Test Problems for Parallel Algorithms of Constrained Global Optimization

  • Konstantin BarkalovEmail author
  • Roman Strongin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10556)


This work considers the problem of building a class of test problems for global optimization algorithms. The authors present an approach to building multidimensional multiextremal problems, which can clearly demonstrate the nature of the best current approximation, regardless of the problems dimensionality. As part of this approach, the objective function and constraints arise in the process of solving an auxiliary approximation problem. The proposed generator allows the problem to be simplified or complicated, which results in changes to its dimensionality and changes in the feasible domain. The generator was tested by building and solving 100 problems using a parallel global optimization index algorithm. The algorithm’s results are presented using different numbers of computing cores, which clearly demonstrate its acceleration and non-redundancy.


Global optimization Multiextremal functions Non-convex constraints 



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


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Lobachevsky State University of Nizhny NovgorodNizhny NovgorodRussia

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