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Efficient Methods of Multicriterial Optimization Based on the Intensive Use of Search Information

  • Victor GergelEmail author
  • Evgeny Kozinov
Conference paper
Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 197)

Abstract

In this paper, an efficient approach for solving complex multicriterial optimization problems is proposed. For the problems being solved, the optimality criteria may be multiextremal ones, and calculating the criteria values may require a large amount of computations. The proposed approach is based on reducing multicriterial problems to nonlinear programming problems via the minimax convolution of the partial criteria, reducing dimensionality by using Peano evolvents, and applying efficient information-statistical global optimization methods. The new contribution is that all the search information obtained in the course of optimization is used to find each current Pareto-optimal solution. The results of the computational experiments show that the proposed approach essentially reduces the computational costs of solving multicriterial optimization problems (by tens and hundreds of times).

Notes

Acknowledgements

This work has been supported by 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 International Publishing AG 2017

Authors and Affiliations

  1. 1.Nizhni Novgorod State UniversityNizhny NovgorodRussia

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