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A Look-Ahead Based Meta-heuristics for Optimizing Continuous Optimization Problems

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Optimization, Learning Algorithms and Applications (OL2A 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1488))

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Abstract

In this paper, the famous kernighan-Lin algorithm is adjusted and embedded into the simulated annealing algorithm and the genetic algorithm for continuous optimization problems. The performance of the different algorithms are evaluated using a set of well known optimization test functions.

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Author information

Authors and Affiliations

  1. University of South-Eastern Norway, Kongsberg, Norway

    Thomas Nordli & Noureddine Bouhmala

Authors
  1. Thomas Nordli
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  2. Noureddine Bouhmala
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Corresponding author

Correspondence to Noureddine Bouhmala .

Editor information

Editors and Affiliations

  1. Instituto Politécnico de Bragança, Bragança, Portugal

    Ana I. Pereira

  2. Instituto Politécnico de Bragança, Bragança, Portugal

    Florbela P. Fernandes

  3. Instituto Politécnico de Bragança, Bragança, Portugal

    João P. Coelho

  4. Instituto Politécnico de Bragança, Bragança, Portugal

    João P. Teixeira

  5. Instituto Politécnico de Bragança, Bragança, Portugal

    Maria F. Pacheco

  6. Instituto Politécnico de Bragança, Bragança, Portugal

    Paulo Alves

  7. Instituto Politécnico de Bragança, Bragança, Portugal

    Rui P. Lopes

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Nordli, T., Bouhmala, N. (2021). A Look-Ahead Based Meta-heuristics for Optimizing Continuous Optimization Problems. In: Pereira, A.I., et al. Optimization, Learning Algorithms and Applications. OL2A 2021. Communications in Computer and Information Science, vol 1488. Springer, Cham. https://doi.org/10.1007/978-3-030-91885-9_4

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  • DOI: https://doi.org/10.1007/978-3-030-91885-9_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-91884-2

  • Online ISBN: 978-3-030-91885-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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