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Parallelized Heuristics for Combinatorial Search

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Parallel Computing in Optimization

Part of the book series: Applied Optimization ((APOP,volume 7))

Abstract

Combinatorial optimization problems of various kinds arise in different fields. Many of these problems are large, complicated problems that require huge computing powers and long execution times for the solving procedure. Therefore the use of massively parallel computer architectures presents an interesting opportunity. The difficulty in solving these problems can sometimes also necessitate the use of heuristic methods that do not necessarily find the global optimum. In this chapter, we give brief descriptions of the techniques and discuss different parallel implementations of the modern heuristic techniques Simulated Annealing, Tabu Search, Genetic Algorithms, and GRASP.

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Authors
  1. K. Holmqvist
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  2. A. Migdalas
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  3. P. M. Pardalos
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Editor information

Editors and Affiliations

  1. Division of Optimization, Department of Mathematics, Linköping Institute of Technology, S-581 83, Linköping, Sweden

    Athanasios Migdalas

  2. Department of Industrial and Systems Engineering, University of Florida, FL32611, Gainesville, USA

    Panos M. Pardalos

  3. Department of Informatics, University of Bergen, Thormøhlensgate 55, N-5020, Bergen, Norway

    Sverre Storøy

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© 1997 Kluwer Academic Publishers

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Holmqvist, K., Migdalas, A., Pardalos, P.M. (1997). Parallelized Heuristics for Combinatorial Search. In: Migdalas, A., Pardalos, P.M., Storøy, S. (eds) Parallel Computing in Optimization. Applied Optimization, vol 7. Springer, Boston, MA. https://doi.org/10.1007/978-1-4613-3400-2_8

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  • DOI: https://doi.org/10.1007/978-1-4613-3400-2_8

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4613-3402-6

  • Online ISBN: 978-1-4613-3400-2

  • eBook Packages: Springer Book Archive

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