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Parallel Algorithm for Solving Constrained Global Optimization Problems

  • Konstantin BarkalovEmail author
  • Ilya Lebedev
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10421)

Abstract

This work considers a parallel algorithm for solving multiextremal problems with non-convex constraints. The distinctive feature of this algorithm, which does not use penalty functions, is the separate consideration of each problem constraint. The search process can be conducted by reducing the original multidimensional problem to a number of related one-dimensional problems and solving this set of problems in parallel. An experimental assessment of parallel algorithm efficiency was conducted by finding the numeric solution to several hundred randomly generated multidimensional multiextremal problems with non-convex constraints.

Keywords

Global optimization Constrained problems Non-convex constraints Dimension reduction Parallel algorithms 

Notes

Acknowledgements

The 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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