Abstract
We consider in this paper an efficient approach to the parallel solution of complex multicriterial optimization problems using heterogeneous computing systems. The complexity of these problems can be very high since the criteria that are to be optimized can be multiextremal and the computation of criteria values can be time-consuming. In the framework of the proposed approach, the multicriterial optimization problem is reduced to the solution of a series of global optimization problems by means of the convolution of the partial criteria with different sets of parameters. To solve the series of global optimization problems, we apply an efficient information-statistical method of global search. Parallel computations are implemented through the simultaneous solution of several global optimization problems. We present in this paper a comparative analysis of various methods for parallel computations and the results of numerical experiments.
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