Automatic data distribution is one of the most crucial issues preventing the development of a fully automatic parallelisation environment. Researchers have proposed solutions that utilise artificial intelligence (AI) technology including expert systems and neural networks to try and solve the problem. In this research project, alternative artificial intelligent techniques including Genetic Algorithms (GAs) and Ant Colony Optimisation (ACO) are investigated for the purposes of determining if their use would be beneficial in the data distribution process. A data distribution 1001 has been developed for each technique in order to verify the detailed analysis. The tools were tested using 300 example loops and the results show that the introduction of these techniques was successful in determining an appropriate data partition and distribution strategy for all 300 test cases. Furthermore, a novel hyper-heuristic approach to the data distribution problem involving case base reasoning is also investigated. The aim of the hyper-heuristic approach is to select the most appropriate heuristic to apply to a particular problem. The approach has been verified by the development of a case base reasoning tool that will choose an appropriate heuristic based on previous experience. Results show that the approach is effective at identifying similar cases in the case base and choosing the most appropriate heuristic to apply.