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Journal of Global Optimization

, Volume 71, Issue 1, pp 73–90 | Cite as

Efficient multicriterial optimization based on intensive reuse of search information

  • Victor GergelEmail author
  • Evgeny Kozinov
Article

Abstract

This paper proposes an efficient method for solving complex multicriterial optimization problems, for which the optimality criteria may be multiextremal and the calculations of the criteria values may be time-consuming. The approach involves reducing multicriterial problems to global optimization ones through minimax convolution of partial criteria, reducing dimensionality by using Peano curves and implementing efficient information-statistical methods for global optimization. To efficiently find the set of Pareto-optimal solutions, it is proposed to reuse all the search information obtained in the course of optimization. The results of computational experiments indicate that the proposed approach greatly reduces the computational complexity of solving multicriterial optimization problems.

Keywords

Decision making Multicriterial optimization Scalarization Dimensionality reduction Global optimization algorithm Search information Computational complexity 

Notes

Acknowledgements

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

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Institute of Information Technology, Mathematics and MechanicsLobachevsky State University of Nizhni NovgorodNizhni NovgorodRussia

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