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OVERVIEW

The Swarm Intelligence focus area is currently the most active in the group, with the largest number of members. The focus area's main interest is particle swarm optimization (PSO), with the development of new and improved PSO algorithms. Theoretical analyses of PSO are also being done, with convergence proofs being studied. Techniques are developed for constrained optimization, niching (locating multiple solutions), multi-objective optimization, dynamic optimization problems, and to cope with discrete search spaces.

Applications of PSO techniques that are under investigation include the coevolutionary training of neural networks for game playing and financial traders, scheduling, image analysis, and data clustering. The research focus area is also investigating the application of ant colony optimization techniques to exploratory data analysis, workload distribution in computer grids, energy efficient routing in mobile ad hoc networks, and network topology design.

ACTIVE MEMBERS

List the current members actively doing research in this focus area. [ Show ]

ALUMNI MEMBERS

L Schoeman

PhD Completed in 2010

J Grobler

PhD Started in 2009
M.Eng Completed in 2009

S Khan

PhD Completed in 2009

D Barla-Szabo

M.Sc Started in 2003
Hons-B.Sc Completed in 2002

M Neethling

M.Sc Completed in 2008

F Zablocki

M.Sc Completed in 2008

E Papacostantis

M.Sc Started in 2004
Hons-B.Sc Completed in 2003

L Messerschmidt

M.Sc Completed

C Naicker

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

J Conradie

Hons-B.Sc Completed in 2004

J du Plessis

M.Sc Completed in 2005

E Peer

M.Sc Completed in 2005

M Omran

PhD Completed in 2005

G Pampara

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

E van Loggerenberg

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

N Franken

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

R Brits

M.Sc Completed in 2003
Hons-B.Sc Completed in 2000

U Paquet

M.Sc Completed in 2003

F van den Bergh

PhD Completed in 2002

A Ismail

PhD Started in 2005
M.Sc Completed in 2001

GROUP PUBLICATIONS

List publications of this research focus area. [ Show ]

MEMBER PROFILE



 Name:

 Johan du Plessis

Portrait photo

 E-mail:

 jduplessis@cs.up.ac.za

 Group(s):

 Swarm Intelligence

 

 Degree specific information: M.Sc

 Title:

 ACODV: Ant Colony Optimisation Distance Vector Routing in Ad Hoc Networks

 Abstract:

A mobile ad hoc network is a collection of wireless mobile devices which dynamically form a temporary network, without using any existing network infrastructure or centralised administration. Each node in the network effectively becomes a router, and forwards packets towards the packet's destination node. Ad hoc networks are characterized by frequently changing network topology, multi-hop wireless connections and the need for dynamic, efficient routing protocols.
This work considers the routing problem in a network of uniquely addressable sensors. These networks are encountered in many industrial applications, where the aim is to relay information from a collection of data gathering devices deployed over an area to central points. The routing problem in such networks are characterised by:
The overarching requirement for low power consumption, as battery powered sensors may be required to operate for years without battery replacement; An emphasis on reliable communication as opposed to real-time communication, it is more important for packets to arrive reliably than to arrive quickly; and Very scarce processing and memory resources, as these sensors are often implemented on small low-power microprocessors.
This work provides overviews of routing protocols in ad hoc networks, swarm intelligence, and swarm intelligence applied to ad hoc routing. Various mechanisms that are commonly encountered in ad hoc routing are experimentally evaluated under situations as close to real-life as possible. Where possible, enhancements to the mechanisms are suggested and evaluated. Finally, a routing protocol suitable for such low-power sensor networks is defined and benchmarked in various scenarios against the Ad hoc On-Demand Distance Vector (AODV) algorithm.

 Supervisor / Co-Supervisor:

 AP Engelbrecht

 Thesis:

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