. H. (2008). Finding effective courses of action using particle swarm optimization., 1135-1140. https://doi.org/10.1109/CEC.2008.4630939
COinS
