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Solving Continuous Optimization Problems with a New Hyperheuristic Framework

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Machine Learning, Optimization, and Data Science (LOD 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14505))

Abstract

Continuous optimization is a central task in computer science. Hyperheuristics prove to be an effective mechanism for intelligent operator selection and generation for optimization problems. In this paper we propose a two level hyperheuristic framework for continuous optimization problems. The base level is used to optimize the problem with operator sequences that are modeled by a nested Markov chain, while the hyper level searches the operator sequence and parameter space with simulated annealing. The experimental results show that the proposed approach matches the performance of another state-of-the-art hyperheuristic using significantly less operators and computational time. The model outperforms the simple metaheuristic operator approach and the random hyperheuristic search strategy.

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Authors and Affiliations

  1. Faculty of Mathematics and Computer Science, Babeş-Bolyai University, Cluj, Romania

    Nándor Bándi & Noémi Gaskó

Authors
  1. Nándor Bándi
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  2. Noémi Gaskó
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Corresponding author

Correspondence to Nándor Bándi .

Editor information

Editors and Affiliations

  1. University of Catania, Catania, Catania, Italy

    Giuseppe Nicosia

  2. Newcastle University, Newcastle upon Tyne, UK

    Varun Ojha

  3. University of Oxford, Oxford, UK

    Emanuele La Malfa

  4. University of Cambridge, Cambridge, UK

    Gabriele La Malfa

  5. University of Florida, Gainesville, FL, USA

    Panos M. Pardalos

  6. Dana-Farber Cancer Institute, Boston, MA, USA

    Renato Umeton

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Bándi, N., Gaskó, N. (2024). Solving Continuous Optimization Problems with a New Hyperheuristic Framework. In: Nicosia, G., Ojha, V., La Malfa, E., La Malfa, G., Pardalos, P.M., Umeton, R. (eds) Machine Learning, Optimization, and Data Science. LOD 2023. Lecture Notes in Computer Science, vol 14505. Springer, Cham. https://doi.org/10.1007/978-3-031-53969-5_10

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  • DOI: https://doi.org/10.1007/978-3-031-53969-5_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-53968-8

  • Online ISBN: 978-3-031-53969-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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