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International Conference on Learning and Intelligent Optimization

LION 12 2018: Learning and Intelligent Optimization pp 241–256Cite as

Adaptive Multi-objective Local Search Algorithms for the Permutation Flowshop Scheduling Problem

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11353))

Abstract

Automatic algorithm configuration (AAC) is an increasingly critical factor in the design of efficient metaheuristics. AAC was previously successfully applied to multi-objective local search (MOLS) algorithms using offline tools. However, offline approaches are usually very expensive, draw general recommendations regarding algorithm design for a given set of instances, and does generally not allow per-instance adaptation. Online techniques for automatic algorithm control are usually applied to single-objective evolutionary algorithms. In this work we investigate the impact of including control mechanisms to MOLS algorithms on a classical bi-objective permutation flowshop scheduling problem (PFSP), and demonstrate how even simple control mechanisms can complement traditional offline configuration techniques.

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Authors and Affiliations

  1. Université de Lille, CNRS, UMR 9189 - CRIStAL, Lille, France

    Aymeric Blot, Marie-Éléonore Kessaci & Laetitia Jourdan

  2. KU Leuven, CODeS and imec research groups, Kortrijk, Belgium

    Patrick De Causmaecker

Authors
  1. Aymeric Blot
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  2. Marie-Éléonore Kessaci
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  3. Laetitia Jourdan
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  4. Patrick De Causmaecker
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Corresponding author

Correspondence to Aymeric Blot .

Editor information

Editors and Affiliations

  1. University of Trento, Trento, Italy

    Roberto Battiti

  2. University of Trento, Trento, Italy

    Mauro Brunato

  3. Wilfrid Laurier University, Waterloo, ON, Canada

    Ilias Kotsireas

  4. University of Florida, Gainesville, FL, USA

    Panos M. Pardalos

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Blot, A., Kessaci, MÉ., Jourdan, L., De Causmaecker, P. (2019). Adaptive Multi-objective Local Search Algorithms for the Permutation Flowshop Scheduling Problem. In: Battiti, R., Brunato, M., Kotsireas, I., Pardalos, P. (eds) Learning and Intelligent Optimization. LION 12 2018. Lecture Notes in Computer Science(), vol 11353. Springer, Cham. https://doi.org/10.1007/978-3-030-05348-2_22

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  • DOI: https://doi.org/10.1007/978-3-030-05348-2_22

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-030-05348-2

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