Parallel multi-objective evolutionary algorithms on graphics processing units

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.