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2017 | OriginalPaper | Chapter

Parallel Algorithm for Solving Constrained Global Optimization Problems

Authors : Konstantin Barkalov, Ilya Lebedev

Published in: Parallel Computing Technologies

Publisher: Springer International Publishing

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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.

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Metadata
Title
Parallel Algorithm for Solving Constrained Global Optimization Problems
Authors
Konstantin Barkalov
Ilya Lebedev
Copyright Year
2017
Book
Parallel Computing Technologies

Print ISBN: 978-3-319-62931-5

Electronic ISBN: 978-3-319-62932-2

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-62932-2_38

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