Abstract
Evolutionary multi-objective optimization algorithms aim at finding an approximation of the Pareto set. For hard to solve problems with many conflicting objectives, the number of functions evaluations to represent the Pareto front can be large and time consuming. Parallel computing can reduce the wall-clock time of such algorithms. Previous studies tackled the parallelization of a particular evolutionary algorithm. In this research, we focus on improving one of the most time consuming procedures—the non-dominated sorting—, which is used in the state-of-the-art multi-objective genetic algorithms. Here, three parallel versions of the non-dominated sorting procedure are developed: (1) a multicore (based on Pthreads); (2) a Graphic Processing Unit (GPU) (based on CUDA interface); and (3) a hybrid (based on Pthreads and CUDA). The user can select the most suitable option to efficiently compute the non-dominated sorting procedure depending on the available hardware. Results show that the use of GPU computing provides a substantial improvement in terms of performance. The hybrid approach has the best performance when a good load balance is established among cores and GPU.
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