Comparison of Dimensionality Reduction Schemes for Parallel Global Optimization Algorithms

  • Konstantin Barkalov
  • Vladislav SovrasovEmail author
  • Ilya Lebedev
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 965)


This work considers a parallel algorithms for solving multi-extremal optimization problems. Algorithms are developed within the framework of the information-statistical approach and implemented in a parallel solver Globalizer. The optimization problem is solved by reducing the multidimensional problem to a set of joint one-dimensional problems that are solved in parallel. Five types of Peano-type space-filling curves are employed to reduce dimension. The results of computational experiments carried out on several hundred test problems are discussed.


Global optimization Dimension reduction Parallel algorithms Multidimensional multiextremal optimization Global search algorithms Parallel computations 



The study was supported by the Russian Science Foundation, project No 16-11-10150.


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Konstantin Barkalov
    • 1
  • Vladislav Sovrasov
    • 1
    Email author
  • Ilya Lebedev
    • 1
  1. 1.Lobachevsky State University of Nizhni NovgorodNizhni NovgorodRussia

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