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GPU-Based Parallel Computations in Multicriterial Optimization

  • Victor GergelEmail author
  • Evgeny Kozinov
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 965)

Abstract

In the present paper, an efficient approach for solving the time-consuming multicriterial optimization problems, in which the optimality criteria could be the multiextremal ones and computing the criteria values could require a large amount of computations is proposed. The proposed approach is based on the reduction of the multicriterial problems to the scalar optimization ones with the use of the minimax convolution of the partial criteria, on the dimensionality reduction with the use of the Peano space-filling curves, and on the application of the efficient information-statistical global optimization methods. An additional application of the block multistep scheme provides the opportunity of the large-scale parallel computations with the use of the graphics processing units (GPUs) with thousands of computational cores. The results of the numerical experiments have demonstrated such an approach to allow improving the computational efficiency of solving the multicriterial optimization problems considerably – hundreds and thousands.

Keywords

Decision making Multicriterial optimization Global optimization High performance computations Dimensionality reduction Criteria convolution Global search algorithm Computational costs 

Notes

Acknowledgements

This research was supported by the Russian Science Foundation, project No 16-11-10150 “Novel efficient methods and software tools for time-consuming decision-making problems using supercomputers of superior performance”.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Lobachevsky State University of Nizhni NovgorodNizhni NovgorodRussia

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