Parallel Computations for Various Scalarization Schemes in Multicriteria Optimization Problems

  • Victor GergelEmail author
  • Evgeny Kozinov
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12043)


In the present paper, a novel approach to parallel computations for solving time-consuming multicriteria global optimization problems is presented. This approach includes various methods for the scalarization of vector criteria, dimensionality reduction with the use of the Peano space-filling curves, and efficient global search algorithms. The applied criteria scalarization methods can be altered in the course of computations in order to better meet the stated optimality requirements. To reduce the computational complexity of multicriteria problems, the methods developed feature an extensive use of all the computed optimization information and are well parallelized for effective performance on high-performance computing systems. Numerical experiments confirmed the efficiency of the developed approach.


Multicriteria optimization Criteria scalarization Global optimization Parallel computations 


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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Lobachevsky State University of Nizhni NovgorodNizhni NovgorodRussia

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