Abstract
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.
Original language | English |
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Title of host publication | Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference Late Breaking Papers |
Editors | Franz ROTHLAUF |
Publisher | Association for Computing Machinery (ACM) |
Pages | 2515-2522 |
ISBN (Print) | 9781605585055 |
DOIs | |
Publication status | Published - Jul 2009 |
Event | The 2009 Genetic and Evolutionary Computation Conference - Duration: 1 Jul 2009 → 1 Jul 2009 |
Conference
Conference | The 2009 Genetic and Evolutionary Computation Conference |
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Abbreviated title | GECCO09 |
Period | 1/07/09 → 1/07/09 |
Other | Association for Computing Machinery |