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A framework for parallel large-scale global optimization

  • Yuri Evtushenko
  • Mikhail PosypkinEmail author
  • Israel Sigal
Special Issue Paper

Abstract

The paper describes the design and implementation of BNB-Solver, an object-oriented framework for discrete and continuous parallel global optimization. The framework supports exact branch-and-bound algorithms, heuristic methods and hybrid approaches. BNB-Solver provides a support for distributed and shared memory architectures. The implementation for distributed memory machines is based on MPI and thus can run on almost any computational cluster. In order to take advantages of multicore processors we provide a separate multi-threaded implementation for shared memory platforms. We introduce a novel collaborative scheme for combining exact and heuristic search methods that provides the support for sophisticated parallel heuristics and convenient balancing between exact and heuristic methods. In the experimental results section we discuss a nonlinear programming solver and a highly efficient knapsack solver that significantly outperforms existing parallel implementations.

Keywords

Parallel global optimization   Branch-and-bound methods   Heuristic methods, knapsack problems   Nonlinear programming 

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Copyright information

© Springer-Verlag 2009

Authors and Affiliations

  • Yuri Evtushenko
    • 1
  • Mikhail Posypkin
    • 2
    Email author
  • Israel Sigal
    • 1
  1. 1.Dorodnicyn Computing Centre of the Russian Academy of SciencesMoscowRussia
  2. 2.Institute for System Analysis of Russian Academy of SciencesMoscowRussia

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