Solution of Large-Scale Problems of Global Optimization on the Basis of Parallel Algorithms and Cluster Implementation of Computing Processes

  • Authors:

  • Vladimir Koshur;Dmitriy Kuzmin;Aleksandr Legalov;Kirill Pushkaryov

  • Affiliations:

  • Institute of Space and Information Technology, Siberian Federal University, Krasnoyarsk, Russia 660074;Institute of Space and Information Technology, Siberian Federal University, Krasnoyarsk, Russia 660074;Institute of Space and Information Technology, Siberian Federal University, Krasnoyarsk, Russia 660074;Institute of Space and Information Technology, Siberian Federal University, Krasnoyarsk, Russia 660074

  • Venue:
  • PaCT '09 Proceedings of the 10th International Conference on Parallel Computing Technologies
  • Year:
  • 2009

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Abstract

The parallel hybrid inverse neural network coordinate approxima tions algorithm (PHINNCA) for solution of large-scale global optimization problems is proposed in this work. The algorithm maps a trial value of an ob jective function into values of objective function arguments. It decreases a trial value step by step to find a global minimum. Dual generalized regression neural networks are used to perform the mapping. The algorithm is intended for cluster systems. A search is carried out concurrently. When there are multiple pro cesses, they share the information about their progress and apply a simulated annealing procedure to it.