Most real-life optimization problems or decision-making problems are multi-objective in nature, since they normally have several (possibly conflicting) objectives that must be satisfied at the same time. Multi-Objective Evolutionary Algorithms (MOEAs) have been gaining increasing attention among researchers and practitioners. However, they may execute for a long time for some difficult problems, because several evaluations must be performed. Moreover, the non-dominance checking and the non-dominated selection procedures are also very time consuming. From our experiments, more than 99% of the execution time is used in performing the two procedures. A promising approach to overcome this limitation is to parallelize these algorithms. In this paper, we propose a parallel MOEA on consumer-level Graphics Processing Units (GPU). We perform many experiments on two-objective and three-objective benchmark problems to compare our parallel MOEA with a sequential MOEA and demonstrate that the former is much more efficient than the latter.
|Title of host publication
|Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference Late Breaking Papers
|Association for Computing Machinery (ACM)
|Number of pages
|Published - Jul 2009
|11th Annual Genetic and Evolutionary Computation Conference, GECCO-2009 - Montreal, QC, Canada
Duration: 8 Jul 2009 → 12 Jul 2009
|Proceedings of the 11th Annual Genetic and Evolutionary Computation Conference, GECCO-2009
|11th Annual Genetic and Evolutionary Computation Conference, GECCO-2009
|8/07/09 → 12/07/09
|Association for Computing Machinery
Bibliographical noteFunding Information:
This work is supported by the Lingnan University Direct Grant DR08B2.
© 2009 ACM.
- Graphic Process- ing Units
- Multi-Objective Evolutionary Algorithms
- Parallel Programming