A distributed variant of multi-objective particle swarm optimization (MOPSO) called
multi-objective parallel asynchronous particle swarm optimization (MOPAPSO) is
presented, and the effects of distribution of objective function calculations to slave
processors on the results and performance are investigated and employed for the
synthesis of Grashof mechanisms.
By using a formal multi-objective handling scheme based on Pareto dominance criteria, the need to pre-weight competing systemic objective functions is removed and the optimal solution for a design problem can be selected from a front of candidates after the parameter optimization has been completed.
MOPAPSO's ability to match MOPSO's results using parallelization for improved performance is presented. Results for both four and ve bar mechanism synthesis
examples are shown. / UOIT
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:OOSHDU.10155/17 |
| Date | 01 December 2008 |
| Creators | McDougall, Robin David |
| Contributors | Nokleby, Scott |
| Source Sets | Library and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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