Accelerating Parallel Multicriterial Optimization Methods Based on Intensive Using of Search Information

In the present paper, an efficient parallel method for solving complex multicriterial optimization problems, which the optimality criteria can be multiextremal, and the computing of the criteria values can require a large amount of computations in, is proposed. The proposed approach is based on the reduction of the multicriterial problems to the global optimization ones using the minimax convolution of the partial criteria, the dimensionality reduction with the use of the Peano space-filling curves, and the application of the efficient parallel information-statistical global optimization methods. The intensive use of the search information obtained in the course of computations is provided when conducting the computations. The results of the computational experiments demonstrated such an approach to allow reducing the computation costs of solving the multicriterial optimization problems essentially tens and hundreds times. (C) 2017 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of the International Conference on Computational Science