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Below is a list of the doctorate members actively conducting research at CIRG. Click on each name for more detailed information on the researcher and his/her project.

M Snyman

PhD Started in 2009

P Raharja

PhD Started in 2009

M Mtshali

PhD Started in 2009

B Kalema

PhD Started in 2009

J Grobler

PhD Started in 2009
M.Eng Completed in 2009

M Ahmad

PhD Started in 2009

T Museba

PhD Started in 2008

L Li

PhD Started in 2008

C Naicker

PhD Started in 2007
M.Sc Completed in 2006
Hons-B.Sc Completed in 2002

K Malan

PhD Started in 2007

M Greeff

PhD Started in 2007

B Baridam

PhD Started in 2007

MC du Plessis

PhD Started in 2006

D Constantinou

PhD Started in 2006

A Graaff

PhD Started in 2005
M.Sc Completed in 2003

N Franken

PhD Started in 2005
M.Sc Completed in 2004
Hons-B.Sc Completed in 2002

A Ismail

PhD Started in 2005
M.Sc Completed in 2001


Below is a list of previous doctorate members that conducted research at CIRG. Click on each name for more detailed information on the researcher and his/her project.

L Schoeman

PhD Completed in 2010

P Lutu

PhD Completed in 2010

S Khan

PhD Completed in 2009

D Rodic

PhD Completed in 2005
M.Sc Completed in 1999

M Omran

PhD Completed in 2005

F van den Bergh

PhD Completed in 2002



 Frans van den Bergh

Portrait photo




 Swarm Intelligence


 Degree specific information: PhD


 An Analysis of Particle Swarm Optimizers


Many scientific, engineering and economic problems involve the optimization of a set of parameters. These problems include examples like minimising the losses in a power grid by finding the optimal configuration of the components, or training a neural network to recognise images of people's faces. Numerous optimisation algorithms have been proposed to solve these problems, with varying degrees of success. The Particle Swarm Optimizer (PSO) is a relatively new technique that has been empirically shown to perform well on many of these optimisation problems. This research develops a theoretical model that can be used to describe the long-term behaviour of the algorithm. An enhanced version of the PSO is constructed and shown to have guaranteed convergence on local minima. This algorithm is extended further, resulting in an algorithm with guaranteed convergence on global minima. A model for constructing cooperative PSO algorithms is also developed, resulting in the introduction of two new PSO-based algorithms. The new PSO models are applied to function optimization tasks and training of product unit neural networks.

 Supervisor / Co-Supervisor:

 AP Engelbrecht



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