The computational Grid has become an important infrastructure as an execution environment for scientific applications that require large amount of computing resources.  Applications which would otherwise be unmanageable or take a prohibitively longer execution time under previous computing paradigms can now be executed efficiently on the Grid within a reasonable time.

Multi-agent based simulation (MABS) is a methodology used to study and understand the dynamics of real world phenomena in domains involving interaction and/or cooperative problem solving where the participants are characterized by entities having autonomous and social behaviour.  For certain domains the size of the simulation is extremely large, intractable without employing adequate computing resources such as the Grid. 

Although the Grid has come with immense opportunities to resource demanding applications such as MABS, it has also brought with it a number of challenges related to performance.  Performance problems may have their origins either on the side of the computing infrastructure or the application itself, or both. 

This thesis aims at improving the performance of MABS applications by overcoming problems inherent to the behaviour of MABS applications.  It also studies the extent to which the MABS technologies have been exploited in the field of simulation and find ways to adapt existing technologies for the Grid.  It investigates performance monitoring and prediction systems in the Grid environment and their implementation for MABS application with the purpose of identifying application related performance problems and their solutions.

Our research shows that large-scale MABS applications have not been implemented despite the fact that many problem domains that cannot be studied properly with only partial simulation.  We assume that this is due to the lack of appropriate tools such as MABS platforms for the Grid.  Another important finding of this work is the improvement of application performance through the use of MABS specific middleware.