In this thesis, we devise a new stochastic optimisation method (cascade optimisation algorithm) by incorporating the concepts from Markov process whilst eliminating the inherent sequential nature that is the major deficit preventing the exploitation of advances in distributed computing infrastructures. This method introduces partitions and pools to store intermediate solution and corresponding objectives. A Markov process increases the population of partitions and pools. The population is distributed periodically following an external certain. With the use of partitions and pools, multiple Markov processes can be launched simultaneously for different partitions and pools. The cascade optimisation algorithm is suitable for parallel and distributed computing environments. In addition, this method has the potential to integrate knowledge acquisition techniques (e. g. data mining and ontology) to achieve effective knowledge-based decision making. Several features are extracted and studied in this thesis. The application problems involve both the small-scale and the large-scale optimisation problems. Comparisons with the stochastic optimisation methods are made and results show that the cascade optimisation algorithm can converge to the optimal solutions in agreement with other methods more quickly. The cascade optimisation algorithm is also studied on parallel and distributed computing environments in terms of the reduction in computation time.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:502683 |
| Date | January 2009 |
| Creators | Yang, Siyu |
| Contributors | Kokossis, Antonis ; Linke, Patrick ; Cecelja, Franjo |
| Publisher | University of Surrey |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://epubs.surrey.ac.uk/2119/ |
Page generated in 0.1471 seconds