Abstract
A parallel algorithm for solving multiextremal multidimensional global optimization problems is proposed. The algorithm is based on reducing multidimensional problems to the one-dimensional ones by applying Peano-type space-filling curves. A new parallel scheme to construct such curves is presented. For reduced optimization problems a parallel global optimization method is constructed. Sufficient conditions of global convergence are investigated. Conditions, which guarantee considerable speedup with respect to the sequential version of the algorithm, are established. Numerical experiments executed on ALLIANT FX/80 are also presented.
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