Particle Swarm Optimization and Hill-Climbing Optimized Rough Sets for Modeling Interstate Conflict

  • Tshilidzi Marwala
  • Monica Lagazio
Part of the Advanced Information and Knowledge Processing book series (AI&KP)


This chapter presents methods to optimally granulize rough set partition sizes using particle swarm optimization and hill climbing techniques. These two methods are then compared to the equal-width-bin partitioning technique. The results obtained demonstrated that hill climbing provides higher forecasting accuracy, followed by the particle swarm optimization method, which was better than the equal-width-bin technique.


Particle Swarm Optimization Hill Climbing Particle Swarm Optimization Method Standard Particle Swarm Optimization Partition Size 
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© Springer-Verlag London Limited 2011

Authors and Affiliations

  1. 1.University of JohannesburgJohannesburgSouth Africa

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