Book Chapter or Conference Paper Title

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)

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.

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